I am a web developer. I took a course (Data Visualization) where i need to write how this course is related to my work (web development). I have uploaded the textbook and we use R language in this course.

Length: 600 words

Data Visualisation
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Data Visualisation
A Handbook for Data Driven Design
Andy Kirk
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SAGE Publications Ltd
1 Oliver’s Yard
55 City Road
London EC1Y 1SP
SAGE Publications Inc.
2455 Teller Road
Thousand Oaks, California 91320
SAGE Publications India Pvt Ltd
B 1/I 1 Mohan Cooperative Industrial Area
Mathura Road
New Delhi 110 044
SAGE Publications Asia-Pacific Pte Ltd
3 Church Street
#10-04 Samsung Hub
Singapore 049483
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© Andy Kirk 2016
First published 2016
Apart from any fair dealing for the purposes of research or private study,
or criticism or review, as permitted under the Copyright, Designs and
Patents Act, 1988, this publication may be reproduced, stored or
transmitted in any form, or by any means, only with the prior permission
in writing of the publishers, or in the case of reprographic reproduction, in
accordance with the terms of licences issued by the Copyright Licensing
Agency. Enquiries concerning reproduction outside those terms should be
sent to the publishers.
Library of Congress Control Number: 2015957322
British Library Cataloguing in Publication data
A catalogue record for this book is available from the British Library
ISBN 978-1-4739-1213-7
ISBN 978-1-4739-1214-4 (pbk)
Editor: Mila Steele
Editorial assistant: Alysha Owen
Production editor: Ian Antcliff
Marketing manager: Sally Ransom
Cover design: Shaun Mercier
Typeset by: C&M Digitals (P) Ltd, Chennai, India
Printed and bound in Great Britain by Bell and Bain Ltd, Glasgow
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Contents
List of Figures with Source Notes
Acknowledgements
About the Author
INTRODUCTION
PART A FOUNDATIONS
1 Defining Data Visualisation
2 Visualisation Workflow
PART B THE HIDDEN THINKING
3 Formulating Your Brief
4 Working With Data
5 Establishing Your Editorial Thinking
PART C DEVELOPING YOUR DESIGN SOLUTION
6 Data Representation
7 Interactivity
8 Annotation
9 Colour
10 Composition
PART D DEVELOPING YOUR CAPABILITIES
11 Visualisation Literacy
References
Index
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List of Figures with Source Notes
1.1 A Definition for Data Visualisation 19
1.2 Per Capita Cheese Consumption in the U.S., by Sarah Slobin
(Fortune magazine) 20
1.3 The Three Stages of Understanding 22
1.4–6 Demonstrating the Process of Understanding 24–27
1.7 The Three Principles of Good Visualisation Design 30
1.8 Housing and Home Ownership in the UK, by ONS Digital
Content Team 33
1.9 Falling Number of Young Homeowners, by the Daily Mail 33
1.10 Gun Deaths in Florida (Reuters Graphics) 34
1.11 Iraq’s Bloody Toll, by Simon Scarr (South China Morning Post)
34
1.12 Gun Deaths in Florida Redesign, by Peter A. Fedewa
(@pfedewa) 35
1.13 If Vienna would be an Apartment, by NZZ (Neue Zürcher
Zeitung) [Translated] 45
1.14 Asia Loses Its Sweet Tooth for Chocolate, by Graphics
Department (Wall Street Journal) 45
2.1 The Four Stages of the Visualisation Workflow 54
3.1 The ‘Purpose Map’ 76
3.2 Mizzou’s Racial Gap Is Typical On College Campuses, by
FiveThirtyEight 77
3.3 Image taken from ‘Wealth Inequality in America’, by YouTube
user ‘Politizane’ (www.youtube.com/watch?v=QPKKQnijnsM) 78
3.4 Dimensional Changes in Wood, by Luis Carli (luiscarli.com) 79
3.5 How Y’all, Youse and You Guys Talk, by Josh Katz (The New
York Times) 80
3.6 Spotlight on Profitability, by Krisztina Szücs 81
3.7 Countries with the Most Land Neighbours 83
3.8 Buying Power: The Families Funding the 2016 Presidential
Election, by Wilson Andrews, Amanda Cox, Alicia DeSantis, Evan
Grothjan, Yuliya Parshina-Kottas, Graham Roberts, Derek Watkins
and Karen Yourish (The New York Times) 84
3.9 Image taken from ‘Texas Department of Criminal Justice’
Website
(www.tdcj.state.tx.us/death_row/dr_executed_offenders.html) 86
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3.10 OECD Better Life Index, by Moritz Stefaner, Dominikus Baur,
Raureif GmbH 89
3.11 Losing Ground, by Bob Marshall, The Lens, Brian Jacobs and
Al Shaw (ProPublica) 89
3.12 Grape Expectations, by S. Scarr, C. Chan, and F. Foo (Reuters
Graphics) 91
3.13 Keywords and Colour Swatch Ideas from Project about
Psychotherapy Treatment in the Arctic 92
3.14 An Example of a Concept Sketch, by Giorgia Lupi of Accurat 92
4.1 Example of a Normalised Dataset 99
4.2 Example of a Cross-tabulated Dataset 100
4.3 Graphic Language: The Curse of the CEO, by David Ingold and
Keith Collins (Bloomberg Visual Data), Jeff Green (Bloomberg
News) 101
4.4 US Presidents by Ethnicity (1789 to 2015) 114
4.5 OECD Better Life Index, by Moritz Stefaner, Dominikus Baur,
Raureif GmbH 116
4.6 Spotlight on Profitability, by Krisztina Szücs 117
4.7 Example of ‘Transforming to Convert’ Data 119
4.8 Making Sense of the Known Knowns 123
4.9 What Good Marathons and Bad Investments Have in Common,
by Justin Wolfers (The New York Times) 124
5.1 The Fall and Rise of U.S. Inequality, in Two Graphs Source:
World Top Incomes Database; Design credit: Quoctrung Bui (NPR)
136
5.2–4 Why Peyton Manning’s Record Will Be Hard to Beat, by
Gregor Aisch and Kevin Quealy (The New York Times) 138–140
C.1 Mockup Designs for ‘Poppy Field’, by Valentina D’Efilippo
(design); Nicolas Pigelet (code); Data source: The Polynational War
Memorial, 2014 (poppyfield.org) 146
6.1 Mapping Records and Variables on to Marks and Attributes 152
6.2 List of Mark Encodings 153
6.3 List of Attribute Encodings 153
6.4 Bloomberg Billionaires, by Bloomberg Visual Data (Design and
development), Lina Chen and Anita Rundles (Illustration) 155
6.5 Lionel Messi: Games and Goals for FC Barcelona 156
6.6 Image from the Home page of visualisingdata.com 156
6.7 How the Insane Amount of Rain in Texas Could Turn Rhode
Island Into a Lake, by Christopher Ingraham (The Washington Post)
156
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6.8 The 10 Actors with the Most Oscar Nominations but No Wins
161
6.9 The 10 Actors who have Received the Most Oscar Nominations
162
6.10 How Nations Fare in PhDs by Sex Interactive, by Periscopic;
Research by Amanda Hobbs; Published in Scientific American 163
6.11 Gender Pay Gap US, by David McCandless, Miriam Quick
(Research) and Philippa Thomas (Design) 164
6.12 Who Wins the Stanley Cup of Playoff Beards? by Graphics
Department (Wall Street Journal) 165
6.13 For These 55 Marijuana Companies, Every Day is 4/20, by Alex
Tribou and Adam Pearce (Bloomberg Visual Data) 166
6.14 UK Public Sector Capital Expenditure, 2014/15 167
6.15 Global Competitiveness Report 2014–2015, by Bocoup and the
World Economic Forum 168
6.16 Excerpt from a Rugby Union Player Dashboard 169
6.17 Range of Temperatures (°F) Recorded in the Top 10 Most
Populated Cities During 2015 170
6.18 This Chart Shows How Much More Ivy League Grads Make
Than You, by Christopher Ingraham (The Washington Post) 171
6.19 Comparing Critics Scores (Rotten Tomatoes) for Major Movie
Franchises 172
6.20 A Career in Numbers: Movies Starring Michael Caine 173
6.21 Comparing the Frequency of Words Used in Chapter 1 of this
Book 174
6.22 Summary of Eligible Votes in the UK General Election 2015
175
6.23 The Changing Fortunes of Internet Explorer and Google Chrome
176
6.24 Literarcy Proficiency: Adult Levels by Country 177
6.25 Political Polarization in the American Public’, Pew Research
Center, Washington, DC (February, 2015) (http://www.people-
press.org/2014/06/12/political-polarization-in-the-american-public/)
178
6.26 Finviz (www.finviz.com) 179
6.27 This Venn Diagram Shows Where You Can Both Smoke Weed
and Get a Same-Sex Marriage, by Phillip Bump (The Washington
Post) 180
6.28 The 200+ Beer Brands of SAB InBev, by Maarten Lambrechts
for Mediafin: www.tijd.be/sabinbev (Dutch),
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www.lecho.be/service/sabinbev (French) 181
6.29 Which Fossil Fuel Companies are Most Responsible for Climate
Change? by Duncan Clark and Robin Houston (Kiln), published in
the Guardian, drawing on work by Mike Bostock and Jason Davies
182
6.30 How Long Will We Live – And How Well? by Bonnie
Berkowitz, Emily Chow and Todd Lindeman (The Washington Post)
183
6.31 Crime Rates by State, by Nathan Yau 184
6.32 Nutrient Contents – Parallel Coordinates, by Kai Chang
(@syntagmatic) 185
6.33 How the ‘Avengers’ Line-up Has Changed Over the Years, by
Jon Keegan (Wall Street Journal) 186
6.34 Interactive Fixture Molecules, by @experimental361 and
@bootifulgame 187
6.35 The Rise of Partisanship and Super-cooperators in the U.S.
House of Representatives. Visualisation by Mauro Martino, authored
by Clio Andris, David Lee, Marcus J. Hamilton, Mauro Martino,
Christian E. Gunning, and John Armistead Selde 188
6.36 The Global Flow of People, by Nikola Sander, Guy J. Abel and
Ramon Bauer 189
6.37 UK Election Results by Political Party, 2010 vs 2015 190
6.38 The Fall and Rise of U.S. Inequality, in Two Graphs. Source:
World Top Incomes Database; Design credit: Quoctrung Bui (NPR)
191
6.39 Census Bump: Rank of the Most Populous Cities at Each
Census, 1790–1890, by Jim Vallandingham 192
6.40 Coal, Gas, Nuclear, Hydro? How Your State Generates Power.
Source: U.S. Energy Information Administration, Credit: Christopher
Groskopf, Alyson Hurt and Avie Schneider (NPR) 193
6.41 Holdouts Find Cheapest Super Bowl Tickets Late in the Game,
by Alex Tribou, David Ingold and Jeremy Diamond (Bloomberg
Visual Data) 194
6.42 Crude Oil Prices (West Texas Intermediate), 1985–2015 195
6.43 Percentage Change in Price for Select Food Items, Since 1990,
by Nathan Yau 196
6.44 The Ebb and Flow of Movies: Box Office Receipts 1986–2008,
by Mathew Bloch, Lee Byron, Shan Carter and Amanda Cox (The
New York Times) 197
6.45 Tracing the History of N.C.A.A. Conferences, by Mike Bostock,
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Shan Carter and Kevin Quealy (The New York Times) 198
6.46 A Presidential Gantt Chart, by Ben Jones 199
6.47 How the ‘Avengers’ Line-up Has Changed Over the Years, by
Jon Keegan (Wall Street Journal) 200
6.48 Native and New Berliners – How the S-Bahn Ring Divides the
City, by Julius Tröger, André Pätzold, David Wendler (Berliner
Morgenpost) and Moritz Klack (webkid.io) 201
6.49 How Y’all, Youse and You Guys Talk, by Josh Katz (The New
York Times) 202
6.50 Here’s Exactly Where the Candidates Cash Came From, by Zach
Mider, Christopher Cannon, and Adam Pearce (Bloomberg Visual
Data) 203
6.51 Trillions of Trees, by Jan Willem Tulp 204
6.52 The Racial Dot Map. Image Copyright, 2013, Weldon Cooper
Center for Public Service, Rector and Visitors of the University of
Virginia (Dustin A. Cable, creator) 205
6.53 Arteries of the City, by Simon Scarr (South China Morning
Post) 206
6.54 The Carbon Map, by Duncan Clark and Robin Houston (Kiln)
207
6.55 Election Dashboard, by Jay Boice, Aaron Bycoffe and Andrei
Scheinkman (Huffington Post). Statistical model created by Simon
Jackman 208
6.56 London is Rubbish at Recycling and Many Boroughs are Getting
Worse, by URBS London using London Squared Map © 2015
www.aftertheflood.co 209
6.57 Automating the Design of Graphical Presentations of Relational
Information. Adapted from McKinlay, J. D. (1986). ACM
Transactions on Graphics, 5(2), 110–141. 213
6.58 Comparison of Judging Line Size vs Area Size 213
6.59 Comparison of Judging Related Items Using Variation in Colour
(Hue) vs Variation in Shape 214
6.60 Illustrating the Correct and Incorrect Circle Size Encoding 216
6.61 Illustrating the Distortions Created by 3D Decoration 217
6.62 Example of a Bullet Chart using Banding Overlays 218
6.63 Excerpt from What’s Really Warming the World? by Eric
Roston and Blacki Migliozzi (Bloomberg Visual Data) 218
6.64 Example of Using Markers Overlays 219
6.65 Why Is Her Paycheck Smaller? by Hannah Fairfield and Graham
Roberts (The New York Times) 219
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6.66 Inside the Powerful Lobby Fighting for Your Right to Eat Pizza,
by Andrew Martin and Bloomberg Visual Data 220
6.67 Excerpt from ‘Razor Sales Move Online, Away From Gillette’,
by Graphics Department (Wall Street Journal) 220
7.1 US Gun Deaths, by Periscopic 225
7.2 Finviz (www.finviz.com) 226
7.3 The Racial Dot Map: Image Copyright, 2013, Weldon Cooper
Center for Public Service, Rector and Visitors of the University of
Virginia (Dustin A. Cable, creator) 227
7.4 Obesity Around the World, by Jeff Clark 228
7.5 Excerpt from ‘Social Progress Index 2015’, by Social Progress
Imperative, 2015 228
7.6 NFL Players: Height & Weight Over Time, by Noah Veltman
(noahveltman.com) 229
7.7 Excerpt from ‘How Americans Die’, by Matthew C. Klein and
Bloomberg Visual Data 230
7.8 Model Projections of Maximum Air Temperatures Near the
Ocean and Land Surface on the June Solstice in 2014 and 2099:
NASA Earth Observatory maps, by Joshua Stevens 231
7.9 Excerpt from ‘A Swing of Beauty’, by Sohail Al-Jamea, Wilson
Andrews, Bonnie Berkowitz and Todd Lindeman (The Washington
Post) 231
7.10 How Well Do You Know Your Area? by ONS Digital Content
team 232
7.11 Excerpt from ‘Who Old Are You?’, by David McCandless and
Tom Evans 233
7.12 512 Paths to the White House, by Mike Bostock and Shan Carter
(The New York Times) 233
7.13 OECD Better Life Index, by Moritz Stefaner, Dominikus Baur,
Raureif GmbH 233
7.14 Nobel Laureates, by Matthew Weber (Reuters Graphics) 234
7.15 Geography of a Recession, by Graphics Department (The New
York Times) 234
7.16 How Big Will the UK Population be in 25 Years Time? by ONS
Digital Content team 234
7.17 Excerpt from ‘Workers’ Compensation Reforms by State’, by
Yue Qiu and Michael Grabell (ProPublica) 235
7.18 Excerpt from ‘ECB Bank Test Results’, by Monica Ulmanu,
Laura Noonan and Vincent Flasseur (Reuters Graphics) 236
7.19 History Through the President’s Words, by Kennedy Elliott, Ted
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Mellnik and Richard Johnson (The Washington Post) 237
7.20 Excerpt from ‘How Americans Die’, by Matthew C. Klein and
Bloomberg Visual Data 237
7.21 Twitter NYC: A Multilingual Social City, by James Cheshire,
Ed Manley, John Barratt, and Oliver O’Brien 238
7.22 Killing the Colorado: Explore the Robot River, by Abrahm
Lustgarten, Al Shaw, Jeff Larson, Amanda Zamora and Lauren
Kirchner (ProPublica) and John Grimwade 238
7.23 Losing Ground, by Bob Marshall, The Lens, Brian Jacobs and
Al Shaw (ProPublica) 239
7.24 Excerpt from ‘History Through the President’s Words’, by
Kennedy Elliott, Ted Mellnik and Richard Johnson (The Washington
Post) 240
7.25 Plow, by Derek Watkins 242
7.26 The Horse in Motion, by Eadweard Muybridge. Source: United
States Library of Congress’s Prints and Photographs division, digital
ID cph.3a45870. 243
8.1 Titles Taken from Projects Published and Credited Elsewhere in
This Book 248
8.2 Excerpt from ‘The Color of Debt: The Black Neighborhoods
Where Collection Suits Hit Hardest’, by Al Shaw, Annie Waldman
and Paul Kiel (ProPublica) 249
8.3 Excerpt from ‘Kindred Britain’ version 1.0 © 2013 Nicholas
Jenkins – designed by Scott Murray, powered by SUL-CIDR 249
8.4 Excerpt from ‘The Color of Debt: The Black Neighborhoods
Where Collection Suits Hit Hardest’, by Al Shaw, Annie Waldman
and Paul Kiel (ProPublica) 250
8.5 Excerpt from ‘Bloomberg Billionaires’, by Bloomberg Visual
Data (Design and development), Lina Chen and Anita Rundles
(Illustration) 251
8.6 Excerpt from ‘Gender Pay Gap US?’, by David McCandless,
Miriam Quick (Research) and Philippa Thomas (Design) 251
8.7 Excerpt from ‘Holdouts Find Cheapest Super Bowl Tickets Late
in the Game’, by Alex Tribou, David Ingold and Jeremy Diamond
(Bloomberg Visual Data) 252
8.8 Excerpt from ‘The Life Cycle of Ideas’, by Accurat 252
8.9 Mizzou’s Racial Gap Is Typical On College Campuses, by
FiveThirtyEight 253
8.10 Excerpt from ‘The Infographic History of the World’, Harper
Collins (2013); by Valentina D’Efilippo (co-author and designer);
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James Ball (co-author and writer); Data source: The Polynational War
Memorial, 2012 254
8.11 Twitter NYC: A Multilingual Social City, by James Cheshire,
Ed Manley, John Barratt, and Oliver O’Brien 255
8.12 Excerpt from ‘US Gun Deaths’, by Periscopic 255
8.13 Image taken from Wealth Inequality in America, by YouTube
user ‘Politizane’ (www.youtube.com/watch?v=QPKKQnijnsM) 256
9.1 HSL Colour Cylinder: Image from Wikimedia Commons
published under the Creative Commons Attribution-Share Alike 3.0
Unported license 265
9.2 Colour Hue Spectrum 265
9.3 Colour Saturation Spectrum 266
9.4 Colour Lightness Spectrum 266
9.5 Excerpt from ‘Executive Pay by the Numbers’, by Karl Russell
(The New York Times) 267
9.6 How Nations Fare in PhDs by Sex Interactive, by Periscopic;
Research by Amanda Hobbs; Published in Scientific American 268
9.7 How Long Will We Live – And How Well? by Bonnie
Berkowitz, Emily Chow and Todd Lindeman (The Washington Post)
268
9.8 Charting the Beatles: Song Structure, by Michael Deal 269
9.9 Photograph of MyCuppa mug, by Suck UK
(www.suck.uk.com/products/mycuppamugs/) 269
9.10 Example of a Stacked Bar Chart Based on Ordinal Data 270
9.11 Rim Fire – The Extent of Fire in the Sierra Nevada Range and
Yosemite National Park, 2013: NASA Earth Observatory images, by
Robert Simmon 270
9.12 What are the Current Electricity Prices in Switzerland
[Translated], by Interactive things for NZZ (the Neue Zürcher
Zeitung) 271
9.13 Excerpt from ‘Obama’s Health Law: Who Was Helped Most’,
by Kevin Quealy and Margot Sanger-Katz (The New York Times) 272
9.14 Daily Indego Bike Share Station Usage, by Randy Olson
(@randal_olson)
(http://www.randalolson.com/2015/09/05/visualizing-indego-bike-
share-usage-patterns-in-philadelphia-part-2/) 272
9.15 Battling Infectious Diseases in the 20th Century: The Impact of
Vaccines, by Graphics Department (Wall Street Journal) 273
9.16 Highest Max Temperatures in Australia (1st to 14th January
2013), Produced by the Australian Government Bureau of
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Meteorology 274
9.17 State of the Polar Bear, by Periscopic 275
9.18 Excerpt from Geography of a Recession by Graphics
Department (The New York Times) 275
9.19 Fewer Women Run Big Companies Than Men Named John, by
Justin Wolfers (The New York Times) 276
9.20 NYPD, Council Spar Over More Officers by Graphics
Department (Wall Street Journal) 277
9.21 Excerpt from a Football Player Dashboard 277
9.22 Elections Performance Index, The Pew Charitable Trusts © 2014
278
9.23 Art in the Age of Mechanical Reproduction: Walter Benjamin by
Stefanie Posavec 279
9.24 Casualties, by Stamen, published by CNN 279
9.25 First Fatal Accident in Spain on a High-speed Line [Translated],
by Rodrigo Silva, Antonio Alonso, Mariano Zafra, Yolanda Clemente
and Thomas Ondarra (El Pais) 280
9.26 Lunge Feeding, by Jonathan Corum (The New York Times);
whale illustration by Nicholas D. Pyenson 281
9.27 Examples of Common Background Colour Tones 281
9.28 Excerpt from NYC Street Trees by Species, by Jill Hubley 284
9.29 Demonstrating the Impact of Red-green Colour Blindness
(deuteranopia) 286
9.30 Colour-blind Friendly Alternatives to Green and Red 287
9.31 Excerpt from, ‘Pyschotherapy in The Arctic’, by Andy Kirk 289
9.32 Wind Map, by Fernanda Viégas and Martin Wattenberg 289
10.1 City of Anarchy, by Simon Scarr (South China Morning Post)
294
10.2 Wireframe Sketch, by Giorgia Lupi for ‘Nobels no degree’ by
Accurat 295
10.3 Example of the Small Multiples Technique 296
10.4 The Glass Ceiling Persists Redesign, by Francis Gagnon
(ChezVoila.com) based on original by S. Culp (Reuters Graphics)
297
10.5 Fast-food Purchasers Report More Demands on Their Time, by
Economic Research Service (USDA) 297
10.6 Stalemate, by Graphics Department (Wall Street Journal) 297
10.7 Nobels No Degrees, by Accurat 298
10.8 Kasich Could Be The GOP’s Moderate Backstop, by
FiveThirtyEight 298
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10.9 On Broadway, by Daniel Goddemeyer, Moritz Stefaner,
Dominikus Baur, and Lev Manovich 299
10.10 ER Wait Watcher: Which Emergency Room Will See You the
Fastest? by Lena Groeger, Mike Tigas and Sisi Wei (ProPublica) 300
10.11 Rain Patterns, by Jane Pong (South China Morning Post) 300
10.12 Excerpt from ‘Pyschotherapy in The Arctic’, by Andy Kirk 301
10.13 Gender Pay Gap US, by David McCandless, Miriam Quick
(Research) and Philippa Thomas (Design) 301
10.14 The Worst Board Games Ever Invented, by FiveThirtyEight
303
10.15 From Millions, Billions, Trillions: Letters from Zimbabwe,
2005−2009, a book written and published by Catherine Buckle
(2014), table design by Graham van de Ruit (pg. 193) 303
10.16 List of Chart Structures 304
10.17 Illustrating the Effect of Truncated Bar Axis Scales 305
10.18 Excerpt from ‘Doping under the Microscope’, by S. Scarr and
W. Foo (Reuters Graphics) 306
10.19 Record-high 60% of Americans Support Same-sex Marriage,
by Gallup 306
10.20 Images from Wikimedia Commons, published under the
Creative Commons Attribution-Share Alike 3.0 Unported license 308
11.1–7 The Pursuit of Faster’ by Andy Kirk and Andrew Witherley
318–324
17

Acknowledgements
This book has been made possible thanks to the unwavering support of my
incredible wife, Ellie, and the endless encouragement from my Mum and
Dad, the rest of my brilliant family and my super group of friends.
From a professional standpoint I also need to acknowledge the
fundamental role played by the hundreds of visualisation practitioners (no
matter under what title you ply your trade) who have created such a wealth
of brilliant work from which I have developed so many of my convictions
and formed the basis of so much of the content in this book. The people
and organisations who have provided me with permission to use their work
are heroes and I hope this book does their rich talent justice.
18

About the Author
Andy Kirk
is a freelance data visualisation specialist based in Yorkshire, UK. He
is a visualisation design consultant, training provider, teacher,
researcher, author, speaker and editor of the award-winning website
visualisingdata.com
After graduating from Lancaster University in 1999 with a BSc
(hons) in Operational Research, Andy held a variety of business
analysis and information management positions at organisations
including West Yorkshire Police and the University of Leeds.
He discovered data visualisation in early 2007 just at the time when
he was shaping up his proposal for a Master’s (MA) Research
Programme designed for members of staff at the University of Leeds.
On completing this programme with distinction, Andy’s passion for
the subject was unleashed. Following his graduation in December
2009, to continue the process of discovering and learning the subject
he launched visualisingdata.com, a blogging platform that would
chart the ongoing development of the data visualisation field. Over
time, as the field has continued to grow, the site too has reflected this,
becoming one of the most popular in the field. It features a wide
range of fresh content profiling the latest projects and contemporary
techniques, discourse about practical and theoretical matters,
commentary about key issues, and collections of valuable references
and resources.
In 2011 Andy became a freelance professional focusing on data
visualisation consultancy and training workshops. Some of his clients
include CERN, Arsenal FC, PepsiCo, Intel, Hershey, the WHO and
McKinsey. At the time of writing he has delivered over 160 public
and private training events across the UK, Europe, North America,
Asia, South Africa and Australia, reaching well over 3000 delegates.
In addition to training workshops Andy also has two academic
teaching positions. He joined the highly respected Maryland Institute
College of Art (MICA) as a visiting lecturer in 2013 and has been
teaching a module on the Information Visualisation Master’s
Programme since its inception. In January 2016, he began teaching a
data visualisation module as part of the MSc in Business Analytics at
the Imperial College Business School in London.
19

Between 2014 and 2015 Andy was an external consultant on a
research project called ‘Seeing Data’, funded by the Arts &
Humanities Research Council and hosted by the University of
Sheffield. This study explored the issues of data visualisation literacy
among the general public and, among many things, helped to shape
an understanding of the human factors that affect visualisation
literacy and the effectiveness of design.
20

Introduction
I.1 The Quest Begins
In his book The Seven Basic Plots, author Christopher Booker investigated
the history of telling stories. He examined the structures used in biblical
teachings and historical myths through to contemporary storytelling
devices used in movies and TV. From this study he found seven common
themes that, he argues, can be identifiable in any form of story.
One of these themes was ‘The Quest’. Booker describes this as revolving
around a main protagonist who embarks on a journey to acquire a
treasured object or reach an important destination, but faces many
obstacles and temptations along the way. It is a theme that I feel shares
many characteristics with the structure of this book and the nature of data
visualisation.
You are the central protagonist in this story in the role of the data
visualiser. The journey you are embarking on involves a route along a
design workflow where you will be faced with a wide range of different
conceptual, practical and technical challenges. The start of this journey
will be triggered by curiosity, which you will need to define in order to
accomplish your goals. From this origin you will move forward to
initiating and planning your work, defining the dimensions of your
challenge. Next, you will begin the heavy lifting of working with data,
determining what qualities it contains and how you might share these with
others. Only then will you be ready to take on the design stage. Here you
will be faced with the prospect of handling a spectrum of different design
options that will require creative and rational thinking to resolve most
effectively.
The multidisciplinary nature of this field offers a unique opportunity and
challenge. Data visualisation is not an especially difficult capability to
acquire, it is largely a game of decisions. Making better decisions will be
your goal but sometimes clear decisions will feel elusive. There will be
occasions when the best choice is not at all visible and others when there
will be many seemingly equal viable choices. Which one to go with? This
book aims to be your guide, helping you navigate efficiently through these
21

difficult stages of your journey.
You will need to learn to be flexible and adaptable, capable of shifting
your approach to suit the circumstances. This is important because there
are plenty of potential villains lying in wait looking to derail progress.
These are the forces that manifest through the imposition of restrictive
creative constraints and the pressure created by the relentless ticking clock
of timescales. Stakeholders and audiences will present complex human
factors through the diversity of their needs and personal traits. These will
need to be astutely accommodated. Data, the critical raw material of this
process, will dominate your attention. It will frustrate and even disappoint
at times, as promises of its treasures fail to materialise irrespective of the
hard work, love and attention lavished upon it.
Your own characteristics will also contribute to a certain amount of the
villainy. At times, you will find yourself wrestling with internal creative
and analytical voices pulling against each other in opposite directions.
Your excitably formed initial ideas will be embraced but will need taming.
Your inherent tastes, experiences and comforts will divert you away from
the ideal path, so you will need to maintain clarity and focus.
The central conflict you will have to deal with is the notion that there is no
perfect in data visualisation. It is a field with very few ‘always’ and
‘nevers’. Singular solutions rarely exist. The comfort offered by the rules
that instruct what is right and wrong, good and evil, has its limits. You can
find small but legitimate breaking points with many of them. While you
can rightly aspire to reach as close to perfect as possible, the attitude of
aiming for good enough will often indeed be good enough and
fundamentally necessary.
In accomplishing the quest you will be rewarded with competency in data
visualisation, developing confidence in being able to judge the most
effective analytical and design solutions in the most efficient way. It will
take time and it will need more than just reading this book. It will also
require your ongoing effort to learn, apply, reflect and develop. Each new
data visualisation opportunity poses a new, unique challenge. However, if
you keep persevering with this journey the possibility of a happy ending
will increase all the time.
I.2 Who is this Book Aimed at?
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The primary challenge one faces when writing a book about data
visualisation is to determine what to leave in and what to leave out. Data
visualisation is big. It is too big a subject even to attempt to cover it all, in
detail, in one book. There is no single book to rule them all because there
is no one book that can cover it all. Each and every one of the topics
covered by the chapters in this book could (and, in several cases, do) exist
as whole books in their own right.
The secondary challenge when writing a book about data visualisation is to
decide how to weave all the content together. Data visualisation is not
rocket science; it is not an especially complicated discipline. Lots of it, as
you will see, is rooted in common sense. It is, however, certainly a
complex subject, a semantic distinction that will be revisited later. There
are lots of things to think about and decide on, as well as many things to
do and make. Creative and analytical sensibilities blend with artistic and
scientific judgments. In one moment you might be checking the statistical
rigour of your calculations, in the next deciding which tone of orange most
elegantly contrasts with an 80% black. The complexity of data
visualisation manifests itself through how these different ingredients, and
many more, interact, influence and intersect to form the whole.
The decisions I have made in formulating this book‘s content have been
shaped by my own process of learning about, writing about and practising
data visualisation for, at the time of writing, nearly a decade. Significantly
– from the perspective of my own development – I have been fortunate to
have had extensive experience designing and delivering training
workshops and postgraduate teaching. I believe you only truly learn about
your own knowledge of a subject when you have to explain it and teach it
to others.
I have arrived at what I believe to be an effective and proven pedagogy
that successfully translates the complexities of this subject into accessible,
practical and valuable form. I feel well qualified to bridge the gap between
the large population of everyday practitioners, who might identify
themselves as beginners, and the superstar technical, creative and
academic minds that are constantly pushing forward our understanding of
the potential of data visualisation. I am not going to claim to belong to that
latter cohort, but I have certainly been the former – a beginner – and most
of my working hours are spent helping other beginners start their journey.
I know the things that I would have valued when I was starting out and I
23

know how I would have wished them to be articulated and presented for
me to develop my skills most efficiently.
There is a large and growing library of fantastic books offering many
different theoretical and practical viewpoints on the subject of data
visualisation. My aim is to bring value to this existing collection of work
by taking on a particular perspective that is perhaps under-represented in
other texts – exploring the notion and practice of a visualisation design
process. As I have alluded to in the opening, the central premise of this
book is that the path to mastering data visualisation is achieved by making
better decisions: effective choices, efficiently made. The book’s central
goal is to help develop your capability and confidence in facing these
decisions.
Just as a single book cannot cover the whole of this subject, it stands that a
single book cannot aim to address directly the needs of all people doing
data visualisation. In this section I am going to run through some of the
characteristics that shape the readers to whom this book is primarily
targeted. I will also put into context the content the book will and will not
cover, and why. This will help manage your expectations as the reader and
establish its value proposition compared with other titles.
Domain and Duties
The core audiences for whom this book has been primarily written are
undergraduate and postgraduate-level students and early career researchers
from social science subjects. This reflects a growing number of people in
higher education who are interested in and need to learn about data
visualisation.
Although aimed at social sciences, the content will also be relevant across
the spectrum of academic disciplines, from the arts and humanities right
through to the formal and natural sciences: any academic duty where there
is an emphasis on the use of quantitative and qualitative methods in studies
will require an appreciation of good data visualisation practices. Where
statistical capabilities are relevant so too is data visualisation.
Beyond academia, data visualisation is a discipline that has reached
mainstream consciousness with an increasing number of professionals and
organisations, across all industry types and sizes, recognising the
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importance of doing it well for both internal and external benefit. You
might be a market researcher, a librarian or a data analyst looking to
enhance your data capabilities. Perhaps you are a skilled graphic designer
or web developer looking to take your portfolio of work into a more data-
driven direction. Maybe you are in a managerial position and not directly
involved in the creation of visualisation work, but you need to coordinate
or commission others who will be. You require awareness of the most
efficient approaches, the range of options and the different key decision
points. You might be seeking generally to improve the sophistication of
the language you use around commissioning visualisation work and to
have a better way of expressing and evaluating work created for you.
Basically, anyone who is involved in whatever capacity with the analysis
and visual communication of data as part of their professional duties will
need to grasp the demands of data visualisation and this book will go some
way to supporting these needs.
Subject Neutrality
One of the important aspects of the book will be to emphasise that data
visualisation is a portable practice. You will see a broad array of examples
of work from different industries, covering very different topics. What will
become apparent is that visualisation techniques are largely subject-matter
neutral: a line chart that displays the ebb and flow of favourable opinion
towards a politician involves the same techniques as using a line chart to
show how a stock has changed in value over time or how peak
temperatures have changed across a season in a given location. A line
chart is a line chart, regardless of the subject matter. The context of the
viewers (such as their needs and their knowledge) and the specific
meaning that can be drawn will inevitably be unique to each setting, but
the role of visualisation itself is adaptable and portable across all subject
areas.
Data visualisation is an entirely global concern, not focused on any defined
geographic region. Although the English language dominates the written
discourse (books, websites) about this subject, the interest in it and visible
output from across the globe are increasing at a pace. There are cultural
matters that influence certain decisions throughout the design process,
especially around the choices made for colour usage, but otherwise it is a
discipline common to all.
25

Level and Prerequisites
The coverage of this book is intended to serve the needs of beginners and
those with intermediate capability. For most people, this is likely to be as
far as they might ever need to go. It will offer an accessible route for
novices to start their learning journey and, for those already familiar with
the basics, there will be content that will hopefully contribute to fine-
tuning their approaches.
For context, I believe the only distinction between beginner and
intermediate is one of breadth and depth of critical thinking rather than any
degree of difficulty. The more advanced techniques in visualisation tend to
be associated with the use of specific technologies for handling larger,
complex datasets and/or producing more bespoke and feature-rich outputs.
This book is therefore not aimed at experienced or established
visualisation practitioners. There may be some new perspectives to enrich
their thinking, some content that will confirm and other content that might
constructively challenge their convictions. Otherwise, the coverage in this
book should really echo the practices they are likely to be already
observing.
As I have already touched on, data visualisation is a genuinely
multidisciplinary field. The people who are active in this field or
profession come from all backgrounds – everyone has a different entry
point and nobody arrives with all constituent capabilities. It is therefore
quite difficult to define just what are the right type and level of pre-
existing knowledge, skills or experiences for those learning about data
visualisation. As each year passes, the savvy-ness of the type of audience
this book targets will increase, especially as the subject penetrates more
into the mainstream. What were seen as bewilderingly new techniques
several years ago are now commonplace to more people.
That said, I think the following would be a fair outline of the type and
shape of some of the most important prerequisite attributes for getting the
most out of this book:
Strong numeracy is necessary as well as a familiarity with basic
statistics.
While it is reasonable to assume limited prior knowledge of data
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visualisation, there should be a strong desire to want to learn it. The
demands of learning a craft like data visualisation take time and
effort; the capabilities will need nurturing through ongoing learning
and practice. They are not going to be achieved overnight or acquired
alone from reading this book. Any book that claims to be able
magically to inject mastery through just reading it cover to cover is
over-promising and likely to under-deliver.
The best data visualisers possess inherent curiosity. You should be
the type of person who is naturally disposed to question the world
around them or can imagine what questions others have. Your instinct
for discovering and sharing answers will be at the heart of this
activity.
There are no expectations of your having any prior familiarity with
design principles, but a desire to embrace some of the creative aspects
presented in this book will heighten the impact of your work. Unlock
your artistry!
If you are somebody with a strong creative flair you are very
fortunate. This book will guide you through when and crucially when
not to tap into this sensibility. You should be willing to increase the
rigour of your analytical decision making and be prepared to have
your creative thinking informed more fundamentally by data rather
than just instinct.
A range of technical skills covering different software applications,
tools and programming languages is not expected for this book, as I
will explain next, but you will ideally have some knowledge of basic
Excel and some experience of working with data.
I.3 Getting the Balance
Handbook vs Tutorial Book
The description of this book as being a ‘handbook’ positions it as being of
practical help and presented in accessible form. It offers direction with
comprehensive reference – more of a city guidebook for a tourist than an
instruction manual to fix a washing machine. It will help you to know what
things to think about, when to think about them, what options exist and
how best to resolve all the choices involved in any data-driven design.
Technology is the key enabler for working with data and creating
27

visualisation design outputs. Indeed, apart from a small proportion of
artisan visualisation work that is drawn by hand, the reliance on
technology to create visualisation work is an inseparable necessity. For
many there is a understandable appetite for step-by-step tutorials that help
them immediately to implement data visualisation techniques via existing
and new tools.
However, writing about data visualisation through the lens of selected
tools is a bit of a minefield, given the diversity of technical options out
there and the mixed range of skills, access and needs. I greatly admire
those people who have authored tutorial-based texts because they require
astute judgement about what is the right level, structure and scope.
The technology space around visualisation is characterised by flux. There
are the ongoing changes with the enhancement of established tools as well
as a relatively high frequency of new entrants offset by the decline of
others. Some tools are proprietary, others are open source; some are easier
to learn, others require a great deal of understanding before you can even
consider embarking on your first chart. There are many recent cases of
applications or services that have enjoyed fleeting exposure before
reaching a plateau: development and support decline, the community of
users disperses and there is a certain expiry of value. Deprecation of
syntax and functions in programming languages requires the perennial
updating of skills.
All of this perhaps paints a rather more chaotic picture than is necessarily
the case but it justifies the reasons why this book does not offer teaching in
the use of any tools. While tutorials may be invaluable to some, they may
also only be mildly interesting to others and possibly of no value to most.
Tools come and go but the craft remains. I believe that creating a practical,
rather than necessarily a technical, text that focuses on the underlying craft
of data visualisation with a tool-agnostic approach offers an effective way
to begin learning about the subject in appropriate depth. The content
should be appealing to readers irrespective of the extent of their technical
knowledge (novice to advanced technicians) and specific tool experiences
(e.g. knowledge of Excel, Tableau, Adobe Illustrator).
There is a role for all book types. Different people want different sources
of insight at different stages in their development. If you are seeking a text
that provides in-depth tutorials on a range of tools or pages of
programmatic instruction, this one will not be the best choice. However, if
28

you consult only tutorial-related books, the chances are you will likely fall
short on the fundamental critical thinking that will be needed in the longer
term to get the most out of the tools with which you develop strong skills.
To substantiate the book’s value, the digital companion resources to this
book will offer a curated, up-to-date collection of visualisation technology
resources that will guide you through the most common and valuable tools,
helping you to gain a sense of what their roles are and where these fit into
the design workflow. Additionally, there will be recommended exercises
and many further related digital materials available for exploring.
Useful vs Beautiful
Another important distinction to make is that this book is not intended to
be seen as a beauty pageant. I love flicking through those glossy ‘coffee
table’ books as much as the next person; such books offer great inspiration
and demonstrate some of the finest work in the field. This book serves a
very different purpose. I believe that, as a beginner or relative beginner on
this learning journey, the inspiration you need comes more from
understanding what is behind the thinking that makes these amazing works
succeed and others not.
My desire is to make this the most useful text available, a reference that
will spend more time on your desk than on your bookshelf. To be useful is
to be used. I want the pages to be dog-eared. I want to see scribbles and
annotated notes made across its pages and key passages underlined. I want
to see sticky labels peering out above identified pages of note. I want to
see creases where pages have been folded back or a double-page spread
that has been weighed down to keep it open. In time I even want its cover
reinforced with wallpaper or wrapping paper to ensure its contents remain
bound together. There is every intention of making this an elegantly
presented and packaged book but it should not be something that invites
you to ‘look, but don’t touch’.
Pragmatic vs Theoretical
The content of this book has been formed through many years of absorbing
knowledge from all manner of books, generations of academic papers,
thousands of web articles, hundreds of conference talks, endless online and
29

personal discussions, and lots of personal practice. What I present here is a
pragmatic translation and distillation of what I have learned down the
years.
It is not a deeply academic or theoretical book. Where theoretical context
and reference is relevant it will be signposted as I do want to ground this
book in as much evidenced-based content as possible; it is about judging
what is going to add most value. Experienced practitioners will likely have
an appetite for delving deeper into theoretical discourse and the underlying
sciences that intersect in this field but that is beyond the scope of this
particular text.
Take the science of visual perception, for example. There is no value in
attempting to emulate what has already been covered by other books in
greater depth and quality than I could achieve. Once you start peeling back
the many different layers of topics like visual and cognitive science the
boundaries of your interest and their relevance to data visualisation never
seem to arrive. You get swallowed up by the depth of these subjects. You
realise that you have found yourself learning about what the very concept
of light and sight is and at that point your brain begins to ache (well, mine
does at least), especially when all you set out to discover was if a bar chart
would be better than a pie chart.
An important reason for giving greater weight to pragmatism is because of
people: people are the makers, the stakeholders, the audiences and the
critics in data visualisation. Although there are a great deal of valuable
research-driven concepts concerning data visualisation, their practical
application can be occasionally at odds with the somewhat sanitised and
artificial context of the research methods employed. To translate them into
real-world circumstances can sometimes be easier said than done as the
influence of human factors can easily distort the significance of otherwise
robust ideas.
I want to remove the burden from you as a reader having to translate
relevant theoretical discourse into applicable practice. Critical thinking
will therefore be the watchword, equipping you with the independence of
thought to decide rationally for yourself what the solutions are that best fit
your context, your data, your message and your audience. To do this you
will need an appreciation of all the options available to you (the different
things you could do) and a reliable approach for critically determining
what choices you should make (the things you will do and why).
30

Contemporary vs Historical
This book is not going to look too far back into the past. We all respect the
ancestors of this field, the great names who, despite primitive means,
pioneered new concepts in the visual display of statistics to shape the
foundations of the field being practised today. The field’s lineage is
decorated by the influence of William Playfair’s first ever bar chart,
Charles Joseph Minard’s famous graphic about Napoleon’s Russian
campaign, Florence Nightingale’s Coxcomb plot and John Snow’s cholera
map. These are some of the totemic names and classic examples that will
always be held up as the ‘firsts’. Of course, to many beginners in the field,
this historical context is of huge interest. However, again, this kind of
content has already been superbly covered by other texts on more than
enough occasions. Time to move on.
I am not going to spend time attempting to enlighten you about how we
live in the age of ‘Big Data’ and how occupations related to data are or
will be the ‘sexiest jobs’ of our time. The former is no longer news, the
latter claim emerged from a single source. I do not want to bloat this book
with the unnecessary reprising of topics that have been covered at length
elsewhere. There is more valuable and useful content I want you to focus
your time on.
The subject matter, the ideas and the practices presented here will
hopefully not date a great deal. Of course, many of the graphic examples
included in the book will be surpassed by newer work demonstrating
similar concepts as the field continues to develop. However, their worth as
exhibits of a particular perspective covered in the text should prove
timeless. As more research is conducted in the subject, without question
there will be new techniques, new concepts, new empirically evidenced
principles that emerge. Maybe even new rules. There will be new thought-
leaders, new sources of reference, new visualisers to draw insight from.
New tools will be created, existing tools will expire. Some things that are
done and can only be done by hand as of today may become seamlessly
automated in the near future. That is simply the nature of a fast-growing
field. This book can only be a line in the sand.
Analysis vs Communication
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A further important distinction to make concerns the subtle but significant
difference between visualisations which are used for analysis and
visualisations used for communication.
Before a visualiser can confidently decide what to communicate to others,
he or she needs to have developed an intimate understanding of the
qualities and potential of the data. This is largely achieved through
exploratory data analysis. Here, the visualiser and the viewer are the same
person. Through visual exploration, different interrogations can be pursued
‘on the fly’ to unearth confirmatory or enlightening discoveries about what
insights exist.
Visualisation techniques used for analysis will be a key component of the
journey towards creating visualisation for communication but the practices
involved differ. Unlike visualisation for communication, the techniques
used for visual analysis do not have to be visually polished or necessarily
appealing. They are only serving the purpose of helping you to truly learn
about your data. When a data visualisation is being created to
communicate to others, many careful considerations come into play about
the requirements and interests of the intended or expected audience. This
has a significant influence on many of the design decisions you make that
do not exist alone with visual analysis.
Exploratory data analysis is a huge and specialist subject in and of itself. In
its most advanced form, working efficiently and effectively with large
complex data, topics like ‘machine learning’, using self-learning
algorithms to help automate and assist in the discovery of patterns in data,
become increasingly relevant. For the scope of this book the content is
weighted more towards methods and concerns about communicating data
visually to others. If your role is in pure data science or statistical analysis
you will likely require a deeper treatment of the exploratory data analysis
topic than this book can reasonably offer. However, Chapter 4 will cover
the essential elements in sufficient depth for the practical needs of most
people working with data.
Print vs Digital
The opportunity to supplement the print version of this book with an e-
book and further digital companion resources helps to cushion the
agonising decisions about what to leave out. This text is therefore
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enhanced by access to further digital resources, some of which are newly
created, while others are curated references from the endless well of
visualisation content on the Web. Included online
(book.visualisingdata.com) will be:
a completed case-study project that demonstrates the workflow
activities covered in this book, including full write-ups and all related
digital materials;
an extensive and up-to-date catalogue of over 300 data visualisation
tools;
a curated collection of tutorials and resources to help develop your
confidence with some of the most common and valuable tools;
practical exercises designed to embed the learning from each chapter;
further reading resources to continue learning about the subjects
covered in each chapter.
I.4 Objectives
Before moving on to an outline of the book’s contents, I want to share four
key objectives that I hope to accomplish for you by the final chapter.
These are themes that will run through the entire text: challenge, enlighten,
equip and inspire.
To challenge you I will be encouraging you to recognise that your current
thinking about visualisation may need to be reconsidered, both as a creator
and as a consumer. We all arrive in visualisation from different subject and
domain origins and with that comes certain baggage and prior sensibilities
that can distort our perspectives. I will not be looking to eliminate these,
rather to help you harness and align them with other traits and viewpoints.
I will ask you to relentlessly consider the diverse decisions involved in this
process. I will challenge your convictions about what you perceive to be
good or bad, effective or ineffective visualisation choices: arbitrary
choices will be eliminated from your thinking. Even if you are not
necessarily a beginner, I believe the content you read in this book will
make you question some of your own perspectives and assumptions. I will
encourage you to reflect on your previous work, asking you to consider
how and why you have designed visualisations in the way that you have:
where do you need to improve? What can you do better?
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It is not just about creating visualisations, I will also challenge your
approach to reading visualisations. This is not something you might
usually think much about, but there is an important role for more tactical
approaches to consuming visualisations with greater efficiency and
effectiveness.
To enlighten you will be to increase your awareness of the possibilities in
data visualisation. As you begin your discovery of data visualisation you
might not be aware of the whole: you do not entirely know what options
exist, how they are connected and how to make good choices. Until you
know, you don’t know – that is what the objective of enlightening is all
about.
As you will discover, there is a lot on your plate, much to work through. It
is not just about the visible end-product design decisions. Hidden beneath
the surface are many contextual circumstances to weigh up, decisions
about how best to prepare your data, choices around the multitude of
viable ways of slicing those data up into different angles of analysis. That
is all before you even reach the design stage, where you will begin to
consider the repertoire of techniques for visually portraying your data – the
charts, the interactive features, the colours and much more besides.
This book will broaden your visual vocabulary to give you more ways of
expressing your data visually. It will enhance the sophistication of your
decision making and of visual language for any of the challenges you may
face.
To equip is to ensure you have robust tactics for managing your way
through the myriad options that exist in data visualisation. The variety it
offers makes for a wonderful prospect but, equally, introduces the burden
of choice. This book aims to make the challenge of undertaking data
visualisation far less overwhelming, breaking down the overall prospect
into smaller, more manageable task chunks.
The structure of this book will offer a reliable and flexible framework for
thinking, rather than rules for learning. It will lead to better decisions.
With an emphasis on critical thinking you will move away from an over-
reliance on gut feeling and taste. To echo what I mentioned earlier, its role
as a handbook will help you know what things to think about, when to
think about them and how best to resolve all the thinking involved in any
data-driven design challenge you meet.
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To inspire is to give you more than just a book to read. It is the opening of
a door into a subject to inspire you to step further inside. It is about helping
you to want to continue to learn about it and expose yourself to as much
positive influence as possible. It should elevate your ambition and broaden
your capability.
It is a book underpinned by theory but dominated by practical and
accessible advice, including input from some of the best visualisers in the
field today. The range of print and digital resources will offer lots of
supplementary material including tutorials, further reading materials and
suggested exercises. Collectively this will hopefully make it one of the
most comprehensive, valuable and inspiring titles out there.
I.5 Chapter Contents
The book is organised into four main parts (A, B, C and D) comprising
eleven chapters and preceded by the ‘Introduction’ sections you are
reading now.
Each chapter opens with an introductory outline that previews the content
to be covered and provides a bridge between consecutive chapters. In the
closing sections of each chapter the most salient learning points will be
summarised and some important, practical tips and tactics shared. As
mentioned, online there will be collections of practical exercises and
further reading resources recommended to substantiate the learning from
the chapter.
Throughout the book you will see sidebar captions that will offer relevant
references, aphorisms, good habits and practical tips from some of the
most influential people in the field today.
Introduction
This introduction explains how I have attempted to make sense of the
complexity of the subject, outlining the nature of the audience I am trying
to reach, the key objectives, what topics the book will be covering and not
covering, and how the content has been organised.
35

Part A: Foundations
Part A establishes the foundation knowledge and sets up a key reference of
understanding that aids your thinking across the rest of the book. Chapter 1
will be the logical starting point for many of you who are new to the field
to help you understand more about the definitions and attributes of data
visualisation. Even if you are not a complete beginner, the content of the
chapter forms the terms of reference that much of the remaining content is
based on. Chapter 2 prepares you for the journey through the rest of the
book by introducing the key design workflow that you will be following.
Chapter 1: Defining Data Visualisation
Defining data visualisation: outlining the components of thinking
that make up the proposed definition for data visualisation.
The importance of conviction: presenting three guiding principles of
good visualisation design: trustworthy, accessible and elegant.
Distinctions and glossary: explaining the distinctions and overlaps
with other related disciplines and providing a glossary of terms used
in this book to establish consistency of language.
Chapter 2: Visualisation Workflow
The importance of process: describing the data visualisation design
workflow, what it involves and why a process approach is required.
The process in practice: providing some useful tips, tactics and
habits that transcend any particular stage of the process but will best
prepare you for success with this activity.
Part B: The Hidden Thinking
Part B discusses the first three preparatory stages of the data visualisation
design workflow. ‘The hidden thinking’ title refers to how these vital
activities, that have a huge influence over the eventual design solution, are
somewhat out of sight in the final output; they are hidden beneath the
surface but completely shape what is visible. These stages represent the
often neglected contextual definitions, data wrangling and editorial
challenges that are so critical to the success or otherwise of any
36

visualisation work – they require a great deal of care and attention before
you switch your attention to the design stage.
Chapter 3: Formulating Your Brief
What is a brief?: describing the value of compiling a brief to help
initiate, define and plan the requirements of your work.
Establishing your project’s context: defining the origin curiosity or
motivation, identifying all the key factors and circumstances that
surround your work, and defining the core purpose of your
visualisation.
Establishing your project’s vision: early considerations about the
type of visualisation solution needed to achieve your aims and
harnessing initial ideas about what this solution might look like.
Chapter 4: Working With Data
Data literacy: establishing a basic understanding with this critical
literacy, providing some foundation understanding about datasets and
data types and some observations about statistical literacy.
Data acquisition: outlining the different origins of and methods for
accessing your data.
Data examination: approaches for acquainting yourself with the
physical characteristics and meaning of your data.
Data transformation: optimising the condition, content and form of
your data fully to prepare it for its analytical purpose.
Data exploration: developing deeper intimacy with the potential
qualities and insights contained, and potentially hidden, within your
data.
Chapter 5: Establishing Your Editorial Thinking
What is editorial thinking?: defining the role of editorial thinking in
data visualisation.
The influence of editorial thinking: explaining how the different
dimensions of editorial thinking influence design choices.
Part C: Developing Your Design Solution
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Part C is the main part of the book and covers progression through the data
visualisation design and production stage. This is where your concerns
switch from hidden thinking to visible thinking. The individual chapters in
this part of the book cover each of the five layers of the data visualisation
anatomy. They are treated as separate affairs to aid the clarity and
organisation of your thinking, but they are entirely interrelated matters and
the chapter sequences support this. Within each chapter there is a
consistent structure beginning with an introduction to each design layer, an
overview of the many different possible design options, followed by
detailed guidance on the factors that influence your choices.
The production cycle: describing the cycle of development activities
that take place during this stage, giving a context for how to work
through the subsequent chapters in this part.
Chapter 6: Data Representation
Introducing visual encoding: an overview of the essentials of data
representation looking at the differences and relationships between
visual encoding and chart types.
Chart types: a detailed repertoire of 49 different chart types, profiled
in depth and organised by a taxonomy of chart families: categorical,
hierarchical, relational, temporal, and spatial.
Influencing factors and considerations: presenting the factors that
will influence the suitability of your data representation choices.
Chapter 7: Interactivity
The features of interactivity:
Data adjustments: a profile of the options for interactively
interrogating and manipulating data.
View adjustments: a profile of the options for interactively
configuring the presentation of data.
Influencing factors and considerations: presenting the factors that will
influence the suitability of your interactivity choices.
Chapter 8: Annotation
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The features of annotation:
Project annotation: a profile of the options for helping to provide
viewers with general explanations about your project.
Chart annotation: a profile of the annotated options for helping to
optimise viewers’ understanding your charts.
Influencing factors and considerations: presenting the factors that will
influence the suitability of your annotation choices.
Chapter 9: Colour
The features of colour:
Data legibility: a profile of the options for using colour to represent
data.
Editorial salience: a profile of the options for using colour to direct
the eye towards the most relevant features of your data.
Functional harmony: a profile of the options for using colour most
effectively across the entire visualisation design.
Influencing factors and considerations: presenting the factors that will
influence the suitability of your colour choices.
Chapter 10: Composition
The features of composition:
Project composition: a profile of the options for the overall layout and
hierarchy of your visualisation design.
Chart composition: a profile of the options for the layout and
hierarchy of the components of your charts.
Influencing factors and considerations: presenting the factors that will
influence the suitability of your composition choices.
Part D: Developing Your Capabilities
Part D wraps up the book’s content by reflecting on the range of
capabilities required to develop confidence and competence with data
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visualisation. Following completion of the design process, the
multidisciplinary nature of this subject will now be clearly established.
This final part assesses the two sides of visualisation literacy – your role as
a creator and your role as a viewer – and what you need to enhance your
skills with both.
Chapter 11: Visualisation Literacy
Viewing: Learning to see: learning about the most effective strategy
for understanding visualisations in your role as a viewer rather than a
creator.
Creating: The capabilities of the visualiser: profiling the skill sets,
mindsets and general attributes needed to master data visualisation
design as a creator.
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Part A Foundations
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1 Defining Data Visualisation
This opening chapter will introduce you to the subject of data
visualisation, defining what data visualisation is and is not. It will outline
the different ingredients that make it such an interesting recipe and
establish a foundation of understanding that will form a key reference for
all of the decision making you are faced with.
Three core principles of good visualisation design will be presented that
offer guiding ideals to help mould your convictions about distinguishing
between effective and ineffective in data visualisation.
You will also see how data visualisation sits alongside or overlaps with
other related disciplines, and some definitions about the use of language in
this book will be established to ensure consistency in meaning across all
chapters.
1.1 The Components of Understanding
To set the scene for what is about to follow, I think it is important to start
this book with a proposed definition for data visualisation (Figure 1.1).
This definition offers a critical term of reference because its components
and their meaning will touch on every element of content that follows in
this book. Furthermore, as a subject that has many different proposed
definitions, I believe it is worth clarifying my own view before going
further:
Figure 1.1 A Definition for Data Visualisation
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At first glance this might appear to be a surprisingly short definition: isn’t
there more to data visualisation than that, you might ask? Can nine words
sufficiently articulate what has already been introduced as an eminently
complex and diverse discipline?
I have arrived at this after many years of iterations attempting to improve
the elegance of my definition. In the past I have tried to force too many
words and too many clauses into one statement, making it cumbersome
and rather undermining its value. Over time, as I have developed greater
clarity in my own convictions, I have in turn managed to establish greater
clarity about what I feel is the real essence of this subject. The definition
above is, I believe, a succinct and practically useful description of what the
pursuit of visualisation is truly about. It is a definition that largely informs
the contents of this book. Each chapter will aim to enlighten you about
different aspects of the roles of and relationships between each component
expressed. Let me introduce and briefly examine each of these one by one,
explaining where and how they will be discussed in the book.
Firstly, data, our critical raw material. It might appear a formality to
mention data in the definition for, after all, we are talking about data
visualisation as opposed to, let’s say, cheese visualisation (though
visualisation of data using cheese has happened, see Figure 1.2), but it
needs to be made clear the core role that data has in the design process.
Without data there is no visualisation; indeed there is no need for one.
Data plays the fundamental role in this work, so you will need to give it
your undivided attention and respect. You will discover in Chapter 4 the
importance of developing an intimacy with your data to acquaint yourself
with its physical properties, its meaning and its potential qualities.
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Figure 1.2 Per Capita Cheese Consumption in the US
Data is names, amounts, groups, statistical values, dates, comments,
locations. Data is textual and numeric in format, typically held in datasets
in table form, with rows of records and columns of different variables.
This tabular form of data is what we will be considering as the raw form of
data. Through tables, we can look at the values contained to precisely read
them as individual data points. We can look up values quite efficiently,
scanning across many variables for the different records held. However,
we cannot easily establish the comparative size and relationship between
multiple data points. Our eyes and mind are not equipped to translate
easily the textual and numeric values into quantitative and qualitative
meaning. We can look at the data but we cannot really see it without the
context of relationships that help us compare and contrast them effectively
with other values. To derive understanding from data we need to see it
represented in a different, visual form. This is the act of data
representation.
This word representation is deliberately positioned near the front of the
definition because it is the quintessential activity of data visualisation
design. Representation concerns the choices made about the form in which
your data will be visually portrayed: in lay terms, what chart or charts you
will use to exploit the brain’s visual perception capabilities most
effectively.
When data visualisers create a visualisation they are representing the data
they wish to show visually through combinations of marks and attributes.
Marks are points, lines and areas. Attributes are the appearance properties
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of these marks, such as the size, colour and position. The recipe of these
marks and their attributes, along with other components of apparatus, such
as axes and gridlines, form the anatomy of a chart.
In Chapter 6 you will gain a deeper and more sophisticated appreciation of
the range of different charts that are in common usage today, broadening
your visual vocabulary. These charts will vary in complexity and
composition, with each capable of accommodating different types of data
and portraying different angles of analysis. You will learn about the key
ingredients that shape your data representation decisions, explaining the
factors that distinguish the effective from the ineffective choices.
Beyond representation choices, the presentation of data concerns all the
other visible design decisions that make up the overall visualisation
anatomy. This includes choices about the possible applications of
interactivity, features of annotation, colour usage and the composition of
your work. During the early stages of learning this subject it is sensible to
partition your thinking about these matters, treating them as isolated
design layers. This will aid your initial critical thinking. Chapters 7–10
will explore each of these layers in depth, profiling the options available
and the factors that influence your decisions.
However, as you gain in experience, the interrelated nature of visualisation
will become much more apparent and you will see how the overall design
anatomy is entirely connected. For instance, the selection of a chart type
intrinsically leads to decisions about the space and place it will occupy; an
interactive control may be included to reveal an annotated caption; for any
design property to be even visible to the eye it must possess a colour that is
different from that of its background.
The goal expressed in this definition states that data visualisation is about
facilitating understanding. This is very important and some extra time is
required to emphasise why it is such an influential component in our
thinking. You might think you know what understanding means, but when
you peel back the surface you realise there are many subtleties that need to
be acknowledged about this term and their impact on your data
visualisation choices. Understanding ‘understanding’ (still with me?) in
the context of data visualisation is of elementary significance.
When consuming a visualisation, the viewer will go through a process of
understanding involving three stages: perceiving, interpreting and
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comprehending (Figure 1.3). Each stage is dependent on the previous one
and in your role as a data visualiser you will have influence but not full
control over these. You are largely at the mercy of the viewer – what they
know and do not know, what they are interested in knowing and what
might be meaningful to them – and this introduces many variables outside
of your control: where your control diminishes the influence and reliance
on the viewer increases. Achieving an outcome of understanding is
therefore a collective responsibility between visualiser and viewer.
These are not just synonyms for the same word, rather they carry
important distinctions that need appreciating. As you will see
throughout this book, the subtleties and semantics of language in data
visualisation will be a recurring concern.
Figure 1.3 The Three Stages of Understanding
Let’s look at the characteristics of the different stages that form the process
of understanding to help explain their respective differences and mutual
dependencies.
Firstly, perceiving. This concerns the act of simply being able to read a
chart. What is the chart showing you? How easily can you get a sense of
the values of the data being portrayed?
Where are the largest, middle-sized and smallest values?
What proportion of the total does that value hold?
How do these values compare in ranking terms?
To which other values does this have a connected relationship?
The notion of understanding here concerns our attempts as viewers to
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efficiently decode the representations of the data (the shapes, the sizes and
the colours) as displayed through a chart, and then convert them into
perceived values: estimates of quantities and their relationships to other
values.
Interpreting is the next stage of understanding following on from
perceiving. Having read the charts the viewer now seeks to convert these
perceived values into some form of meaning:
Is it good to be big or better to be small?
What does it mean to go up or go down?
Is that relationship meaningful or insignificant?
Is the decline of that category especially surprising?
The viewer’s ability to form such interpretations is influenced by their pre-
existing knowledge about the portrayed subject and their capacity to utilise
that knowledge to frame the implications of what has been read. Where a
viewer does not possess that knowledge it may be that the visualiser has to
address this deficit. They will need to make suitable design choices that
help to make clear what meaning can or should be drawn from the display
of data. Captions, headlines, colours and other annotated devices, in
particular, can all be used to achieve this.
Comprehending involves reasoning the consequence of the perceiving and
interpreting stages to arrive at a personal reflection of what all this means
to them, the viewer. How does this information make a difference to what
was known about the subject previously?
Why is this relevant? What wants or needs does it serve?
Has it confirmed what I knew or possibly suspected beforehand or
enlightened me with new knowledge?
Has this experience impacted me in an emotional way or left me
feeling somewhat indifferent as a consequence?
Does the context of what understanding I have acquired lead me to
take action – such as make a decision or fundamentally change my
behaviour – or do I simply have an extra grain of knowledge the
consequence of which may not materialise until much later?
Over the page is a simple demonstration to further illustrate this process of
understanding. In this example I play the role of a viewer working with a
sample isolated chart (Figure 1.4). As you will learn throughout the design
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chapters, a chart would not normally just exist floating in isolation like this
one does, but it will serve a purpose for this demonstration.
Figure 1.4 shows a clustered bar chart that presents a breakdown of the
career statistics for the footballer Lionel Messi during his career with FC
Barcelona.
The process commences with perceiving the chart. I begin by establishing
what chart type is being used. I am familiar with this clustered bar chart
approach and so I quickly feel at ease with the prospect of reading its
display: there is no learning for me to have to go through on this occasion,
which is not always the case as we will see.
I can quickly assimilate what the axes are showing by examining the labels
along the x- and y-axes and by taking the assistance provided by colour
legend at the top. I move on to scanning, detecting and observing the
general physical properties of the data being represented. The eyes and
brain are working in harmony, conducting this activity quite instinctively
without awareness or delay, noting the most prominent features of
variation in the attributes of size, shape, colour and position.
Figure 1.4 Demonstrating the Process of Understanding
I look across the entire chart, identifying the big, small and medium values
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(these are known as stepped magnitude judgements), and form an overall
sense of the general value rankings (global comparison judgements). I am
instinctively drawn to the dominant bars towards the middle/right of the
chart, especially as I know this side of the chart concerns the most recent
career performances. I can determine that the purple bar – showing goals –
has been rising pretty much year-on-year towards a peak in 2011/12 and
then there is a dip before recovery in his most recent season.
My visual system is now working hard to decode these properties into
estimations of quantities (amounts of things) and relationships (how
different things compare with each other). I focus on judging the absolute
magnitudes of individual bars (one bar at a time). The assistance offered
by the chart apparatus, such as the vertical axis (or y- axis) values and the
inclusion of gridlines, is helping me more quickly estimate the quantities
with greater assurance of accuracy, such as discovering that the highest
number of goals scored was around 73.
I then look to conduct some relative higher/lower comparisons. In
comparing the games and goals pairings I can see that three out of the last
four years have seen the purple bar higher than the blue bar, in contrast to
all the rest. Finally I look to establish proportional relationships between
neighbouring bars, i.e. by how much larger one is compared with the next.
In 2006/07 I can see the blue bar is more than twice as tall as the purple
one, whereas in 2011/12 the purple bar is about 15% taller.
By reading this chart I now have a good appreciation of the quantities
displayed and some sense of the relationship between the two measures,
games and goals.
The second part of the understanding process is interpreting. In reality, it
is not so consciously consecutive or delayed in relationship to the
perceiving stage but you cannot get here without having already done the
perceiving. Interpreting, as you will recall, is about converting perceived
‘reading’ into meaning. Interpreting is essentially about orientating your
assessment of what you’ve read against what you know about the subject.
As I mentioned earlier, often a data visualiser will choose to – or have the
opportunity to – share such insights via captions, chart overlays or
summary headlines. As you will learn in Chapter 3, the visualisations that
present this type of interpretation assistance are commonly described as
offering an ‘explanatory’ experience. In this particular demonstration it is
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an example of an ‘exhibitory’ experience, characterised by the absence of
any explanatory features. It relies on the viewer to handle the demands of
interpretation without any assistance.
As you will read about later, many factors influence how well different
viewers will be able to interpret a visualisation. Some of the most critical
include the level of interest shown towards the subject matter, its relevance
and the general inclination, in that moment, of a viewer to want to read
about that subject through a visualisation. It is also influenced by the
knowledge held about a subject or the capacity to derive meaning from a
subject even if a knowledge gap exists.
Returning to the sample chart, in order to translate the quantities and
relationships I extracted from the perceiving stage into meaning, I am
effectively converting the reading of value sizes into notions of good or
bad and comparative relationships into worse than or better than etc. To
interpret the meaning of this data about Lionel Messi I can tap into my
passion for and knowledge of football. I know that for a player to score
over 25 goals in a season is very good. To score over 35 is exceptional. To
score over 70 goals is frankly preposterous, especially at the highest level
of the game (you might find plenty of players achieving these statistics
playing for the Dog and Duck pub team, but these numbers have been
achieved for Barcelona in La Liga, the Champions League and other
domestic cup competitions). I know from watching the sport, and poring
over statistics like this for 30 years, that it is very rare for a player to score
remotely close to a ratio of one goal per game played. Those purple bars
that exceed the height of the blue bars are therefore remarkable. Beyond
the information presented in the chart I bring knowledge about the periods
when different managers were in charge of Barcelona, how they played the
game, and how some organised their teams entirely around Messi’s talents.
I know which other players were teammates across different seasons and
who might have assisted or hindered his achievements. I also know his age
and can mentally compare his achievements with the traditional football
career arcs that will normally show a steady rise, peak, plateau, and then
decline.
Therefore, in this example, I am not just interested in the subject but can
bring a lot of knowledge to aid me in interpreting this analysis. That helps
me understand a lot more about what this data means. For other people
they might be passingly interested in football and know how to read what
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is being presented, but they might not possess the domain knowledge to go
deeper into the interpretation. They also just might not care. Now imagine
this was analysis of, let’s say, an NHL ice hockey player (Figure 1.5) –
that would present an entirely different challenge for me.
In this chart the numbers are irrelevant, just using the same chart as before
with different labels. Assuming this was real analysis, as a sports fan in
general I would have the capacity to understand the notion of a
sportsperson’s career statistics in terms of games played and goals scored:
I can read the chart (perceiving) that shows me this data and catch the gist
of the angle of analysis it is portraying. However, I do not have sufficient
domain knowledge of ice hockey to determine the real meaning and
significance of the big–small, higher–lower value relationships. I cannot
confidently convert ‘small’ into ‘unusual’ or ‘greater than’ into
‘remarkable’. My capacity to interpret is therefore limited, and besides I
have no connection to the subject matter, so I am insufficiently interested
to put in the effort to spend much time with any in-depth attempts at
interpretation.
Figure 1.5 Demonstrating the Process of Understanding
Imagine this is now no longer analysis about sport but about the sightings
in the wild of Winglets and Spungles (completely made up words). Once
again I can still read the chart shown in Figure 1.6 but now I have
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absolutely no connection to the subject whatsoever. No knowledge and no
interest. I have no idea what these things are, no understanding about the
sense of scale that should be expected for these sightings, I don’t know
what is good or bad. And I genuinely don’t care either. In contrast, for
those who do have a knowledge of and interest in the subject, the meaning
of this data will be much more relevant. They will be able to read the chart
and make some sense of the meaning of the quantities and relationships
displayed.
To help with perceiving, viewers need the context of scale. To help with
interpreting, viewers need the context of subject, whether that is provided
by the visualiser or the viewer themself. The challenge for you and I as
data visualisers is to determine what our audience will know already and
what they will need to know in order to possibly assist them in interpreting
the meaning. The use of explanatory captions, perhaps positioned in that
big white space top left, could assist those lacking the knowledge of the
subject, possibly offering a short narrative to make the interpretations – the
meaning – clearer and immediately accessible.
We are not quite finished, there is one stage left. The third part of the
understanding process is comprehending. This is where I attempt to form
some concluding reasoning that translates into what this analysis means for
me. What can I infer from the display of data I have read? How do I relate
and respond to the insights I have drawn out as through interpretation?
Does what I’ve learnt make a difference to me? Do I know something
more than I did before? Do I need to act or decide on anything? How does
it make me feel emotionally?
Figure 1.6 Demonstrating the Process of Understanding
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Through consuming the Messi chart, I have been able to form an even
greater appreciation of his amazing career. It has surprised me just how
prolific he has been, especially having seen his ratio of goals to games, and
I am particularly intrigued to see whether the dip in 2013/14 was a
temporary blip or whether the bounce back in 2014/15 was the blip. And
as he reaches his late 20s, will injuries start to creep in as they seem to do
for many other similarly prodigious young talents, especially as he has
been playing relentlessly at the highest level since his late teens?
My comprehension is not a dramatic discovery. There is no sudden
inclination to act nor any need – based on what I have learnt. I just feel a
heightened impression, formed through the data, about just how good and
prolific Lionel Messi has been. For Barcelona fanatics who watch him play
every week, they will likely have already formed this understanding. This
kind of experience would only have reaffirmed what they already probably
knew.
And that is important to recognise when it comes to managing
expectations about what we hope to achieve amongst our viewers in terms
of their final comprehending. One person’s ‘I knew that already’ is another
person’s ‘wow’. For every ‘wow, I need to make some changes’ type of
reflection there might be another ‘doesn’t affect me’. A compelling
visualisation about climate change presented to Sylvie might affect her
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significantly about the changes she might need to make in her lifestyle
choices that might reduce her carbon footprint. For Robert, who is already
familiar with the significance of this situation, it might have substantially
less immediate impact – not indifference to the meaning of the data, just
nothing new, a shrug of the shoulders. For James, the hardened sceptic,
even the most indisputable evidence may have no effect; he might just not
be receptive to altering his views regardless.
What these scenarios try to explain is that, from your perspective of the
visualiser, this final stage of understanding is something you will have
relatively little control over because viewers are people and people are
complex. People are different and as such they introduce inconsistencies.
You can lead a horse to water but you cannot make it drink: you cannot
force a viewer to be interested in your work, to understand the meaning of
a subject or get that person to react exactly how you would wish.
Visualising data is just an agent of communication and not a guarantor for
what a viewer does with the opportunity for understanding that is
presented. There are different flavours of comprehension, different
consequences of understanding formed through this final stage. Many
visualisations will be created with the ambition to simply inform, like the
Messi graphic achieved for me, perhaps to add just an extra grain to the
pile of knowledge a viewer has about a subject. Not every visualisation
results in a Hollywood moment of grand discoveries, surprising insights or
life-saving decisions. But that is OK, so long as the outcome fits with the
intended purpose, something we will discuss in more depth in Chapter 3.
Furthermore, there is the complexity of human behaviour in how people
make decisions in life. You might create the most compelling
visualisation, demonstrating proven effective design choices, carefully
constructed with very a specific audience type and need in mind. This
might clearly show how a certain decision really needs to be taken by
those in the audience. However, you cannot guarantee that the decision
maker in question, while possibly recognising that there is a need to act,
will be in a position to act, and indeed will know how to act.
It is at this point that one must recognise the ambitions and – more
importantly – realise the limits of what data visualisation can achieve.
Going back again, finally, to the components of the definition, all the
reasons outlined above show why the term to facilitate is the most a
visualiser can reasonably aspire to achieve.
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It might feel like a rather tepid and unambitious aim, something of a cop-
out that avoids scrutiny over the outcomes of our work: why not aim to
‘deliver’, ‘accomplish’, or do something more earnest than just ‘facilitate’?
I deliberately use ‘facilitate’ because as we have seen we can only control
so much. Design cannot change the world, it can only make it run a little
smoother. Visualisers can control the output but not the outcome: at best
we can expect to have only some influence on it.
1.2 The Importance of Conviction
The key structure running through this book is a data visualisation design
process. By following this process you will be able to decrease the size of
the challenge involved in making good decisions about your design
solution. The sequencing of the stages presented will help reduce the
myriad options you have to consider, which makes the prospect of arriving
at the best possible solution much more likely to occur.
Often, the design choices you need to make will be clear cut. As you will
learn, the preparatory nature of the first three stages goes a long way to
securing that clarity later in the design stage. On other occasions, plain old
common sense is a more than sufficient guide. However, for more nuanced
situations, where there are several potentially viable options presenting
themselves, you need to rely on the guiding value of good design
principles.
‘I say begin by learning about data visualisation’s “black and whites”,
the rules, then start looking for the greys. It really then becomes quite a
personal journey of developing your conviction.’ Jorge Camoes, Data
Visualization Consultant
For many people setting out on their journey in data visualisation, the
major influences that shape their early beliefs about data visualisation
design tend to be influenced by the first authors they come across. Names
like Edward Tufte, unquestionably one of the most important figures in
this field whose ideas are still pervasive, represent a common entry point
into the field, as do people like Stephen Few, David McCandless, Alberto
Cairo, and Tamara Munzner, to name but a few. These are authors of
prominent works that typically represent the first books purchased and
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read by many beginners.
Where you go from there – from whom you draw your most valuable
enduring guidance –will be shaped by many different factors: taste, the
industry you are working in, the topics on which you work, the types of
audiences you produce for. I still value much of what Tufte extols, for
example, but find I can now more confidently filter out some of his ideals
that veer towards impractical ideology or that do not necessarily hold up
against contemporary technology and the maturing expectations of people.
‘My key guiding principle? Know the rules, before you break them.’
Gregor Aisch, Graphics Editor, The New York Times
The key guidance that now most helpfully shapes and supports my
convictions comes from ideas outside the boundaries of visualisation
design in the shape of the work of Dieter Rams. Rams was a German
industrial and product designer who was most famously associated with
the Braun company.
In the late 1970s or early 1980s, Rams was becoming concerned about the
state and direction of design thinking and, given his prominent role in the
industry, felt a responsibility to challenge himself, his own work and his
own thinking against a simple question: ‘Is my design good design?’. By
dissecting his response to this question he conceived 10 principles that
expressed the most important characteristics of what he considered to be
good design. They read as follows:
1. Good design is innovative.
2. Good design makes a product useful.
3. Good design is aesthetic.
4. Good design makes a product understandable.
5. Good design is unobtrusive.
6. Good design is honest.
7. Good design is long lasting.
8. Good design is thorough down to the last detail.
9. Good design is environmentally friendly.
10. Good design is as little design as possible.
Inspired by the essence of these principles, and considering their
applicability to data visualisation design, I have translated them into three
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high-level principles that similarly help me to answer my own question: ‘Is
my visualisation design good visualisation design?’ These principles offer
me a guiding voice when I need to resolve some of the more seemingly
intangible decisions I am faced with (Figure 1.7).
Figure 1.7 The Three Principles of Good Visualisation Design
In the book Will it Make the Boat Go Faster?, co-author Ben Hunt-Davis
provides details of the strategies employed by him and his team that led to
their achieving gold medal success in the Men’s Rowing Eight event at the
Sydney Olympics in 2000. As the title suggests, each decision taken had to
pass the ‘will it make the boat go faster?’ test. Going back to the goal of
data visualisation as defined earlier, these design principles help me judge
whether any decision I make will better aid the facilitation of
understanding: the equivalence of ‘making the boat go faster’.
I will describe in detail the thinking behind each of these principles and
explain how Rams’ principles map onto them. Before that, let me briefly
explain why there are three principles of Rams’ original ten that do not
entirely fit, in my view, as universal principles for data visualisation.
‘I’m always the fool looking at the sky who falls off the cliff. In other
words, I tend to seize on ideas because I’m excited about them without
thinking through the consequences of the amount of work they will
entail. I find tight deadlines energizing. Answering the question of
“what is the graphic trying to do?” is always helpful. At minimum the
work I create needs to speak to this. Innovation doesn’t have to be a
wholesale out-of-the box approach. Iterating on a previous idea, moving
it forward, is innovation.’ Sarah Slobin, Visual Journalist
Good design is innovative: Data visualisation does not need always
to be innovative. For the majority of occasions the solutions being
created call upon the tried and tested approaches that have been used
for generations. Visualisers are not conceiving new forms of
representation or implementing new design techniques in every
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project. Of course, there are times when innovation is required to
overcome a particular challenge; innovation generally materialises
when faced with problems that current solutions fail to overcome.
Your own desire for innovation may be aligned to personal goals
about the development of your skills or through reflecting on previous
projects and recognising a desire to rethink a solution. It is not that
data visualisation is never about innovation, just that it is not always
and only about innovation.
Good design is long lasting: The translation of this principle to the
context of data visualisation can be taken in different ways. ‘Long
lasting’ could be related to the desire to preserve the ongoing
functionality of a digital project, for example. It is quite demoralising
how many historic links you visit online only to find a project has
now expired through a lack of sustained support or is no longer
functionally supported on modern browsers.
Another way to interpret ‘long lasting’ is in the durability of the
technique. Bar charts, for example, are the old reliables of the field –
always useful, always being used, always there when you need them
(author wipes away a respectful tear). ‘Long lasting’ can also relate to
avoiding the temptation of fashion or current gimmickry and having a
timeless approach to design. Consider the recent design trend moving
away from skeuomorphism and the emergence of so-called flat
design. By the time this book is published there will likely be a new
movement. ‘Long lasting’ could apply to the subject matter. Expiry in
the relevance of certain angles of analysis or out-of-date data is
inevitable in most of our work, particularly with subjects that concern
current matters. Analysis about the loss of life during the Second
World War is timeless because nothing is now going to change the
nature or extent of the underlying data (unless new discoveries
emerge). Analysis of the highest grossing movies today will change
as soon as new big movies are released and time elapses. So, once
again, this idea of long lasting is very context specific, rather than
being a universal goal for data visualisation.
Good design is environmentally friendly: This is, of course, a noble
aim but the relevance of this principle has to be positioned again at
the contextual level, based on the specific circumstances of a given
project. If your work is to be printed, the ink and paper usage
immediately removes the notion that it is an environmentally friendly
activity. Developing a powerful interactive that is being hammered
constantly and concurrently by hundreds of thousands of users puts an
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extra burden on the hosting server, creating more demands on energy
supply. The specific judgements about issues relating to the impact of
a project on the environment realistically reside with the protagonists
and stakeholders involved.
A point of clarity is that, while I describe them as design principles, they
actually provide guidance long before you reach the design thinking at the
final stage of this workflow. Design choices encapsulate the critical
thinking undertaken throughout. Think of it like an iceberg: the design is
the visible consequences of lots of hidden preparatory thinking formed
through earlier stages.
Finally, a comment is in order about something often raised in discussions
about the principles for this subject: that is, the idea that visualisations
need to be memorable. This is, in my view, not relevant as a universal
principle. If something is memorable, wonderful, that will be a terrific by-
product of your design thinking, but in itself the goal of achieving
memorability has to be isolated, again, to a contextual level based on the
specific goals of a given task and the capacity of the viewer. A politician
or a broadcaster might need to recall information more readily in their
work than a group of executives in a strategy meeting with permanent
access to endless information at the touch of a button via their iPads.
Principle 1: Good Data Visualisation is
Trustworthy
The notion of trust is uppermost in your thoughts in this first of the three
principles of good visualisation design. This maps directly onto one of
Dieter Rams’ general principles of good design, namely that good design
is honest.
Trust vs Truth
This principle is presented first because it is about the fundamental
integrity, accuracy and legitimacy of any data visualisation you produce.
This should always exist as your primary concern above all else. There
should be no compromise here. Without securing trust the entire purpose
of doing the work is undermined.
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There is an important distinction to make between trust and truth. Truth is
an obligation. You should never create work you know to be misleading in
content, nor should you claim something presents the truth if it evidently
cannot be supported by what you are presenting. For most people, the
difference between a truth and an untruth should be beyond dispute. For
those unable or unwilling to be truthful, or who are ignorant of how to
differentiate, it is probably worth putting this book away now: my telling
you how this is a bad thing is not likely to change your perspective.
If the imperative for being truthful is clear, the potential for there being
multiple different but legitimate versions of ‘truth’ within the same data-
driven context muddies things. In data visualisation there is rarely a
singular view of the truth. The glass that is half full is also half empty.
Both views are truthful, but which to choose? Furthermore, there are many
decisions involved in your work whereby several valid options may
present themselves. In these cases you are faced with choices without
necessarily having the benefit of theoretical influence to draw out the right
option. You decide what is right. This creates inevitable biases – no matter
how seemingly tiny – that ripple through your work. Your eventual
solution is potentially comprised of many well-informed, well-intended
and legitimate choices – no doubt – but they will reflect a subjective
perspective all the same. All projects represent the outcome of an entirely
unique pathway of thought.
You can mitigate the impact of these subjective choices you make, for
example, by minimising the amount of assumptions applied to the data you
are working with or by judiciously consulting your audience to best ensure
their requirements are met. However, pure objectivity is not possible in
visualisation.
‘Every number we publish is wrong but it is the best number there is.’
Andrew Dilnott, Chair of the UK Statistics Authority
Rather than view the unavoidability of these biases as an obstruction, the
focus should instead be on ensuring your chosen path is trustworthy. In the
absence of an objective truth, you need to be able to demonstrate that your
truth is trustable.
Trust has to be earned but this is hard to secure and very easy to lose. As
the translation of a Dutch proverb states, ‘trust arrives on foot and leaves
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on horseback’. Trust is something you can build by eliminating any sense
that your version of the truth can be legitimately disputed. Yet, visualisers
only have so much control and influence in the securing of trust. A
visualisation can be truthful but not viewed as trustworthy. You may have
done something with the best of intent behind your decision making, but it
may ultimately fail to secure trust among your viewers for different
reasons. Conversely a visualisation can be trustworthy in the mind of the
viewer but not truthful, appearing to merit trust yet utterly flawed in its
underlying truth. Neither of these are satisfactory: the latter scenario is a
choice we control, the former is a consequence we must strive to
overcome.
‘Good design is honest. It does not make a product appear more
innovative, powerful or valuable than it really is. It does not attempt to
manipulate the consumer with promises that cannot be kept.’ Dieter
Rams, celebrated Industrial Designer
Let’s consider a couple of examples to illustrate this notion of
trustworthiness. Firstly, think about the trust you might attach respectively
to the graphics presented in Figure 1.8 and Figure 1.9. For the benefit of
clarity both are extracted from articles discussing issues about home
ownership, so each would be accompanied with additional written analysis
at their published location. Both charts are portraying the same data and
the same analysis; they even arrive at the same summary finding. How do
the design choices make you feel about the integrity of each work?
Figure 1.8 Housing and Home Ownership in the UK (ONS)
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Both portrayals are truthful but in my view the first visualisation, produced
by the UK Office for National Statistics (ONS), commands greater
credibility and therefore far more trust than the second visualisation,
produced by the Daily Mail. The primary reason for this begins with the
colour choices. They are relatively low key in the ONS graphic: colourful
but subdued, yet conveying a certain assurance. In contrast, the Daily
Mail’s colour palette feels needy, like it is craving my attention with
sweetly coloured sticks. I don’t care for the house key imagery in the
background but it is relatively harmless. Additionally, the typeface, font
size and text colour feel more gimmicky in the second graphic. Once
again, it feels like it is wanting to shout at me in contrast to the more polite
nature of the ONS text. Whereas the Daily Mail piece refers to the ONS as
the source of the data, it fails to include further details about the data
source, which is included on the ONS graphic alongside other important
explanatory features such as the subtitle, clarity about the yearly periods
and the option to access and download the associated data. The ONS
graphic effectively ‘shows all its workings’ and overall earns, from me at
least, significantly more trust.
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Figure 1.9 Falling Number of Young Homeowners (Daily Mail)
Another example about the fragility of trust concerns the next graphic,
which plots the number of murders committed using firearms in Florida
over a period of time. This frames the time around the enactment of the
‘Stand your ground’ law in the Florida. The area chart in Figure 1.10
shows the number of murders over time and, as you can see, the chart uses
an inverted vertical y-axis with the red area going lower down as the
number of deaths increases, with peak values at about 1990 and 2007.
However, some commentators felt the inversion of the y-axis was
deceptive and declared the graphic not trustworthy based on the fact they
were perceiving the values as represented by an apparent rising ‘white
mountain’. They mistakenly observed peak values around 1999 and 2005
based on them seeing these as the highest points. This confusion is caused
by an effect known as figure-ground perception whereby a background
form (white area) can become inadvertently recognised as the foreground
form, and vice versa (with the red area seen as the background).
Figure 1.10 Gun Deaths in Florida
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Figure 1.11 Iraq’s Bloody Toll
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The key point here is that there was no intention to mislead. Although the
approach to inverting the y-axis may not be entirely conventional, it was
technically legitimate. Creatively speaking, the effect of dribbling blood
was an understandably tempting metaphor to pursue. Indeed, the graphic
attempts to emulate a notable infographic from several years ago showing
the death toll during the Iraq conflict (Figure 1.11). In the case of the
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Florida graphic, on reflection maybe the data was just too ‘smooth’ to
convey the same dribbling effect achieved in the Iraq piece. However,
being inspired and influenced by successful techniques demonstrated by
others is to be encouraged. It is one way of developing our skills.
Figure 1.12 Reworking of ‘Gun Deaths in Florida’
Unfortunately, given the emotive nature of the subject matter – gun deaths
– this analysis would always attract a passionate reaction regardless of its
form. In this case the lack of trust expressed by some was an unintended
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consequence of a single, innocent design: by reverting the y-axis to an
upward direction, as shown in the reworked version in Figure 1.12, you
can see how a single subjective design choice can have a huge influence
on people’s perception.
The creator of the Florida chart will have made hundreds of perfectly
sound visualisations and will make hundreds more, and none of them will
ever carry the intent of being anything other than truthful. However, you
can see how vulnerable perceived trust is when disputes about motives can
so quickly surface as a result of the design choice made. This is especially
the case within the pressured environment of a newsroom where you have
only a single opportunity to publish a work to a huge and widespread
audience. Contrast this setting with a graphic published within an
organisation that can be withdrawn and reissued far more easily.
Trust Applies Throughout the Process
Trustworthiness is a pursuit that should guide all your decisions, not just
the design ones. As you will see in the next chapter, the visualisation
design workflow involves a process with many decision junctions – many
paths down which you could pursue different legitimate options.
Obviously, design is the most visible result of your decision making, but
you need to create and demonstrate complete integrity in the choices made
across the entire workflow process. Here is an overview of some of the key
matters where trust must be at the forefront of your concern.
‘My main goal is to represent information accurately and in proper
context. This spans from data reporting and number crunching to
designing human-centered, intuitive and clear visualizations. This is my
sole approach, although it is always evolving.’ Kennedy Elliott,
Graphics Editor, The Washington Post
Formulating your brief: As mentioned in the discussion about the
‘Gun Crimes in Florida’ graphic, if you are working with potentially
emotive subject matter, this will heighten the importance of
demonstrating trust. Rightly or wrongly, your topic will be more
exposed to the baggage of prejudicial opinion and trust will be
precarious. As you will learn in Chapter 3, part of the thinking
involved in ‘formulating your brief’ concerns defining your audience,
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considering your subject and establishing your early thoughts about
the purpose of your work, and what you are hoping to achieve. There
will be certain contexts that lend themselves to exploiting the emotive
qualities of your subject and/or data but many others that will not.
Misjudge these contextual factors, especially the nature of your
audience’s needs, and you will jeopardise the trustworthiness of your
solution. As I have shown, matters of trust are often outside of your
immediate influence: cynicism, prejudice or suspicion held by
viewers through their beliefs or opinions is a hard thing to combat or
accommodate. In general, people feel comfortable with visualisations
that communicate data in a way that fits with their world view. That
said, at times, many are open to having their beliefs challenged by
data and evidence presented through a visualisation. The platform and
location in which your work is published (e.g. website or source
location) will also influence trust. Visualisations encountered in
already-distrusted media will create obstacles that are hard to
overcome.
Working with data: As soon as you begin working with data you
have a great responsibility to be faithful to this raw material. To be
transparent to your audience you need to consider sharing as much
relevant information about how you have handled the data that is
being presented to them:
How was it collected: from where and using what criteria?
What calculations or modifications have you applied to it?
Explain your approach.
Have you made any significant assumptions or observed any
special counting rules that may not be common?
Have you removed or excluded any data?
How representative it is? What biases may exist that could
distort interpretations?
Editorial thinking: Even with the purest of intent, your role as the
curator of your data and the creator of its portrayal introduces
subjectivity. When you choose to do one thing you are often choosing
to not do something else. The choice to focus on analysis that shows
how values have changed over time is also a decision to not show the
same data from other viewpoints such as, for example, how it looks
on a map. A decision to impose criteria on your analysis, like setting
date parameters or minimum value thresholds, in order to reduce
clutter, might be sensible and indeed legitimate, but is still a
subjective choice.
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‘Data and data sets are not objective; they are creations of human
design. Hidden biases in both the collection and analysis stages present
considerable risks [in terms of inference].’ Kate Crawford, Principal
Researcher at Microsoft Research NYC
Data representation: A fundamental tenet of data visualisation is to
never deceive the receiver. Avoiding possible misunderstandings,
inaccuracies, confusions and distortions is of primary concern. There
are many possible features of visualisation design that can lead to
varying degrees of deception, whether intended or not. Here are a few
to list now, but note that these will be picked up in more detail later:
The size of geometric areas can sometimes be miscalculated
resulting in the quantitative values being disproportionately
perceived.
When data is represented in 3D, on the majority of occasions this
represents nothing more than distracting – and distorting –
decoration. 3D should only be used when there are legitimately
three dimensions of data variables being displayed and the
viewer is able to change his or her point of view to navigate to
see different 2D perspectives.
The bar chart value axis should never be ‘truncated’ – the origin
value should always be zero – otherwise this approach will
distort the bar size judgements.
The aspect ratio (height vs width) of a line chart’s display is
influential as it affects the perceived steepness of connecting
lines which are key to reading the trends over time – too narrow
and the steepness will be embellished; too wide and the
steepness is dampened.
When portraying spatial analysis through a thematic map
representation, there are many different mapping projections to
choose from as the underlying apparatus for presenting and
orienting the geographical position of the data. There are many
different approaches to flatten the spherical globe, translating it
into a two-dimensional map form. The mathematical treatment
applied can alter significantly the perceived size or shape of
regions, potentially distorting their perception.
Sometimes charts are used in a way that is effectively corrupt,
like using pie charts for percentages that add up to more, or less,
than 100%.
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Data presentation: The main rule here is: if it looks significant, it
should be, otherwise you are either misleading or creating
unnecessary obstacles for your viewer. The undermining of trust can
also be caused by what you decline to explain: restricted or non-
functioning features of interactivity.
Absent annotations such as introduction/guides, axis titles and
labels, footnotes, data sources that fail to inform the reader of
what is going on.
Inconsistent or inappropriate colour usage, without explanation.
Confusing or inaccessible layouts.
Thoroughness in delivering trust extends to the faith you create
through reliability and consistency in the functional experience,
especially for interactive projects. Does the solution work and,
specifically, does it work in the way it promises to do?
Principle 2: Good Data Visualisation is Accessible
This second of the three principles of good visualisation design helps to
inform judgments about how best to facilitate your viewers through the
process of understanding. It is informed by three of Dieter Rams’ general
principles of good design:
2 Good design makes a product useful.
4 Good design makes a product understandable.
5 Good design is unobtrusive.
Reward vs Effort
The opening section of this chapter broke down the stages a viewer goes
through when forming their understanding about, and from, a visualisation.
This process involved a sequence of perceiving, interpreting and then
comprehending. It was emphasised that a visualiser’s control over the
viewer’s pursuit of understanding diminishes after each stage. The
objective, as stated by the presented definition, of ‘facilitating’
understanding reflects the reality of what can be controlled. You can’t
force viewers to understand, but you can smooth the way.
To facilitate understanding for an audience is about delivering
accessibility. That is the essence of this principle: to remove design-related
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obstacles faced by your viewers when undertaking this process of
understanding. Stated another way, a viewer should experience minimum
friction between the act of understanding (effort) and the achieving of
understanding (reward).
This ‘minimising’ of friction has to be framed by context, though. This is
key. There are many contextual influences that will determine whether
what is judged inaccessible in one situation could be seen as entirely
accessible in another. When people are involved, diverse needs exist. As I
have already discussed, varying degrees of knowledge emerge and
irrational characteristics come to the surface. You can only do so much: do
not expect to get all things right in the eyes of every viewer.
‘We should pay as much attention to understanding the project’s goal in
relation to its audience. This involves understanding principles of
perception and cognition in addition to other relevant factors, such as
culture and education levels, for example. More importantly, it means
carefully matching the tasks in the representation to our audience’s
needs, expectations, expertise, etc. Visualizations are human-centred
projects, in that they are not universal and will not be effective for all
humans uniformly. As producers of visualizations, whether devised for
data exploration or communication of information, we need to take into
careful consideration those on the other side of the equation, and who
will face the challenges of decoding our representations.’ Isabel
Meirelles, Professor, OCAD University (Toronto)
That is not to say that attempts to accommodate the needs of your audience
should just be abandoned, quite the opposite. This is hard but it is
essential. Visualisation is about human-centred design, demonstrating
empathy for your audiences and putting them at the heart of your decision
making.
There are several dimensions of definition that will help you better
understand your audiences, including establishing what they know, what
they do not know, the circumstances surrounding their consumption of
your work and their personal characteristics. Some of these you can
accommodate, others you may not be able to, depending on the diversity
and practicality of the requirements. Again, in the absence of perfection
optimisation is the name of the game, even if this means that sometimes
the least worst is best.
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The Factors Your Audiences Influence
Many of the factors presented here will occur when you think about your
project context, as covered in Chapter 3. For now, it is helpful to introduce
some of the factors that specifically relate to this discussion about
delivering accessible design.
Subject-matter appeal: This was already made clear in the earlier
illustration, but is worth logging again here: the appeal of the subject
matter is a fundamental junction right at the beginning of the
consumption experience. If your audiences are not interested in the
subject – i.e. they are indifferent towards the topic or see no need or
relevance to engage with it there and then – then they will not likely
stick around. They will probably not be interested in putting in the
effort to work through the process of understanding for something
that might be ultimately irrelevant. For those to whom the subject
matter is immediately appealing, they are significantly more likely to
engage with the data visualisation right the way through.
‘Data visualization is like family photos. If you don’t know the people
in the picture, the beauty of the composition won’t keep your attention.’
Zach Gemignani, CEO/Founder of Juice Analytics
Many of the ideas for this principle emerged from the Seeing Data
visualisation literacy research project (seeingdata.org) on which I
collaborated.
Dynamic of need: Do they need to engage with this work or is it
entirely voluntary? Do they have a direct investment in having access
to this information, perhaps as part of their job and they need this
information to serve their duties?
Subject-matter knowledge: What might your audiences know and
not know about this subject? What is their capacity to learn or
potential motivation to develop their knowledge of this subject? A
critical component of this issue, blending existing knowledge with the
capacity to acquire knowledge, concerns the distinctions between
complicated, complex, simple and simplified. This might seem to be
more about the semantics of language but is of significant influence
in data visualisation – indeed in any form of communication:
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Complicated is generally a technical distinction. A subject might
be difficult to understand because it involves pre-existing – and
probably high-level – knowledge and might be intricate in its
detail. The mathematics that underpinned the Moon landings are
complicated. Complicated subjects are, of course, surmountable
– the knowledge and skill are acquirable – but only achieved
through time and effort, hard work and learning (or
extraordinary talent), and, usually, with external assistance.
Complex is associated with problems that have no perfect
conclusion or maybe even no end state. Parenting is complex;
there is no rulebook for how to do it well, no definitive right or
wrong, no perfect way of accomplishing it. The elements of
parenting might not be necessarily complicated – cutting
Emmie’s sandwiches into star shapes – but there are lots of
different interrelated pressures always influencing and
occasionally colliding.
Simple, for the purpose of this book, concerns a matter that is
inherently easy to understand. It may be so small in dimension
and scope that it is not difficult to grasp, irrespective of prior
knowledge and experience.
Simplified involves transforming a problem context from either a
complex or complicated initial state to a reduced form, possibly
by eliminating certain details or nuances.
Understanding the differences in these terms is vital. When considering
your subject matter and the nature of your analysis you will need to assess
whether your audience will be immediately able to understand what you
are presenting or have the capacity to learn how to understand it. If it is a
subject that is inherently complex or complicated, will it need to be
simplified? If you are creating a graphic about taxation, will you need to
strip it down to the basics or will this process of simplification risk the
subject being oversimplified? The final content may be obscured by the
absence of important subtleties. Indeed, the audience may have felt
sufficiently sophisticated to have had the capacity to work out and work
with a complicated topic, but you denied them that opportunity. You might
reasonably dilute/reduce a complex subject for kids, but generally my
advice is don’t underestimate the capacity of your audience. Accordingly,
clarity trumps simplicity as the most salient concern about data
visualisation design.
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‘Strive for clarity, not simplicity. It’s easy to “dumb something down,”
but extremely difficult to provide clarity while maintaining complexity.
I hate the word “simplify.” In many ways, as a researcher, it is the bane
of my existence. I much prefer “explain,” “clarify,” or “synthesize.” If
you take the complexity out of a topic, you degrade its existence and
malign its importance. Words are not your enemy. Complex thoughts
are not your enemy. Confusion is. Don’t confuse your audience. Don’t
talk down to them, don’t mislead them, and certainly don’t lie to them.’
Amanda Hobbs, Researcher and Visual Content Editor
What do they need to know? The million-dollar question. Often, the
most common frustration expressed by viewers is that the
visualisation ‘didn’t show them what they were most interested in’.
They wanted to see how something changed over time, not how it
looked on a map. If you were them what would you want to know?
This is a hard thing to second-guess with any accuracy. We will be
discussing it further in Chapter 5.
Unfamiliar representation: In the final chapter of this book I will
cover the issue of visualisation literacy, discussing the capabilities
that go into being the most rounded creator of visualisation work and
the techniques involved in being the most effective consumer also.
Many people will perhaps be unaware of a deficit in their
visualisation literacy with regard to consuming certain chart types.
The bar, line and pie chart are very common and broadly familiar to
all. As you will see in Chapter 6, there are many more ways of
portraying data visually. This deficit in knowing how to read a new or
unfamiliar chart type is not a failing on the part of the viewer, it is
simply a result of their lack of prior exposure to these different
methods. For visualisers a key challenge lies with situations when the
deployment of an uncommon chart may be an entirely reasonable and
appropriate choice – indeed perhaps even the ‘simplest’ chart that
could have been used – but it is likely to be unfamiliar to the intended
viewers. Even if you support it with plenty of ‘how to read’ guidance,
if a viewer is overwhelmed or simply unwilling to make the effort to
learn how to read a different chart type, you have little control in
overcoming this.
Time: At the point of consuming a visualisation is the viewer in a
pressured situation with a lot at stake? Are viewers likely to be
impatient and intolerant of the need to spend time learning how to
read a display? Do they need quick insights or is there some capacity
for them to take on exploring or reading in more depth? If it is the
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former, the immediacy of the presented information will therefore be
a paramount requirement. If they have more time to work through the
process of perceiving, interpreting and comprehending, this could be
a more conducive situation to presenting complicated or complex
subject matter – maybe even using different, unfamiliar chart types.
Format: What format will your viewers need to consume your work?
Are they going to need work created for a print output or a digital
one? Does this need to be compatible with a small display as on a
smartphone or a tablet? If what you create is consumed away from its
intended native format, such as viewing a large infographic with
small text on a mobile phone, that will likely result in a frustrating
experience for the viewer. However, how and where your work is
consumed may be beyond your control. You can’t mitigate for every
eventuality.
Personal tastes: Individual preferences towards certain colours,
visual elements and interaction features will often influence (enabling
or inhibiting) a viewer’s engagement. The semiotic conventions that
visualisers draw upon play a part in determining whether viewers are
willing to spend time and expend effort looking at a visualisation. Be
aware though that accommodating the preferences of one person may
not cascade, with similar appeal, to all, and might indeed create a
rather negative reaction.
Attitude and emotion: Sometimes we are tired, in a bad mood,
feeling lazy, or having a day when we are just irrational. And the
prospect of working on even the most intriguing and well-designed
project sometimes feels too much. I spend my days looking at
visualisations and can sympathise with the narrowing of mental
bandwidth when I am tired or have had a bad day. Confidence is an
extension of this. Sometimes our audiences may just not feel
sufficiently equipped to embark on a visualisation if it is about an
unknown subject or might involve pushing them outside their comfort
zone in terms of the demands placed on their interpretation and
comprehension.
The Factors You Can Influence
Flipping the coin, let’s look at the main ways we, as visualisers, can
influence (positively or negatively) the accessibility of the designs created.
In effect, this entire book is focused on minimising the likelihood that your
solution demonstrates any of these negative attributes. Repeating the
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mantra from earlier, you must avoid doing anything that will cause the
boat to go slower.
‘The key difference I think in producing data visualisation/infographics
in the service of journalism versus other contexts (like art) is that there
is always an underlying, ultimate goal: to be useful. Not just beautiful or
efficient – although something can (and should!) be all of those things.
But journalism presents a certain set of constraints. A journalist has to
always ask the question: How can I make this more useful? How can
what I am creating help someone, teach someone, show someone
something new?’ Lena Groeger, Science Journalist, Designer and
Developer at ProPublica
As you saw listed at the start of this section, the selected, related design
principles from Dieter Rams’ list collectively include the aim of ensuring
our work is useful, unobtrusive and understandable. Thinking about what
not to do – focusing on the likely causes of failure across these aims – is,
in this case, more instructive.
Your Solution is Useless
You have failed to focus on relevant content.
It is not deep enough. You might have provided a summary-
level/aggregated view of the data when the audience wanted further
angles of analysis and greater depth in the details provided.
A complex subject was oversimplified.
It is not fit for the setting. You created work that required too much
time to make sense of, when immediate understanding and rapid
insights were needed.
Your Solution is Obtrusive
It is visually inaccessible. There is no appreciation of potential
impairments like colour blindness and the display includes clumsily
ineffective interactive features.
Its format is misjudged. You were supposed to create work fit for a
small-sized screen, but the solution created was too fine-detailed and
could not be easily read.
It has too many functions. You failed to focus and instead provided
too many interactive options when the audience had no desire to put
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in a lot of effort interrogating and manipulating the display.
You Solution is not Understandable
Complex subject or complex analysis. Not explained clearly enough –
assumed domain expertise, such as too many acronyms, abbreviations
and technical language.
Used a complex chart type. Not enough explanation of how to read
the graphic or failure to consider if the audience would be capable of
understanding this particular choice of chart type.
Absent annotations. Insufficient details like scales, units,
descriptions, etc.
Principle 3: Good Data Visualisation is Elegant
Elegance in design is the final principle of good visualisation design. This
relates closely to the essence of three more of Dieter Rams’ general
principles of good design:
3 Good design is aesthetic.
8 Good design is thorough down to the last detail.
10 Good design is as little design as possible.
What is Elegant Design?
Elegant design is about seeking to achieve a visual quality that will attract
your audience and sustain that sentiment throughout the experience, far
beyond just the initial moments of engagement. This is presented as the
third principle for good reason. Any choices you make towards achieving
‘elegance’ must not undermine the accomplishment of trustworthiness and
accessibility in your design. Indeed, in pursuing the achievement of the
other principles, elegance may have already arrived as a by-product of
trustworthy and accessible design thinking. Conversely, the visual ‘look
and feel’ of your work will be the first thing viewers encounter before
experiencing the consequences of your other principle-led thinking. It
therefore stands that optimising the perceived appeal of your work will
have a great impact on your viewers.
The pursuit of elegance is elusive, as is its definition: what gives
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something an elegant quality? As we know, beauty is in the eye of the
beholder, but how do we really recognise elegance when we are
confronted by it?
When thinking about what the pursuit of elegance of means, the kind of
words that surface in my mind are adjectives like stylish, dignified,
effortless and graceful. For me, they capture the timelessness of elegance,
certainly more so than fancy, cool or trendy, which seem more momentary.
Elegance is perhaps appreciated more when it is absent from or not
entirely accomplished in a design. If something feels cumbersome,
inconsistent and lacking a sense of harmony across its composition and use
of colour, it is missing that key ingredient of elegance.
‘When working on a problem, I never think about beauty. I think only
how to solve the problem. But when I have finished, if the solution is
not beautiful, I know it is wrong.’ Richard Buckminster Fuller,
celebrated inventor and visionary
‘Complete is when something looks seamless, as if it took little effort to
produce.’ Sarah Slobin, Visual Journalist
When it feels like style over substance has been at the heart of decision-
making, no apparent beauty can outweigh the negatives of an obstructed or
absent functional experience. While I’m loathe to dwell on forcing a
separation in concern between form and function, as a beginner working
through the design stages and considering all your options, functional
judgements will generally need to be of primary concern. However, it is
imperative that you also find room for appropriate aesthetic expression. In
due course your experience will lead you to fuse the two perspectives
together more instinctively.
In his book The Shape of Design, designer Frank Chimero references a
Shaker proverb: ‘Do not make something unless it is both necessary and
useful; but if it is both, do not hesitate to make it beautiful.’ In serving the
principles of trustworthy and accessible design, you will have hopefully
covered both the necessary and useful. As Chimero suggests, if we have
served the mind, our heart is telling us that now is the time to think about
beauty.
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How Do You Achieve Elegance in Design?
There are several components of design thinking that I believe directly
contribute to achieving an essence of elegance.
‘“Everything must have a reason”… A principle that I learned as a
graphic designer that still applies to data visualisation. In essence,
everything needs to be rationalised and have a logic to why it’s in the
design/visualisation, or it’s out.’ Stefanie Posavec, Information
Designer
Eliminate the arbitrary: As with any creative endeavour or
communication activity, editing is perhaps the most influential skill,
and indeed attitude. Every single design decision you make – every
dot, every pixel – should be justifiable. Nothing that remains in your
work should be considered arbitrary. Even if there isn’t necessarily a
scientific or theoretical basis for your choices, you should still be able
to offer reasons for every thing that is included and also excluded.
The reasons you can offer for design options being rejected or
removed are just as important in evidence of your developing eye for
visualisation design.
Often you will find yourself working alone on a data visualisation
project and will therefore need to demonstrate the discipline and
competence to challenge yourself. Avoid going through the motions
and don’t get complacent. Why present data on a map if there is
nothing spatially relevant about the regional patterns? Why include
slick interactive features if they really add no value to the experience?
It is easy to celebrate the brilliance of your amazing ideas and become
consumed by work that you have invested deeply in – both your time
and emotional energy. Just don’t be stubborn or precious. If
something is not working, learn to recognise when to not pursue it
any further and then kill it.
Thoroughness: A dedicated visualiser should be prepared to agonise
over the smallest details and want to resolve even the smallest pixel-
width inaccuracies. The desire to treat your work with this level of
attention demonstrates respect for your audience: you want them to be
able to work with quality so pride yourself on precision. Do not
neglect checking, do not cut corners, do not avoid the non-sexy
duties, and never stop wanting to do better.
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Style: This is another hard thing to pin down, especially as the word
itself can have different meanings for people, and especially when it
has been somewhat ‘damaged’ by the age-old complaints around
something demonstrating style over substance. Developing a style –
or signature, as Thomas Clever suggests – is in many ways a
manifestation of elegant design. The decisions around colour
selection, typography and composition are all matters that influence
your style. The development of a style preserves the consistency of
your strongest design values, leaving room to respond flexibly to the
nuances of each different task you face. It is something that develops
in time through the choices you make and the good habits you
acquire.
‘You don’t get there [beauty] with cosmetics, you get there by taking
care of the details, by polishing and refining what you have. This is
ultimately a matter of trained taste, or what German speakers call
fingerspitzengefühl (“finger-tip-feeling”)’. Oliver Reichenstein,
founder of Information Architects (iA)
Many news and media organisations seek to devise their own style
guides to help visualisers, graphics editors and developers navigate
through the choppy waters of design thinking. This is a conscious
attempt to foster consistency in approach as well as create efficiency.
In these industries, the perpetual pressure of tight timescales from the
relentless demands of the news cycle means that creating efficiency is
of enormous value. By taking away the burden of having always to
think from scratch about their choices, the visualisers in such
organisations are left with more room to concern themselves with the
fundamental challenge of what to show and not just get consumed by
how to show it. The best styles will stand out as instantly
recognisable: there is a reason why you can instantly pick out the
work of the New York Times, National Geographic, Bloomberg, the
Guardian, the Washington Post, the Financial Times, Reuters and the
South China Morning Post.
Decoration should be additive, not negative: The decorative arts
are historically considered to be an intersection of that which is useful
and beauty, yet the term decoration when applied to data can often
suggest a negative connotation of dressing it up using superfluous
devices to attract people, but without any real substance. Visual
embellishments are, in moderation and when discernibly deployed,
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effective devices for securing visual appeal and preserving
communicated value. This is especially the case when they carry a
certain congruence with the subject matter or key message, such as
with the use of the different ground textures in the treemap displayed
in Figure 1.13. In this graphic, Vienna is reduced to an illustrative
100m2 apartment and the floor plan presents the proportional
composition of the different types of space and land in the city. This
is acceptable gratuitousness because the design choices are additive,
not negatively obstructive or distracting.
‘I suppose one could say our work has a certain “signature”. “Style” – to
me – has a negative connotation of “slapped on” to prettify something
without much meaning. We don’t make it our goal to have a
recognisable (visual) signature, instead to create work that truly matters
and is unique. Pretty much all our projects are bespoke and have a
different end result. That is one of the reasons why we are more
concerned with working according to values and principles that
transcend individual projects and I believe that is what makes our work
recognisable.’ Thomas Clever, Co-founder CLEVER°FRANKE, a
data driven experiences studio
Figure 1.13 If Vienna Would be an Apartment
Any design choices you make with the aim of enhancing appeal through
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novelty or fun need to support, not distract from, the core aim of
facilitating understanding. Be led by your data and your audience, not your
ideas. There should, though, always be room to explore ways of seeking
that elusive blend of being fun, engaging and informative. The bar chart in
Figure 1.14 reflects this: using Kit Kat-style fingers of chocolate for each
bar and a foil wrapper background, it offers an elegant and appealing
presentation that is congruent with its subject.
Figure 1.14 Asia Loses Its Sweet Tooth for Chocolate
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Allow your personality to express itself in the times and places where such
flair is supportive of the aims of facilitating understanding. After all, a
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singularity of style is a dull existence. As Groove Armada once sang: ‘If
everybody looked the same, we’d get tired of looking at each other.’
Not about minimalism: As expressed by Rams’ principle ‘Good
design is as little design as possible’, elegant design achieves a certain
invisibility: as a viewer you should not see design, you should see
content. This is not to be confused with the pursuit of minimalism,
which is a brutal approach that strips away the arbitrary but then cuts
deeper. In the context of visualisation, minimalism can be an
unnecessarily savage and austere act that may be incongruous with
some of the design options you may need to include in your work.
‘I’ve come to believe that pure beautiful visual works are somehow
relevant in everyday life, because they can become a trigger to get
people curious to explore the contents these visuals convey. I like the
idea of making people say “oh that’s beautiful! I want to know what this
is about!” I think that probably (or, at least, lots of people pointed that
out to us) being Italians plays its role on this idea of “making things not
only functional but beautiful”.’ Giorgia Lupi, Co-founder and Design
Director at Accurat
In ‘De architectura’, a thesis on architecture written around 15 BC by
Marcus Vitruvius Pollio, a Roman architect, the author declares how the
essence of quality in architecture is framed by the social relevance of
the work, not the eventual form or workmanship towards that form.
What he is stating here is that good architecture can only be measured
according to the value it brings to the people who use it. In a 1624
translation of the work, Sir Henry Wooton offers a paraphrased version
of one of Vitruvius’s most enduring notions that a ‘well building hath
three conditions: firmness, commodity, and delight’, of which a further
interpretation for today might be read as ‘sturdy, useful, and beautiful’.
One can easily translate these further to fit with these principles of good
visualisation design. Trustworthy is sturdy – it is robust, reliable, and
has integrity. Useful is accessible – it can be used without undue
obstruction. Beautiful is elegant – it appeals and retains attraction.
1.3 Distinctions and Glossary
As in any text, consistency in the meaning of terms or language used
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around data visualisation is important to preserve clarity for readers. I
began this chapter with a detailed breakdown of a proposed definition for
the subject. There are likely to be many other terms that you either are
familiar with or have heard being used. Indeed, there are significant
overlaps and commonalities of thought between data visualisation and
pursuits like, for example, infographic design.
As tools and creative techniques have advanced over the past decade, the
traditional boundaries between such fields begin to blur. Consequently, the
practical value of preserving dogmatic distinctions reduces accordingly.
Ultimately, the visualiser tasked with creating a visual portrayal of data is
probably less concerned about whether their creation will be filed under
‘data visualisation’ or ‘infographic’ as long as it achieves the aim of
helping the audience achieve understanding.
Better people than me attach different labels to different works
interchangeably, perhaps reflecting the fact that these dynamic groups of
activities are all pursuing similar aims and using the same raw material –
data – to achieve them. Across this book you will see plenty of references
to and examples of works that might not be considered data visualisation
design work in the purest sense. You will certainly see plenty of examples
of infographics.
The traditional subject distinctions still deserve to be recognised and
respected. People are rightfully proud of identifying with a discipline they
have expertise or mastery in. And so, before you step into the design
workflow chapters, it is worthwhile to spend a little time establishing
clarifications and definitions for some of the related fields and activities so
all readers are on the same page of understanding. Additionally, there is a
glossary of the terms used that will help you more immediately understand
the content of later chapters. It makes sense to position those clarifications
in this chapter as well.
Distinctions
Data vis: Just to start with one clarification. While the abbreviated
term of data visualisation might be commonly seen as ‘data vis’ (or
‘data viz’; don’t get me started on the ‘z’ issue), and this is probably
how all the cool kids on the street and those running out of characters
on Twitter refer to it, I am sticking with the full Sunday name of ‘data
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visualisation’ or at the very least the shortened term ‘visualisation’.
Information visualisation: There are many who describe data
visualisation as information visualisation and vice versa, myself
included, without a great deal of thought for the possible differences.
The general distinction, if there is any, tends to be down to one’s
emphasis on the input material (data) or the nature of the output form
(information). It is also common that information visualisation is used
as the term to define work that is primarily concerned with visualising
abstract data structures such as trees or graphs (networks) as well as
other qualitative data (therefore focusing more on relationships rather
than quantities).
Infographics: The classic distinction between infographics and data
visualisation concerns the format and the content. Infographics were
traditionally created for print consumption, in newspapers or
magazines, for example. The best infographics explain things
graphically –systems, events, stories – and could reasonably be
termed explanation graphics. They contain charts (visualisation
elements) but may also include illustrations, photo-imagery, diagrams
and text. These days, the art of infographic design continues to be
produced in static form, irrespective of how and where they are
published.
Over the past few years there has been an explosion in different forms
of infographics. From a purist perspective, this new wave of work is
generally viewed as being an inferior form of infographic design and
may be better suited to terms like info-posters or tower graphics
(these commonly exist with a fixed-width dimension in order to be
embedded into websites and social media platforms). Often these
works will be driven by marketing intent through a desire to get
hits/viewers, generally with the compromising of any real valuable
delivery of understanding. It is important not to dismiss entirely the
evident – if superficial – value of this type of work, as demonstrated
by the occasionally incredible numbers for hits received. If your
motive is ‘bums on seats’ then this approach will serve you well.
However, I would question the legitimacy of attaching the term
infographic to these designs and I sense the popular interest in these
forms is beginning to wane.
Visual analytics: Some people use this term to relate to analytical-
style visualisation work, such as dashboards, that serve the role of
operational decision support systems or provide instruments of
business intelligence. Additionally, the term visual analytics is often
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used to describe the analytical reasoning and exploration of data
facilitated by interactive tools. This aligns with the pursuit of
exploratory data analysis that I will be touching on in Chapter 5.
Data art: Aside from the disputes over the merits of certain
infographic work, data art is arguably the other discipline related to
visualisation that stirs up the most debate. Those creating data art are
often pursuing a different motive to pure data visualisation, but its
sheer existence still manages to wind up many who perhaps reside in
the more ‘purest’ visualisation camps. For data artists the raw
material is still data but their goal is not driven by facilitating the kind
of understanding that a data visualisation would offer. Data art is
more about pursuing a form of self-expression or aesthetic exhibition
using data as the paint and algorithms as the brush. As a viewer,
whether you find meaning in displays of data art is entirely down to
your personal experience and receptiveness to the open interpretation
it invites.
Information design: Information design is a design practice
concerned with the presentation of information. It is often associated
with the activities of data visualisation, as it shares the underlying
motive of facilitating understanding. However, in my view,
information design has a much broader application concerned with
the design of many different forms of visual communication, such as
way-finding devices like hospital building maps or in the design of
utility bills.
Data science: As a field, data science is hard to define, so it is easier
to consider this through the ingredients of the role of data scientists.
They possess a broad repertoire of capabilities covering the gathering,
handling and analysing of data. Typically this data is of a large size
and complexity and originates from multiple sources. Data scientists
will have strong mathematical, statistical and computer science skills,
not to mention astute business experience and many notable ‘softer’
skills like problem solving, communication and presentation. If you
find somebody with all these skills, tie them to a desk (legally) and
never ever let them leave your organisation.
Data journalism: Also known as data-driven journalism (DDJ), this
concerns the increasingly recognised importance of having numerical,
data and computer skills in the journalism field. In a sense it is an
adaption of data visualisation but with unquestionably deeper roots in
the responsibilities of the reporter/journalist.
Scientific visualisation: This is another form of a term used by many
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people for different applications. Some give exploratory data analysis
the label scientific visualisation (drawing out the scientific methods
for analysing and reasoning about data). Others relate it to the use of
visualisation for conceiving highly complex and multivariate datasets
specifically concerning matters with a scientific bent (such as the
modelling functions of the brain or molecular structures).
Glossary
The precision and consistency of language in this field can get caught up
in a little too much semantic debate at times, but it is important to establish
early on some clarity about its usage and intent in this book at least.
Roles and Terminology
Project: For the purpose of this book, you should consider any data
visualisation creation activity to be consistent with the idea of a
project. Even if what you are working on is only seen as the smallest
of visualisation tasks that hardly even registers on the bullet points of
a to-do list, you should consider it a project that requires the same
rigorous workflow process approach.
Visualiser: This is the role I am assigning to you – the person making
the visualisation. It could be more realistic to use a term like
researcher, analyst, creator, practitioner, developer, storyteller or, to
be a little pretentious, visualist. Designer would be particularly
appropriate but I want to broaden the scope of the role beyond just the
design thinking to cover all aspects of this discipline.
Viewer: This is the role assigned to the recipient, the person who is
viewing and/or using your visualisation product. It offers a broader
and better fit than alternatives such as consumer, reader, recipient or
customer.
Audience: This concerns the collective group of people to whom you
are intending to serve your work. Within the audience there will be
cohorts of different viewer types that you might characterise through
distinct personas to help your thinking about serving the needs of
target viewers.
Consuming: This will be the general act of the viewer, to consume. I
will use more active descriptions like ‘reading’ and ‘using’ when
consuming becomes too passive and vague, and when distinctions are
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needed between reading text and using interactive features.
Creating: This will be the act of the visualiser, to create. This term
will be mainly used in contrast with consuming to separate the focus
between the act of the visualiser and the act of the viewer.
Data Terminology
Data is: I’m sorry ‘data are’ fans, but that’s just not how normal
people speak. In this book, it’s going to be ‘data is’ all the way.
Unless my editor disagrees, in which case you won’t even see this
passage.
Raw data: Also known as primary data, this is data that has not been
subjected to statistical treatment or any other transformation to
prepare it for usage. Some people have a problem with the implied
‘rawness’ this term claims, given that data will have already lost its
purity having been recorded by some measurement instrument,
stored, retrieved and maybe cleaned already. I understand this view,
but am going to use the term regardless because I think most people
will understand its intent.
Dataset: A dataset is a collection of data values upon which a
visualisation is based. It is useful to think of a dataset as taking the
form of a table with rows and columns, usually existing in a
spreadsheet or database.
Tabulation: A table of data is based on rows and columns. The rows
are the records – instances of things – and the columns are the
variables – details about the things. Datasets are visualised in order to
‘see’ the size, patterns and relationships that are otherwise hard to
observe. For the purpose of this book, I distinguish between types of
datasets that are ‘normalised’ and others that are ‘cross-tabulated’.
This distinction will be explained in context during Chapter 5.
Variables: Variables are related items of data held in a dataset that
describe a characteristic of those records. It might be the names, dates
of birth, genders and salaries of a department of employees. Think of
variables as the different columns of values in a table, with the
variable name being the descriptive label on the header row. There
are different types of variables including, at a general level,
quantitative (e.g. salary) and categorical (e.g. gender). A chart plots
the relationship between different variables. For example, a bar chart
might show the number of staff (with the size of bar showing the
quantity) across different departments (one bar for each department or
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category).
Series: A series of values is essentially a row (or column, depending
on table layout) of related values in a table. An example of a series of
values would be all the highest temperatures in a city for each month
of the year. Plotting this on a chart, like a line chart, would produce a
line for that city’s values across the year. Another line could be added
to compare temperatures for another city thus presenting a further
series of values.
Data source: This is the term used to describe the origin of data or
information used to construct the analysis presented. This is an
important feature of annotation that can help gain trust from viewers
by showing them all they need to know about the source of the data.
Big Data: Big Data is characterised by the 3Vs – high volume
(millions of rows of data), high variety (hundreds of different
variables/columns) and high velocity (new data that is created rapidly
and frequently, every millisecond). A database of bank transactions or
an extract from a social media platform would be typical of Big Data.
It is necessary to take out some of the hot air spouted about Big Data
in its relationship with data visualisation. The ‘Bigness’ (one always
feels obliged to include a capitalised B) of data does not
fundamentally change the tasks one faces when creating a data
visualisation, it just makes it a more significant prospect to work
through. It broadens the range of possibilities, it requires stronger and
more advanced technology resources, and it amplifies the pressures
on time and resources. With more options the discipline of choice
becomes of even greater significance.
Visualisation
Chart type: Charts are individual, visual representations of data.
There are many ways of representing your data, using different
combinations of marks, attributes, layouts and apparatus: these
combinations form archetypes of charts, commonly reduced to simply
chart types. There are some charts you might already be familiar
with, such as the bar chart, pie chart or line chart, while others may be
new to you, like the Sankey diagram, treemap or choropleth map.
Graphs, charts, plots, diagrams and maps: Traditionally the term
graph has been used to describe visualisations that display network
relationships and chart would be commonly used to label common
devices like the bar or pie chart. Plots and diagrams are more
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specifically attached to special types of displays but with no pattern
of consistency in their usage. All these terms are so interchangeable
that useful distinction no longer exists and any energy expended in
championing meaningful difference is wasted. For the purpose of this
book, I will generally stick to the term chart to act as the single label
to cover all visualisation forms. In some cases, this umbrella label
will incorporate maps for the sake of convenience even though they
clearly have a unique visual structure that is quite different from most
charts. By the way, the noise you just heard is every cartographer
reading this book angrily closing it shut in outrage at the sheer
audacity of my lumping maps and charts together.
Graphic: The term graphic will be more apt when referring to
visuals focused more on information-led explanation diagrams
(infographics), whereas chart will be more concerned with data-
driven visuals.
Storytelling: The term storytelling is often attached to various
activities around data visualisation and is a contemporary buzzword
often spread rather thinly in the relevance of its usage. It is a thing but
not nearly as much a thing as some would have you believe. I will be
dampening some of the noise that accompanies this term in the next
chapter.
Format: This concerns the difference in output form between printed
work, digital work and physical visualisation work.
Function: This concerns the difference in functionality of a
visualisation, whether it is static or interactive. Interactive
visualisations allow you to manipulate and interrogate a computer-
based display of data. The vast majority of interactive visualisations
are found on websites but increasingly might also exist within apps
on tablets and smartphones. In contrast, a static visualisation displays
a single-view, non-interactive display of data, often presented in print
but also digitally.
Axes: Many common chart types (such as the bar chart and line chart)
have axis lines that provide reference for measuring quantitative
values or assigning positions to categorical values. The horizontal
axis is known as the x-axis and the vertical axis is known as the y-
axis.
Scale: Scales are marks on axes that describe the range of values
included in a chart. Scales are presented as intervals (10, 20, 30, etc.)
representing units of measurement, such as prices, distances, years or
percentages, or in keys that explain the associations between, for
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example, different sizes of areas or classifications of different colour
attributes.
Legend: All charts employ different visual attributes, such as colours,
shapes or sizes, to represent values of data. Sometimes, a legend is
required to house the ‘key’ that explains what the different scales or
classifications mean.
Outliers: Outliers are points of data that are outside the normal range
of values. They are the unusually large or small or simply different
values that stand out and generally draw attention from a viewer –
either through amazement at their potential meaning or suspicion
about their accuracy.
Correlation: This is a measure of the presence and extent of a mutual
relationship between two or more variables of data. You would
expect to see a correlation between height and weight or age and
salary. Devices like scatter plots, in particular, help visually to portray
possible correlations between two quantitative values.
Summary: Defining Data Visualisation
In this chapter you have learned a definition of data visualisation: ‘The
representation and presentation of data to facilitate understanding.’ The
process of understanding a data visualisation involves three stages,
namely:
Perceiving: what can I see?
Interpreting: what does it mean?
Comprehending: what does it mean to me?
You were also introduced to the three principles of good visualisation
design:
Good data visualisation is trustworthy.
Good data visualisation is accessible.
Good data visualisation is elegant.
Finally, you were presented with an array of descriptions and explanations
about some of the key terms and language used in this field and throughout
the book.
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2 Visualisation Workflow
Clear, effective and efficient thinking is the critical difference between a
visualisation that succeeds and one that fails. You cannot expect just to
land accidentally on a great solution. You have got to work for it.
In this chapter I will outline the data visualisation workflow that forms the
basis of this book’s structure and content. This workflow offers a creative
and analytical process that will guide you from an initial trigger that
instigates the need for a visualisation through to developing your final
solution.
You will learn about the importance of process thinking, breaking down
the components of a visualisation design challenge into sequenced,
manageable chunks. This chapter will also recommend some practical tips
and good habits to ensure the workflow is most effectively adopted.
2.1 The Importance of Process
As I have already established, the emphasis of this book is on better
decision making. There are so many different things to think about when
creating a data visualisation, regardless of whether the output will be the
simplest of charts or the most ambitious of multi-faceted digital
implementations.
The decisions you will face will inevitably vary in the weight of their
significance. There will be some big choices – matters like defining your
editorial angles and selecting the best fit chart type – and many seemingly
small ones – such as picking the precise shade of grey for a chart’s axis
labels. The process of creating a visualisation generally follows the Pareto
principle, whereby 20% of decisions made have implications for about
80% of the final visible design. However, just because some decisions will
appear more significant in the final output, as visualisers we need to attend
to every single decision equally, caring about detail, precision and
accuracy.
To repeat, one of the main mental barriers to overcome for those new to
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the field is to acknowledge that the pursuit of perfect in data visualisation
is always unfulfilled. There are better and there are worse solutions, but
there is no perfect. Perfect exists in an artificial vacuum. It is free of
pressures, has no constraints. That is not real life. There will always be
forces pushing and pulling you in different directions. There may be
frustrating shortcomings in the data you will have to work with or
limitations with your technical capabilities. As discussed, people – your
audience members – introduce huge inconsistencies. They – we – are
complex, irrational and primarily different. Accepting the absence of
perfection helps us unburden ourselves somewhat from the constant
nagging sense that we missed out on discovering the perfect solution. This
can prove quite liberating.
That is not to say our ambitions need to be lowered. Quite the opposite.
We should still strive for best, the absolute optimum solution given the
circumstances we face. To achieve this requires improved effectiveness
and efficiency in decision making. We need to make better calls, more
quickly. The most reliable approach to achieving this is by following a
design process.
The process undertaken in this book is structured around the following
stages (Figure 2.1).
Figure 2.1 The Four Stages of the Visualisation Workflow
Here are a few observations about this process ahead of its
commencement.
Pragmatic: This process aims to provide a framework for thinking,
rather than instructions for learning. As described in the Introduction,
there are very few universal rules to rely upon. While the comfort
provided by rules is what many might seek at the beginning of their
learning journey, flexible pragmatism beats dogmatism in any
situation. Useful rules do exist in visualisation but are often related to
quite micro-level matters. I will come to discuss these in due course.
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Reducing the randomness of your approach: The value of the
process is that it guides your entry and closing points: where and how
to begin your work as well as how and when it will be finished. When
you are new to data visualisation, the sheer extent of things to think
about can be quite an overwhelming prospect. This workflow
approach aims to break down activities into a connected system of
thinking that will help to organise and preserve the cohesiveness of
your activities. The process incrementally leads you towards
developing a solution, with each stage building on the last and
informing the next. The core purpose of the approach is to give you a
greater sense of the options that exist at each stage and provide you
with better information with which to make your choices.
Protect experimentation: The systematic approach I am advocating
in this book should not be seen as squeezing out the scope for
creativity or eradicating any space for experimentation. It is natural to
want to reduce wasted effort, but at the same time it is absolutely vital
to seek opportunities – in the right places – for imagination to
blossom. In reality, many of the projects you will work on will not
necessarily rely on much creative input. There will be projects that
have pressures on time – and a need to compromise on experimenting
in favour of the desire for efficiency. There will be subjects or
datasets that you work with that are just not congruent with overt
creative thinking. It is about striking a balance, affording time on
those activities that will bring the right blend of value to suit each
context.
‘I truly feel that experimentation (even for the sake of experimentation)
is important, and I would strongly encourage it. There are infinite
possibilities in diagramming and visual communication, so we have
much to explore yet. I think a good rule of thumb is to never allow your
design or implementation to obscure the reader understanding the
central point of your piece. However, I’d even be willing to forsake this,
at times, to allow for innovation and experimentation. It ends up moving
us all forward, in some way or another.’ Kennedy Elliott, Graphics
Editor, The Washington Post
Facilitate adaptability and iteration: This workflow is
characterised as a design process rather than a procedure. A good
process should facilitate the adaptability and remove the inflexibility
of a defined procedure of operation. Although the activities are
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introduced and presented in this book in a linear fashion, inevitably
there is much iteration that takes place. There will be times when you
will have to revisit decisions, maybe even redo activities in a
completely different way given what you have discovered further
down the line. If you make mistakes or bad calls – and everyone does
– it is important to fail gracefully but recover quickly. You will need
to be able to respond to changes in circumstances and accommodate
their impact fast. A good process cushions the impact of situations
arising like this.
The first occasion, not the last: It is important to note that the tasks
you face at each stage in the process will represent the first occasion
you pay attention to these matters, but not the final occasion. There is
something of a trickle-down effect here. Many of the concerns you
will be faced with at the start of a challenge will likely continue to
echo in your thoughts right through to the end. Some things are just
not possible to close off that easily. Take the ongoing demands of
profiling who your audiences are and what they might need. That
thinking starts early and should actually never drop off your radar.
The nature of the process gives you the best chance of keeping all the
plates spinning that need to be spun, knowing which ones can be left
to drop and when.
Always the same process: The range of visualisation challenges you
will face in your career will vary. Even if you are producing the same
work every month, no two projects will provide the same experience:
just having an extra month of data means it has a new shape or size. It
is different. Some projects you work on will involve fairly simple
data, others will involve hugely complex data. In some cases you will
have perhaps two hours or two days to deliver a solution, in others
you might have two months. The key thing is that the process you
follow will always require the same activities in the same sequence,
regardless of the size, speed and complexity of your challenge. The
main difference is that any extremes in the circumstances you face
will amplify the stresses at each stage of the process and place greater
demands on the need for thorough, effective and timely decision
making.
Partitioning your mindset: Within each of the sequenced stages
listed in Figure 2.1 there will be different demands on your mindset:
sometimes you are thinking, sometimes you are doing, sometimes
you are making. When you are working alone, especially, it is
important to appreciate the activities that will require different
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mindsets:
Thinking: The duties here will be conceptual in nature, requiring
imagination and judgment, such as formulating your curiosity,
defining your audience’s needs, reasoning your editorial
perspectives, and making decisions about viable design choices.
Doing: These are practical tasks that will still engage the brain,
obviously, but manifest themselves through more hands-on
activities like sketching ideas and concepts, learning about a
subject through research, gathering and handling your data.
Making: These involve the constructive and technical activities
that specifically relate to the production cycle as you face the
challenge of translating promising, well-considered design
concepts into effective, working solutions.
‘You need a design eye to design, and a non-designer eye to feel what
you designed. As Paul Klee said, “See with one eye, feel with the
other”.’ Oliver Reichenstein, founder of Information Architects (iA)
2.2 The Process in Practice
Throughout this book I will call out key points of advice in the form of
useful tips, tactics or good habits you should be looking to consider
employing. Many of these have been informed by interviews with some of
the brilliant people working in this field today. As you are about to
commence the design workflow here are some pieces of advice that
transcend any individual stage of the process.
Managing progress and resources: Good planning, time and
resource management keep a process cohesive and progressing. They
represent the lubricant. You will rarely have the luxury of working on
a project that has no defined end date and so adhering to imposed or
self-imposed timescales is especially important. It is very easy to get
swallowed up by the demands of certain activities, particularly those
involved in the ‘working with data’ stage. Similarly the production
cycle (which takes place during and beyond Part C), as you iterate
between idea, prototype and construction, can at times appear never
to have an end in sight. As one task is finished, another two always
seem to appear. As you get closer to a deadline you will either sink or
swim: for some the pressure of time is crippling; others thrive on the
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adrenaline it brings and their focus is sharpened as a result.
Recognising the need to factor in time for some of the broader
responsibilities – clerical tasks, arranging demo meetings and skype
calls, file management and running backups – will prove hugely
beneficial by the end.
Room to think: On the theme of task duration and progress, it is
important to build in the capacity to think. The notion of brain ‘states’
is relevant here, in particular the ‘alpha’ state which kicks in most
commonly when we are particularly relaxed. Being in this state helps
to heighten your imagination, learning and thought process.
Apologies for the mental image but I do some of my most astute
thinking in the shower or bath, and just before going to sleep at night.
These are the occasions when I am most likely drifting into a relaxed
alpha state and help me to contemplate most clearly the thoughts and
ideas I might have. I find train or air travel achieves the same as does
lying on a beach. Unfortunately in the latter scenario I just don’t care
enough about work in that moment to note down my frequent genius
ideas (what do you mean ‘which ones’?). If I have a task that will
take two days of my time but the deadline is further away, I typically
try to break down the time I give to it across smaller clusters of three
to four hours spread across four days of activity in order to create
sufficient opportunities for my brain to tick over during the
intervening gaps and hopefully allow good ideas to ferment.
Heuristics to support decisions: As I have discussed, there will be
occasions when the best choice does not present itself, when time is
pressurising you and when you will need to make a call. You might
have to occasionally rely on heuristic techniques that help to speed up
your decision-making at certain stages. Although this might seem an
unsatisfactory tactic to consider, given the previously stated need to
eliminate arbitrary choices, heuristics can remain consistent with this
desire when they rely on educated, intuitive or common-sense
judgements. As you develop your experience, the astuteness of such
heuristic judgments will be increasingly reliable to fall back on when
the need arises.
Pen and paper: The humble pairing of pen and paper will prove to
be a real ally throughout your process. I will not over-sentimentally
claim this is the most important tool combination because, unless you
are producing artisan hand-drawn work, you will have other technical
tools that would probably rise up the importance list. However, the
point here is that capturing ideas and creating sketches are a critical
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part of your process. Do not rely on your memory; if you have a great
idea sketch it down. This activity is never about artistic beauty. It
does not need you to be an artist, it just needs you to get things out of
your head and onto paper, particularly if you are collaborating with
others. If you are incredibly fortunate to be so competent with a given
tool that you find it more natural than using pen and paper to ‘sketch’
ideas quickly, then this is of course absolutely fine, as long as it is
indeed the quickest medium to do so.
Note-taking: Whether this is via pen and paper, or in Word, or a
Google doc, note-taking is a vital habit to get into. This is about
preserving records of important details such as:
information about the sources of data you are using;
calculations or manipulations you have applied to your data;
assumptions you have made;
data descriptions, particularly if explanations have been offered
to use verbally by somebody who knows the data well;
questions you have yet to get answers to;
the answers you did get to your questions;
terminology, abbreviations, codes – things you need to
remember the meaning and associations in your data;
task lists and wish lists of features or ideas you would like to
consider pursuing;
issues or problems you can foresee;
websites or magazines that you saw and gave you a bit of
inspiration;
ideas you have had or rejected.
Note-taking is easier said than done, and I am among the least natural
note-takers to roam this Earth, but I have forced it into becoming a habit
and a valuable one at that.
‘Because I speak the language of data, I can talk pretty efficiently with
the experts who made it. It doesn’t take them long, even if the subject is
new to me, for them to tell me any important caveats or trends. I also
think that’s because I approach that conversation as a journalist, where
I’m mostly there to listen. I find if you listen, people talk. (It sounds so
obvious but it is so important.) I find if you ask an insightful question,
something that makes them say “oh, that’s a good point,” the whole
conversation opens up. Now you’re both on the same side, trying to get
this great data to the public in an understandable way.’ Katie Peek,
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Data Visualization Designer and Science Journalist
Communication: Communication is a two-way activity. Firstly, it is
about listening to stakeholders (clients or colleagues) and your
audience: what do they want, what do they expect, what ideas do they
have? In particular, what knowledge do they have about your subject?
Secondly, communication is about speaking to others: presenting
ideas, updating on progress, seeking feedback, sharing your thoughts
about possible solutions, and promoting and selling your work
(regardless of the setting, you will need to do this). If you do not
know the intimate details of your subject matter you will need to
locate others who do: find smart people who know the subject better
than you or find smart people who do not know the subject but are
just smart. You cannot avoid the demands of communicating so do
not hide behind your laptop – get out there and speak and listen to
people who can help you.
Research: Connected to the need for good communication is the
importance of research. This is an activity that will exist as a constant,
running along the spine of your process thinking. You cannot know
everything about your subject, about the meaning of your data, about
the relevant and irrelevant qualities it possesses. As you will see later,
data itself can only tell us so much; often it just tells us where
interesting things might exist, not what actually explains why they are
interesting.
‘Research is key. Data, without interpretation, is just a jumble of words
and numbers – out of context and devoid of meaning. If done well,
research not only provides a solid foundation upon which to build your
graphic/visualisation, but also acts as a source of inspiration and a
guidebook for creativity. A good researcher must be a team player with
the ability to think critically, analytically, and creatively. They should
be a proactive problem solver, identifying potential pitfalls and
providing various roadmaps for overcoming them. In short, their
inclusion should amplify, not restrain, the talents of others.’ Amanda
Hobbs, Researcher and Visual Content Editor
Attention to detail: Like note-taking, this will be something that
might not be a natural trait for some but is so important. You cannot
afford to carry any errors in your work. Start every project with that
commitment. This is such an important ingredient to securing trust in
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your work. The process you are about to learn is greatly influenced by
the concept of ‘aggregation of marginal gains’. You need to sweat the
small stuff. Even if many of your decisions seem small and
inconsequential, they deserve your full attention and merit being done
right, always. You should take pride in the fine detail of your design
thinking, so embrace the need for checking and testing. If you are so
immersed in your work that you become blind to it, get others to help
– call on those same smart people you identified above. As someone
who once published a graphic stating Iran’s population was around 80
billion and not 80 million, I know how one tiny mistake can cause the
integrity of an entire project to crumble to the ground. You do not get
a second chance at a first impression, somebody once said. I forget
who, I wasn’t paying attention …
Make it work for you: The only way you will truly find out whether
a process works for you is if you practise it, relentlessly. As I have
stated, every project will be different even if only in small ways.
However, if you just cannot get the approach presented in this book to
fit your personality or purpose, modify it. We are all different. Do not
feel like I am imposing this single approach. Take it as a proposed
framework based on what has worked for me in the past. Bend it,
stretch it, and make it work. As you become more experienced (and
confident through having experienced many different types of
challenges) the many duties involved in data visualisation design will
become second nature, by which time you probably will no longer be
aware of even observing a process.
Be honest with yourself: Feedback, editing, not doing certain things,
are disciplines of the effective visualiser. Honesty with yourself is
vital, especially as you are often working on a solo project but need
so many different skill sets and mindsets. As I mentioned in the last
section, preciousness or stubbornness that starts to impede on quality
becomes destructive. Being blind to things that are not working, or
not taking on board constructive feedback just because you have
invested so much time in something, will prove to be the larger
burden. Do not be afraid to kill things when they are not working.
Learn: Reflective learning is about looking back over your work,
examining the output and evaluating your approach. What did you do
well? What would you do differently? How well did you manage
your time? Did you make the best decisions you could given the
constraints that existed? Beyond private reflections, some of the best
material about data visualisation on the Web comes from people
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sharing narratives about their design processes. Read how other
people undertake their challenges. Maybe share your own? You will
find you truly learn about something when you find the space to write
about it and explain it to others. Write up your projects, present your
work to others and, in doing so, that will force you to think ‘why did I
do what I did?’.
‘No work is ever wasted. If it’s not working, let go and move on – it’ll
come back around to be useful later.’ Emma Coats, freelance Film
Director, formerly of Pixar
Summary: Data Visualisation Workflow
In this chapter you were introduced to the design workflow, which
involves four key stages:
1. Formulating your brief: planning, defining and initiating your project.
2. Working with data: going through the mechanics of gathering,
handling and preparing your data.
3. Establishing your editorial thinking: defining what you will show
your audience.
4. Developing your design solution: considering all the design options
and beginning the production cycle.
Undertaking the activities in this workflow require you to partition your
mindset:
Thinking: conceptual tasks, decision making.
Doing: practical undertakings like sketching, visually examining data.
Making: technical duties like analysing data, constructing the
solution.
Finally, you were presented with some general tips and tactics ahead of
putting the process into practice:
This will be the first time you think about each of the stages and
activities, not the last – visualisation design is as much about plate-
spinning management as anything else.
The importance of good project management to manage progress and
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resources cannot be over-emphasised.
Create room to think: clear thinking helps with efficiency of effort.
Pen and paper will prove to be one of your key tools.
Note-taking is a habit worth developing.
Communication is a two-way relationship: it is speaking and
listening.
Attention to detail is an obligation: the integrity of your work is
paramount.
Make the workflow work for you: practise and adapt the approach to
suit you.
Be honest with yourself, do not be precious and have the discipline
not to do things, to kill ideas, to avoid scope-creep.
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Part B The Hidden Thinking
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3 Formulating Your Brief
In Chapter 2 you learnt about the importance of process, taking on data
visualisation challenges using a design workflow to help you make good
decisions. This third chapter initiates the process with formulating your
brief.
The essence of this stage is to identify the context in which your work will
be undertaken and then define its aims: it is the who, what, why, where,
when and how. It can be as formal – and shared with others – or as
informal an activity as you need to make it.
The first contextual task will be to consider why you are producing this
data visualisation – what is its raison d’être? To answer this, you will need
to define what triggered it (the origin curiosity) and what it is aiming to
accomplish (the destination purpose). Recognising that no visualisation
projects are ever entirely free of constraints or limitations, you will also
need to identify the circumstances surrounding the project that will shape
the scope and nature of the project you’re about to undertake.
Following these contextual definitions you will briefly switch your
attention to consider a vision for your work. With the origin and intended
destination mapped out you will be able to form an initial idea about what
will be the best-fit type of design solution. You will be introduced to the
purpose map, which provides a landscape of all the different types of
visualisation you could pursue, helping you establish an early sense of
what you should pursue. To wrap up the chapter, you will allocate some
time to harness the instinctive thoughts you might have had about the
ideas, images, keywords and inspirations that you feel could play a role in
your work.
Collectively this work will provide you with a solid foundation from
which to best inform all your subsequent workflow process stages.
3.1 What is a Brief?
In its simplest form a brief represents a set of expectations and captures all
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the relevant information about a task or project. It is commonly associated
with the parlance of project management or graphic design, but in data
visualisation the need to establish clarity about the definitions and
requirements of a project is just as relevant. This is about establishing the
context of and vision for your work.
When you are working with clients or colleagues it will be in the interests
of all parties to have a mutual understanding of the project’s requirements
and some agreement over the key deliverables. In such situations you may
have already been issued with some form of initial brief from these
stakeholders. This could be as informal as an emailed or verbal request or
as formal as a template-based briefing document. Irrespective of what has
been issued you will get more value from compiling your own briefing
document to ensure you have sufficient information to plan your upcoming
work.
If you are not working for or with others – essentially pursuing work that
you have initiated yourself – you clearly will not have been issued with
any brief, but once again, it will be to your advantage to compile a brief
for yourself. This does not have to be an overly burdensome or
bureaucratic task. I use a simple checklist that is not only practically
lightweight but also comprehensively helpful, comprising a series of
question prompts that I either answer myself or raise with those
stakeholders with whom I am working.
For some beginners, this stage can feel somewhat frustrating. On the
surface it sounds like a clerical prospect when what you really want is just
to get on with the good stuff, like playing with the data and focusing on
creativity. Understanding contextual matters, in particular before anything
else, is too invaluable a practice to neglect. All the decisions that follow in
this workflow will be shaped around the definitions you establish now.
There may be changes but you will reap the benefits from gaining as much
early clarity as possible.
3.2 Establishing Your Project’s Context
Defining Your Origin Curiosity
A worthwhile data visualisation project should commence from the
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starting point of a curiosity. According to the dictionary definition,
curiosity is about possessing ‘a strong desire to know or learn something’.
This aligns perfectly with the goal of data visualisation, defined in Chapter
1 as being to facilitate understanding. By establishing a clear sense of
where your project originated in curiosity terms, the primary force that
shapes your decision making will be the desire to respond effectively to
this expressed intrigue.
‘Be curious. Everyone claims she or he is curious, nobody wants to say
“no, I am completely ‘uncurious’, I don’t want to know about the
world”. What I mean is that, if you want to work in data visualisation,
you need to be relentlessly and systematically curious. You should try to
get interested in anything and everything that comes your way. Also,
you need to understand that curiosity is not just about your interests
being triggered. Curiosity also involves pursuing those interests like a
hound. Being truly curious involves a lot of hard work, devoting time
and effort to learn as much as possible about various topics, and to make
connections between them. Curiosity is not something that just comes
naturally. It can be taught, and it can be learned. So my
recommendation is: develop your curiosity, educate yourself – don’t just
wait for the world to come to you with good ideas. Pursue them.’
Professor Alberto Cairo, Knight Chair in Visual Journalism,
University of Miami, and Visualisation Specialist
A visualisation process that lacks an initially articulated curiosity can lead
to a very aimless solution. After all, what is it you are solving? What
deficit in people’s understanding are you trying to address? Having the
benefit of even just a broad motive can help you tremendously in
navigating the myriad options you face.
The nature of the curiosity that surrounds your work will vary depending
on where it originated and who it is serving. Consider these five scenarios
where the characteristics differ sufficiently to offer different contextual
challenges:
Personal intrigue – ‘I wonder what …’
Stakeholder intrigue – ‘He/she needs to know …’
Audience intrigue – ‘They will need to know …’
Anticipated intrigue – ‘They might be interested in knowing …’
Potential intrigue – ‘There might be something interesting …’
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Let’s work through an illustration of each of these scenarios to explain
their differences and influences.
Firstly, there are situations where a project is instigated in response to a
curiosity borne out of personal intrigue. An example of this type of
situation can be found in the case-study project that I have published as a
digital companion to this book (book.visualisingdata.com) to help
demonstrate the workflow process in practice. The project is titled
‘Filmographics’ and concerns the ebb and flow of the careers of different
movie stars. You can find out more about it by visiting the book’s digital
resources.
The reason I pursued this particular project was because, firstly, I have a
passion for movies and, secondly, I had a particular curiosity about the
emergence, re-emergence and apparent disappearance of certain actors.
Expressed as a question, the core curiosity that triggered this project was:
‘What is the pattern of success or failure in the movie careers of a selection
of notable actors?’
This initial question provided me with immediate clarity: the goal of the
visualisation would be to deliver an ‘answer’ to this question, to help me
better understand how the career patterns look for the different actors
selected. In this case I am the originator of the curiosity and I am pursuing
this project for my own interest.
Ultimately, whether this initially defined curiosity remains the same
throughout the process does not really matter. Quite often one’s initial
expression of curiosity shifts considerably once data has been gathered and
analysed. When more research is carried out on the subject matter you
become more roundly acquainted with the relevance (or otherwise) of the
trigger enquiry. You might alter your pursuit when you realise there is
something different – and potentially more interesting – to explore. You do
not want to be anchored to an enquiry that no longer reflects the most
relevant perspective but it does offer at least a clear starting point – an
initial motive – from which the process begins.
Sometimes, the nature of the motivation for a personal intrigue-based
curiosity is recognition of one’s ignorance about an aspect of a subject that
should be known (a deficit in ‘available’ understanding) more than a
defined interest in a subject that may not be known (possibly creating new
understanding).
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Let’s consider another scenario, still concerning movie-related subject
matter, but to explain a different type of curiosity. Suppose you work for a
movie studio and have been tasked by a casting director to compile a one-
off report that will profile which actors are potentially the best option to
cast in a major sci-fi movie that has just been given the green light to begin
production. You have certain criteria to follow: they have to be female,
aged 30-45, and must fit the description of ‘rising’ star. They must not
have been in other sci-fi movies, nor can they have any of the ‘baggage’
that comes with being associated with huge flops. Their fees should be
under $2 million. You go away, undertake the analysis, and compile a
report showing the career paths of some of the most likely stars who fit the
bill.
This scenario has not come about through your own personal curiosity but
instead you are responding to the specific curiosity of the casting director.
In undertaking this work you effectively inherit – take on – the curiosity of
others. They have briefed you to find the data, analyse it, and then present
the findings to them. This would be an example of curiosity born out of
stakeholder intrigue: work commissioned by a stakeholder who is also
the target audience (or is the prominent party among the intended
audience). There is no anticipation of interest here, rather it is known.
For the third scenario, you might work for a business involved in the
analysis and commentary of the state of the movie industry. Let’s imagine
your company specialises in producing a dashboard that is shared with a
broad group of users comprising Hollywood executives, studio senior
management and casting agents, among others. The dashboard profiles all
aspects of the industry, covering current trends and the career fortunes of a
wide range different actors, helping users to identify who is hot, who is
not, who is emerging, who is declining, who will cost what, who scores
well with different audiences, etc.
The various indicators of information you are compiling and presenting on
the dashboard are based on the recognised needs of the professional
curiosities these people (client users) will have about this subject matter
(movie career statuses). Given the diverse permutations of the different
measures included, not all the information provided will be of interest all
the time to all who consume it, but it is provided and available as and
when they do need it. This would be an example of curiosity born out of
audience intrigue – shaped out of a combination of knowing what will be
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needed and reasonably anticipating what could be needed.
What you are working towards in situations like this is ensuring that all the
relevant aspects of possible curiosity can be brought together in a single
place to serve as many needs as possible. There are similarities here with
the multitude of dials, displays and indicators in the cockpit of an aircraft.
The pilot does not need all that information as an immediate priority all the
time, but may need access to some of the information in a reactive sense
should the situation arise. Additionally, this scenario may be typical of a
varied and larger scale audience in contrast to the more bespoke nature of
a stakeholder intrigue scenario. You will rarely if ever be able to serve
100% of the audience’s potential needs but you can certainly aspire to do
your best.
Consider another similar scenario, but with a different setting used to
illustrate a more subtle distinction. Suppose I am as a graphics editor
working for a newspaper. One of the topics of current attention might
concern the relatively late-career breakthrough of a certain actor, who has
almost overnight moved from roles in relatively modest TV shows to
starring in cinematic blockbusters. It is decided by the assignments editor
that I will work on a graphic that examines the fortunes of this actor’s
career alongside a selection of other actors to provide contrast or draw
comparisons with.
On this occasion the trigger is not necessarily emerging from personal
intrigue. I have essentially been issued with the requirements: even if I
agree with the idea or had a similar thought myself, the organisational
relationship decrees that others will instruct me about which tasks to work
on. A stakeholder – the assignments editor (or others in the editorial
hierarchy) – has determined that this is a topic of interest and worth
exploring. However, in contrast to the stakeholder intrigue scenario, here
the stakeholder is not the intended audience. It is not necessarily even a
curiosity they have themselves. The motive for this work is likely driven
by the machinations of current affairs: what is newsworthy and likely to be
of some interest to readers? Therefore, the belief is that this analysis
(looking at this actor’s career path compared with others) is aligned to the
current entertainment news agenda.
‘The best piece of advice, which is “always be curious,” came from
Steve Duenes, who has led the graphics team at the New York Times for
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more than a decade. Being curious covers the essence of journalism:
question everything, never make assumptions, dig. You can’t make
great visualizations without great information, so make sure your
reporting leads you to visual stories that are interesting, surprising,
significant.’ Hannah Fairfield, Sr. Graphics Editor, The New York
Times
This would be an example of curiosity born out of anticipated intrigue.
The audience has not explicitly asked for this and does not necessarily
need it. However, it is perceived to be relevant in the context of the news
cycle and informed judgement has been used to anticipate there should be
sufficient interest among the target audience about this topic. Sometimes
you will work on projects where you have almost to imagine or assume
what appetite exists among an audience rather than just respond to an
expressed need.
Most of the projects I work on will be driven by stakeholders asking me to
create a visualisation to communicate understanding to others (not
necessarily them), as per the ‘audience’ and ‘anticipated’ intrigue
scenarios. The secondary role of the ‘filmographics’ project, that I defined
as emerging from and serving a personal intrigue, will also be to pique the
interest of other movie fans. Again, this is based on anticipated intrigue
more than known audience intrigue.
The final scenario of curiosity goes back to our role as an individual. Let’s
say I am interested in data visualisation and also interested in movies and I
discover a clean dataset full of rich content about movies and actors. This
sounds like a compelling opportunity to do something with it because I am
convinced there will be some nuggets of insight locked inside. I might not
have determined a specific curiosity yet, as my entry point, but I will be
able to establish this later once I have had a closer look at what potential
the data offers.
This would be a situation where curiosity is born out of potential intrigue
– potential because I just do not know explicitly what it will be yet.
Sometimes, in your subject of study or in the workplace, perhaps if you
work with collections of survey results or findings from an experiment,
you might find yourself with the opportunity to explore a dataset without
any real prior sense of exactly what it is you are looking to get out of it.
You are initially unclear about the precise angle of your enquiry but you
will explore the data to acquaint yourself fully with its qualities and
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generally research the subject. From there you should have a better idea of
a more specific curiosity you might pursue. In effect, this scenario would
then switch into more personal intrigue (if it remains just for yourself) or
anticipated intrigue (if you might share it with others).
This final scenario is the only one whereby the availability of and access to
data would arrive before you have articulated a specific curiosity. In all the
other scenarios outlined, the data you need will typically be sought as a
response to the curiosity. Even in the lattermost scenario about potential
intrigue, data itself does not just fall from the sky and into your lap(top).
The sheer fact that you have a dataset to work with will be because
somebody else, at an earlier moment in time, was interested in measuring
an activity, recording it, and making the collected data available. That in
itself could only have arisen from their own curiosity.
The potential intrigue type of curiosity might also extend to situations
where you simply have a desire to practise your visualisation skills,
experimenting and trying out new techniques with some sample data. In
this scenario the incentive is more to learn from a new experience of
working through a visualisation process and may not necessarily have
the same drivers as when definable audiences exist
Why do these different scenarios of curiosity have such an important role
to play? Firstly, they provide clarity about the angles of analysis that you
might be pursuing. As you will see later, even in the smallest and
seemingly simplest dataset, there are many possibilities for conducting
different types of analysis. The burden of choosing is somewhat eased by
knowing in advance what might be the most interesting and relevant
analysis to focus on. Secondly, the different scenarios described all present
slightly different characteristics in the dynamics of the people involved.
Who are the stakeholders and what is their interest? Who are the intended
recipients – the audience – and what is their interest? As you have already
seen – and will keep seeing – the involvement of people creates such
influential forces (good and bad) shaping your visualisation thinking. You
therefore need to know about how those forces might materialise from the
outset.
Identifying Your Project’s Circumstances
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Defining your project’s circumstances involves identifying all the
requirements and restrictions that are inherited by you, imposed on you or
determined by you. These are the different pressure points that establish
what you can or cannot pursue and what you should or should not pursue.
Much of this contextual thinking is therefore associated with the aim of
ambition management.
There are so many hidden variables and influences in a visualisation
project that the end viewer never gets to see and often does not appreciate.
It is natural for them to assess a project through the lens of an idealised
context free of restriction, but there are always limitations, external
influences and project-specific factors that affect the shape of the final
work.
When starting a project you will find that not all the circumstances that
could have an influence on your work will prove to be as identifiable,
definable or fixed as you might like. Some things change. Some things can
only be recognised once you’ve become a little more acquainted with the
nature of your task. As I stated in the previous chapter, doing this activity
now is only the first occasion you will be paying attention to these matters,
not the last. Of course, the more you can define, the greater the clarity
your subsequent decisions will be based upon. There are other stages
where you can work with uncertainty but, ideally, not here. Your work
needs to obtain as much focus as possible.
In order to design a tool, we must make our best efforts to understand
the larger social and physical context within which it is intended to
function.’ Bill Buxton, Computer Scientist, Designer and Author,
Sketching User Experiences
‘Context is key. You’ll hear that the most important quality of a
visualisation is graphical honesty, or storytelling value, or facilitation of
“insights”. The truth is, all of these things (and others) are the most
important quality, but in different times and places. There is no singular
function of visualisation; what’s important shifts with the constraints of
your audience, goals, tools, expertise, and data and time available.’
Scott Murray, Designer
There are some factors that may not be relevant or do not have any
predefined restrictions or set requirements. For example, you might not
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have any format restrictions (print vs digital, large size vs small size) to
contend with, in which case it is entirely up to you how it evolves.
Identifying that no format restrictions exist is as valuable as knowing when
they do. It gives you control. You might decide there is merit in imposing
a restriction yourself. You might appreciate some degree of focus by
determining that your target output will be for a printed, poster-sized
display.
People
Stakeholders: In project situations where you have been
requested/commissioned to do a visualisation by somebody else, it is
helpful to establish an understanding of all the different players and
their involvement. Defining stakeholders will help you anticipate
what sort of experience you are going to go through, how enjoyable
and smooth it might be, or how much friction and what obstacles
might be involved.
For starters, who is the ultimate customer? This might not be the
person who has directly commissioned you, nor might it be somebody
belonging to the intended audience, rather someone who has
influence over the final work. They may not be decision makers,
rather decision approvers. They are the people from whom you await
the thumbs up. Stakeholders will have an influence on when work is
of sufficient quality, in their eyes, to declare it as being on the right
path or, ultimately, to signal the completion of the project. In my
world, when you might be doing work as a contracted design
consultant, they determine when I will get paid. As you have seen,
stakeholders might also be the people from whom the origin curiosity
emerged, so they will be especially invested in what it is you are able
to produce.
Other stakeholders might have a smaller involvement or influence.
Their role may be a positive one – offering advice and assistance with
a specific domain challenge – or, in a minority of cases, a negative
one – hindering progress by influencing design decisions beyond their
remit and capability. In this case they become interferers. We don’t
like interferers because they make life unnecessarily harder
(especially, strangely enough, if they are nice people). A primary
contact person, who will act as the liaison between parties, will be
another important role to identify.
If there are no stakeholders and the project is a solo pursuit there will
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be much more flexibility for you to dictate matters. You might even
be more motivated to go ‘above and beyond’ if you are driven by a
personal intrigue. Conversely there will be fewer channels of
guidance and support. This is not to say that one situation is better, it
just means they are different and this difference needs to be
recognised early.
Audience: What are the characteristics of your viewers? Several
different attributes were defined in discussing the principle for
‘accessible design’ in Chapter 1. You are primarily trying to
understand their relationship with the subject matter. How informed
are they about a subject and what motivation might they have towards
it – is it a passing interest or a definable need? What capacity might
they have to make sense of the type of visualisations you may need to
create (their graphical literacy)? How could their personal traits
influence your design choices? You will never nor should you ever let
the spinning plate of concern about your audience drop.
Constraints
Pressures: The primary pressure relates to timescales: how much
time have you got to work through the full process and publish a
completed solution? The difference in potential ambition between a
project that is needed in two days compared with one that is needed in
two months is clear. However, the real issue is the relationship
between timescales and the estimated duration of your work. Two
months might sound great but not if you have three months’ work to
accomplish. Estimating project duration to any reliable degree is a
difficult task. You need experience from working on a diverse range
of projects that can inform your expectations for how long each
constituent task could take. Even then, seemingly similar projects can
end up with very different task durations as a result of the slightest
changes in certain circumstances, such as the inclusion of an extra
variable of data, or more significant changes like a previously print-
only project requiring a bespoke digital interactive solution as well.
In addition to project timescales, you will need to be aware of any
other milestones that might have to be met. Work that you are
producing for other stakeholders will often require you to present
your ideas/progress at various stages. This is a good thing. It gives
you the opportunity to check if you are in sync or discover if you
have misunderstood certain needs. Note that it can be risky to present
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under-developed concepts to potentially inexperienced stakeholders
who may not be able to extend their imagination to envision how the
work will look when completed.
Other pressures may exist in tangible terms through financial
restrictions. What time can you afford to spend? This is not just
associated with freelancing or studio work, it can be the same for
research groups which have finite resources and need to use their time
– and their costs – sensibly. It might also have an influence on
occasions where you need to outsource parts of your work (e.g.
paying for transcription services, third-party data sources) or make
purchases (software, hardware, licences for photograph usage).
‘What is the LEAST this can be? What is the minimum result that will
1) be factually accurate, 2) present the core concepts of this story in a
way that a general audience will understand, and 3) be readable on a
variety of screen sizes (desktop, mobile, etc.)? And then I judge what
else can be done based on the time I have. Certainly, when we’re down
to the wire it’s no time to introduce complex new features that require
lots of testing and could potentially break other, working features.’
Alyson Hurt, News Graphics Editor, NPR, on dealing with
timescale pressures
Always note down your task durations so you can refine your estimates
far better on future projects. These estimates are not just valuable for
client work, you will need them to manage your own time regardless of
the nature of the project.
The final pressure is slightly less tangible but comes in the form of
what might be described as market influences. Sometimes you will
find your work is competing for attention alongside other work. In
this age of plenty, a desire to emulate the best or differentiate from
the rest can prove to be a strong motive. For example, if you are
working for a charitable organisation, how do you get your message
across louder and more prominently than others? If you are working
on an academic research project, how do you get your findings heard
among all the other studies also looking to create an impact? It might
be the internal dynamics within a student group or organisation or the
broader competition across entire marketplace and industries, but
regardless, considerations like this do introduce an extra ingredient to
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shape your thinking.
Rules: These are relatively straightforward matters to define and are
concerned with any design rules you need to know about and follow.
These might be issues around:
Layout/size restrictions: Maximum size and specific shape
restrictions might exist with graphics created for articles
published in journals or the screen size dimensions for digital
outputs that need to work on a tablet/smartphone. Are there
printing resolution requirements around dpi (dots per inch)? The
commonly used industry standard for printing is 300 dpi.
Style guidelines: In many organisations (and with some media)
there are often visual identity branding guides imposed on you
that determine the colours, typeface and possibly logos that you
need to include. If possible, try to push back on this because they
can be unnecessarily restrictive and often the choices imposed
are horribly ill-suited to data visualisation. Otherwise, you will
have to abide by the style requirements dictated to you. Also
check to see if you will need to include any logos. They may
take up valuable space and you’ll need to think about their
impact on the balance of your overall colour palette and
composition.
Functional restrictions: The potential requirement to create
outputs that are compatible with certain browsers, versions of
software or programming languages will be an important
consideration to establish early.
Consumption
Frequency: The issue of frequency concerns how often a particular
project will be repeated and what its lifespan will be. It might be a
regular (e.g. monthly report) or irregular (e.g. election polling graphic
updated after each new release) product, in which case the efficiency
and reproducibility of your data and design choices will be
paramount. If it is a one-off, you will have freedom from this concern
but you will have to weigh up the cost–benefit involved. Will there be
any future benefits from reusing the techniques and thinking you put
into this project? Can you afford to invest time and energy, for
example, in programmatically automating certain parts of the creation
process or will this be ultimately wasted if it is never reused? What is
the trade-off between the amount of work to create it and the expiry
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of its relevance as time goes by – will it very quickly become out of
date as new data ‘happens’? Maybe it is a one-off project in creation
terms but is to be constantly fed by real-time data updates, in which
case the primary concern will be of functional robustness.
Setting: This concerns the situation in which your work would be
consumed. Firstly, this is judging whether the work is going to be
consumed remotely or presented in person (in which case the key
insights and explanations can be verbalised). Secondly, is the nature
of the engagement one that needs to facilitate especially rapid
understanding or does it lend itself to a more extended/prolonged
engagement?
‘I like to imagine that I have a person sitting in front of me, and I need
to explain something interesting or important about this data to them,
and I’ve only got about 10 seconds to do it. What can I say, or show
them, that will keep them from standing up and walking away?’ Bill
Rapp, Data Visualisation Designer, discussing an audience scenario
setting he conceives in his mind’s eye
I keep four characteristic settings in mind when thinking about the
situations in which my work will be consumed by viewers:
The boardroom: A setting characterised by there being limited
time, patience or tolerance for what might be perceived as any
delay in facilitating understanding: immediate insights required,
key messages at a glance.
The coffee shop: A more relaxed setting that might be
compatible with a piece of work that is more involving and
requiring of viewers to spend more time learning about the
subject, familiarising themselves with how to read the display
and discovering the (likely) many different parts of the content.
The cockpit: The situation that relates to the instrumentation
nature of a visualisation tool or dashboard. There is a need for
immediate signals to stand out at a glance whilst also offering
sufficient breadth and depth to serve the likely multitude of
different potential interests. Another example might be the usage
of a reference map that works on all levels of enquiry, from at a
glance, high-level orientation through to in-depth detail to aid
the operational needs of navigation.
The prop: Here a visualisation plays the role of a supporting
visual device to accompany a presenter’s verbal facilitation of
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the key understandings (via a talk) or an author’s written account
of salient findings (report, article).
Deliverables
Quantity: This concerns establishing the project’s workload prospect
in terms of quantities. How many things am I making? How much,
what type, what shape and what size? Is it going to involve a broad
array of different angles of analysis or a much narrower and focused
view of the data? What are the basic quantities of the outputs? Is it,
for example, going to be about producing 12 different graphics for a
varied slide deck or a 50-page report that will need two charts for
each of the 20 questions in a survey and some further summaries?
Perhaps its a web-based project with four distinct sections, each
requiring four interactively adjustable views of data. It will not
always be possible to determine such dimensions this early on in the
process, but even by just establishing a rough estimate this can be
helpful, especially for informing your estimate of the project’s likely
duration.
Format: This concerns the output format: digital, print or physical.
You will need a clear understanding of the specific format of the
deliverables required to factor in how your design work will be
affected:
Is it intended as a large poster-sized print or something for a
standard A4-sized report?
Will it exist as a website, a video, maybe even a tool or app?
Is the digital output intended for smartphone, tablet (which
ones?) as well as desktop? What ppi (pixels per inch) or
resolution will it ideally need to work with?
Are you handing over to your stakeholder just the final design
work or will you also be expected to provide all the background
files that contributed to the final piece of work?
‘I love, love, love print. I feel there is something so special about having
the texture and weight of paper be the canvas of the visualisation. It’s a
privilege to be able to design for print these days, so take advantage of
the strengths that paper offers – mainly, resolution and texture. Print has
a lot more real estate than screen, allowing for very dense, information
packed visualisations. I love to take this opportunity to build in multiple
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story strands, and let the reader explore on their own. The texture of
paper can also play a role in enhancing the visualisation; consider how a
design and colour choices might be different on a glossy magazine page
versus the rougher surface of a newspaper.’ Jane Pong, Data
Visualisation Designer, loves print (I think)
Resources
Skills: What capabilities exist among those who will have a role to
play in the design process? This might just be you, in which case
what can you do and what can’t you do? What are you good at and
not good at? If you have collaborators, what are the blend of
competencies you collectively bring to the table? How might you
allocate different roles and duties to optimise the use of your
resources? To help assess your capabilities, and possibly those across
any team you are part of, consider the breakdown presented in
Chapter 11 in the ‘Seven hats’ section.
Technology: As I have described already, there are myriad tools,
applications and programming options in the data visualisation space,
offering an array of different capabilities. No single package offers
everything you will ever need but, inevitably, some offer more and
others less. In order to complete the more advanced visualisation
projects you will likely require a Swiss-Army-knife approach
involving a repertoire of different technology options at each of the
preparatory and development stages in this process. The software and
technological infrastructure you have access to will have a great
influence on framing the ambitions of your work. I will be sharing
more information about tools in the digital resources that accompany
this book.
‘The thing is, this world, especially the digital data visualization world,
is changing rapidly: new technologies, new tools and frameworks are
being developed constantly. So, you need to be able to adapt. But
principles are much more timeless. If you know what you want to
create, then using technology is just the means to create what you have
in mind. If you’re too fixed on one type of technology, you may be out
of a job soon. So, keep learning new technologies, but more
importantly, know your principles, as they will allow you to make the
right decisions.’ Jan Willem Tulp, Data Experience Designer
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A final point to make about circumstances is to recognise the value, in
many cases, of limitations and constraint. Often such restrictions can
prove to be a positive influence. Consider the circumstances faced by
Director Steven Spielberg while filming Jaws. The early attempts to
create a convincing-looking shark model proved to be so flawed that for
much of the film’s scheduled production Spielberg was left without a
visible shark to work with. Such were the diminishing time resources
that he could not afford to wait for a solution to film the action
sequences so he had to work with a combination of props and visual
devices. Objects being disrupted, like floating barrels or buoys and,
famously, a mock shark fin piercing the surface, were just some of the
tactics he used to create the suggestion of a shark rather than actually
show a shark. Eventually, a viable shark model was developed to serve
the latter scenes but, as we all now know, in not being able to show the
shark for most of the film, the suspense was immeasurably heightened.
This made it one of the most enduring films of its generation. The
necessary innovation that emerged from the limited resources and
increasing pressure led to a solution that surely transcended any other
outcome had there been freedom from restrictions. The key message
here is to embrace the constraints you face because they can heighten
your creative senses and lead to successful, innovative solutions.
Defining Your Project’s Purpose
Identifying the curiosity that motivates your work establishes the project’s
origin. The circumstances you have just considered will give you a sense
of the different factors that will influence your experience on the project
and shape your ambitions. The final component of contextual thinking is to
consider your intended destination. What is it you specifically hope to
accomplish with your visualisation? This involves articulating your
project’s purpose.
You know now that the overriding goal is to facilitate understanding, that
is non-negotiable, but the nature of this understanding may vary
significantly. In Chapter 1, I described how – as viewers – we go through a
process of understanding involving the stages of perceiving, interpreting
and, finally, comprehending. The undertaking of the first stage of
perceiving is largely controlled by the accessibility of the visualiser’s
design choices. The second stage of interpreting (establishing meaning
from a visualisation) will be influenced by the viewer’s capacity to derive
meaning or by the visualiser providing explanatory assistance to help the
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viewer form this meaning. The final stage of comprehending is largely
determined by the viewer alone as what something means to them is so
uniquely shaped by their personal context: what they know or do not
know, what their beliefs are and what their intentions are for acquiring this
understanding.
This three-stage model of understanding helps demonstrate the importance
of defining the purpose of a visualisation upfront. Some visualisations
might aim to be quite impactive, attempting to shock or inspire viewers in
order to persuade them about a need to change behaviour or make
significant decisions. For example. you might be seeking to demonstrate
visually compelling evidence of the impact of dietary factors like sugary
drinks on the rise of obesity. The purpose might not just be to inform but
actively to seek to make a difference, maybe targeting parents to change
the foods they allow their kids to eat. To achieve this kind of outcome you
might take a more emotive approach in the portrayal of your data to attract
the audience’s attention in the first place and then strike home the
powerful message in a way that resonates more deeply. Affecting people to
this degree can be quite ambitious.
In a different context, you might not need to go this deep. Some projects
may be more modestly designed to enlighten or simply inform viewers
better about a subject, even if the acquired understanding is quite small.
There might be recognition that the target viewers should (and maybe are
better placed to) reach their own conclusions. Perhaps, if you were
revealing the same type of dietary data to health professionals rather than
to parents, you might only be serving to confirm what they already might
know or at least suspect. They probably will not need convincing about the
importance of the message, so the ambitions of the visualisation itself will
be considerably different. To achieve the purpose of this project would
likely lead to a very different design approach from the one in the previous
scenario.
One size does not fit all. No single type of visualisation will be capable of
delivering an experience whereby all flavours of understanding are
facilitated. Articulating your purpose is your statement of intent: a
necessary sense of focus to help inform your design choices and a potential
measure to determine whether you accomplish your aims.
Defining your purpose before establishing your trigger curiosity is
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putting the cart before the horse. A project driven by curiosity is the
purest basis for a visualisation project to commence and one most likely
to be guided by the clearest thinking. It is the approach that fits best
with the sequence of thinking outlined in this workflow. When the
desired purpose drives decisions, visualisers can be overly focused on
outputs and not inputs. As discussed in Chapter 4, you need to let your
data do the talking, not force the data to do your talking. The pressure to
reach the desired destination can impact artificially on the data, editorial
and design decisions you make.
If you are working with colleagues or for clients who express their
requirements from the perspective of an outcome- or purpose-led
process, your skill as a visualiser will be to direct the discussions
towards a more curiosity-led perspective. Sometimes you will find
stakeholders who are primarily motivated by a desire to reach many
viewers and their singular measure of success is purely the quantity of
eyeballs that will peruse a piece of work. However, I would contest that
this does not make it a viable motive for an effective data visualisation,
where the measure of success is about facilitating understanding first
and foremost. Loads of visitors and social media hits (likes, retweets,
upvotes) are a wonderful bonus but should only be seen as a by-product
of interest, not an indicator of effectiveness in and of itself. Those who
seek a viral success story rarely achieve it because it is so hard to
manufacture.
3.3 Establishing Your Project’s Vision
The ‘Purpose’ Map
In compiling definitions about the curiosity, circumstances and purpose,
you have helped to initiate your process with a clear idea of the origin of
your work, its likely desired destination, and some of the most influencing
factors you will have to contend with along the way.
To supplement this contextual thinking you should take the opportunity to
consider forming an initial vision for your work. The definition of vision is
‘the ability to think about or plan the future with imagination or wisdom’
and it has particular relevance for how we might foresee achieving the
purpose we have stated.
There are many types of visualisation with many different characteristics.
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Two of the most significant concern the differences in tone and
experience. Reflecting the diversity of visualisation work being produced,
the ‘purpose map’ (Figure 3.1) offers a high-level view of this landscape
shaped by different relationships across those two dimensions.
Figure 3.1 The ‘Purpose Map’
Certain types of visualisation will offer a better fit for your project. Their
characteristics, in terms of experience and tone, will offer the right blend
to best connect your origin curiosity with destination purpose. What you
need to consider here is what can you envision being the most suitable
type of visualisation that might be most capable of accomplishing what
you intend.
While the more detailed design thinking won’t arrive until later in the
workflow, even at this early stage it is instructive to put some thought into
this matter. Let me explain sequentially the meaning of each of these
dimensions and the significance of the different regions within this
purpose map.
Experience
The horizontal dimension of this map concerns the experience of the
visualisation:
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How will it practically operate as a means of communication?
Through what functional experience will understanding be achieved
by the viewer?
Along this spectrum are three different states against which you may
define your intentions: Explanatory, Exhibitory or Exploratory (for a
mnemonic, think about this as being all about the EXs).
Explanatory visualisations are found on the left side of the map.
Explanatory in this context essentially means we – as visualisers –
will provide the viewer with a visual portrayal of the subject’s data
and will also take some responsibility to bring key insights to the
surface, rather than leave the prospect of interpreting the meaning of
the information entirely to the viewer. The visualiser here is
attempting to assist with the viewers’ process of understanding as
much as possible, in particular with the interpretation, drawing out the
meaning of the data.
The rightmost side of this explanatory region of the map (in the
second column, more towards the middle of the map) might be
considered the ‘mildest’ form of explanatory visualisation. Here you
find projects that include simple annotation devices like value labels
or visual guides that direct the eye to help assist with the task of
interpreting the data: the use of colour can be an immediate visual cue
to help separate different features of a chart and captions might
outline a key message or summary finding. An example of this kind
of explanatory visualisation is seen in Figure 3.2, which was
published in an article reporting on protests across US schools (in
November 2015) regarding the underrepresentation of black students.
Here you can see a scatter plot comparing the share of enrolled black
students for different public research universities (in the vertical axis)
with the share of the college-age black populations in the respective
states. With protests beginning at the University of Missouri, the chart
uses red to highlight this data point within the chart to enable
comparison with other schools. Other notable schools are emphasised
to draw out some of the main insights. Additionally, using encoded
overlays, such as the trend line and dotted-line indicating proportional
representation, the viewer is assisted beyond just perceiving the data
to help them with the stage of interpretation: what does it mean to be
higher or lower on this chart? Which locations are considered
good/bad or typical/atypical?
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Figure 3.2 Mizzou’s Racial Gap Is Typical On College Campuses
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The best way to get your head around ‘explanatory’ visualisations is
to consider how you would explain this display of analysis to a
viewer if you were sat with that person in front of a screen or with a
printout. What features would you be pointing out to them? Which
values would you be pointing to as being the most interesting? What
things would you not need to explain? The traits of a good
explanatory visualisation will accommodate these types of important
descriptions, which would otherwise be verbalised, within the design
of the chart itself, making it ‘stand’ alone without the need for in-
person explanation.
Towards the leftmost region of the map this is where the experience is
about generally more intensive attempts to enlighten an audience’s
understanding of a subject. This could possibly be through the use of
a narrative structured around a compelling sequence of information
and/or a dramatic experience. The form of this type of work would be
characterised by videos or presentations, or maybe an animated or
motion graphic. Some term this ‘narrative’ visualisation. This is
arguably where the most tangible demonstrations of visualisation
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through storytelling (more on this later) are found. An example that
typifies this classification on the map would be characterised by this
very powerful and popular video (Figure 3.3) about the issue of
wealth inequality in the USA. It employs a semi-animated slideshow
sequence to weave together the narrative and is accompanied by an
effective and affective voiceover narrating the story.
Figure 3.3 Image taken from ‘Wealth Inequality in America’
Across all explanatory visualisations the visualiser will require
sufficient knowledge (or the skill and capacity to acquire this) about
the topic being shown in order to identify the most relevant,
interesting and worthwhile insights to present to the viewer. Creating
explanatory visualisations forces you to challenge how well you
actually know a subject. If you cannot explain or articulate what is
insightful, and why, to others, then this probably means you do not
know the reasons yourself.
Fundamentally, explanatory visualisations are the best-fit solution if
the specific context dictates that saying nothing is not good enough;
leaving viewers with a ‘so what?’ reaction would be seen as a failure,
so in such cases a takeaway message(s) would need to be offered.
Exploratory visualisations differ from explanatory visualisations in
that they are focused more on helping the viewer or – more
specifically in this case – the user find their own insights. Almost
universally, these types of works will be digital and interactive in
nature. The ‘mildest’ forms of exploratory works are those that
facilitate interrogation and manipulation of the data. You might be
able to modify a view of the chart, perhaps by highlighting/filtering
certain categories of interest, or maybe change data parameters and
switch between different views. You might be able to hover over
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different features to reveal detailed annotations. All of these
operations facilitate understanding to the extent of the perceiving
stage. The task of interpreting and comprehending will largely be the
responsibility of the viewer to form. This will be suitable if the
intended audience have the necessary foundation knowledge for the
subject and sufficient interest to translate the general and personal
meaning.
An example of this type of visualisation can be seen through the
interactive project (Figure 3.4). It was developed to allow users to
explore different measures concerning the dimension changes of
wood, over time, across selected cities of the world.
Figure 3.4 Dimensional Changes in Wood
There are no captions, no indications of what is significant or
insignificant, no assistance to form meaning through the use of
colours or markers to emphasise ‘good’ or ‘bad’ values. This project
is simply a visual window into the analysis of this data that lets users
perceive the data values and interact with the different dimensions
offered. To form meaning, it is open to them to determine what
features of the data resonate with their existing interests, knowledge
and needs.
As you look more towards the rightmost edge of the purpose map you
reach far deeper exploratory experiences. You might characterise
visualisations here as facilitating a more participatory or contributory
experience. The prospect of greater control, a deeper array of features
and the possibility of contributing one’s own data to a visualisation
can be very seductive. Users are naturally drawn to challenges like
quizzes and projects that allow them to make sense of their place in
the world (e.g. how does my salary compare with others; how well do
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I know the area where I live?) – they are simply too hard to resist!
The huge success of the New York Times’ so-called ‘Dialect map’
(Figure 3.5), showing the similarity or otherwise of US dialects based
on users’ responses to 25 questions, is just one example of a
contemporary project employing this participatory approach to great
effect.
Figure 3.5 How Y’all, Youse and You Guys Talk
The biggest obstacle to the success of an exploratory visualisation’s
impact is the ‘so what?’ factor. ‘What do you want me to do with this
project? Why is it relevant? What am I supposed to get out of this?’ If
these are the reactions you are seeing expressed by your intended
users then there is a clear disconnect between the intentions of your
project and the experience (or maybe expectations) of the audiences
using it.
Exhibitory visualisations are found in the final separate ‘experience’
category within the latitude of the purpose map. They are
characterised by being neither explicitly explanatory nor exploratory.
With exhibitory visualisations the viewers have to do the work to
interpret meaning, relying on their own capacity to make sense of the
display of data (to perceive it) and the context of the subject-matter.
As well as lacking explanatory qualities, they also do not offer scope
for exploratory interrogation. I generally describe them as simply
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being visual displays of data. Think of this term in relation to
exhibiting an artwork: it takes the interpretative capacity of the
viewer to be able to understand the content of a display as well as the
context of a display.
When you look across the many different visualisations being
published you will find that many projects mistakenly fall into the
void of being exhibitory visualisation when they really need to be
more supportively explanatory or functionally exploratory.
So you might wonder what the value is of an exhibitory visualisation.
Well, sometimes the setting for a visualisation does not need
exploration or direct explanation. As I’ve stated, exhibitory projects
rely entirely on and make assumptions about the capacity of and
interest among the target audience. If you have a very specific
audience whom you know to be sufficiently knowledgeable about the
domain and the analysis you have provided, it might not need
important insights to be surfaced in the way you would with an
explanatory visualisation. An explanatory project will mainly be for
audiences who do not have the knowledge, capacity or time to find
for themselves the key features of meaning (through interpreting)
alone. Furthermore, the extent of the analysis might be so narrow that
there is no fundamental need to incorporate ways of manipulating and
personalising the experience as you would see with exploratory
visualisations.
Figure 3.6 Spotlight on Profitability
In Figure 3.6, the analysis of the top three profitable movies by genre
and year is not interactive (and so does not enable any explorations),
nor does it bring to the surface any observations about notable movies
or conclusions about the relationship between movie ratings and
takings. It is intended as an exhibitory experience – a visual display
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of this data – that lets you as a user draw your own conclusions, find
your own shapes of interest, and look up the movies that you want to
see data for.
An exhibitory visualisation might also be a graphic that supports a
written article or report. In and of itself it does not explain things in a
stand-alone sense but instead exists as a visual prop for referencing.
The written passages will therefore provide the explanatory narrative
separate but still drawn from the supporting graphic.
I mentioned earlier the scenario of sitting down with someone and
explaining a chart to them from a printout or a screen. As I said, the
key points verbalised in this setting would, for an explanatory piece,
be directly incorporated within the graphic. Conversely, I might use
an exhibitory visualisation in a presentation where my narrative,
observations and gestures provide the explanatory experience – I
perform these myself, in person – rather than these being incorporated
within or around the chart(s). This would define the visualisation as
exhibitory but presented in an explanatory setting. Two of the most
famous visualisation-based presentations, Al Gore’s presentation in
An Inconvenient Truth and Hans Rosling’s ‘Gapminder TEDtalk’, are
excellent demonstrations of this.
One could argue that the Rosling talk was an explanatory presentation
of an exploratory tool, but some of the main narrative was delivered
against a more exhibitory animation of data.
Tone
The vertical dimension of the purpose map concerns the intended tone of
the visualisation, with reading tone positioned towards the top and feeling
tone towards the bottom. Whereas the experience dimension had two
distinct and opposite sides (Explanatory vs Exploratory) with a pivot in the
middle (Exhibitory), the tone dimension is much more of a continuum with
subtle – and very subjective – variations between the two ends. What you
are largely considering here is a judgement of the most suitable perceptual
readability of your data.
Whereas the difference between types of experience can be quite distinct
once you become familiar with the characteristics of each, defining tone is
a slightly harder matter to nail down, especially as a beginner. The general
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question you are asking yourself is: through what tone of voice in my
design will the purpose of this project be accomplished? Let me elaborate
by looking closely at the two ends of this continuum.
Reading tone: At the top of the purpose map the tone of your
visualisation design choices will be geared towards optimising the
ease with which viewers can accurately estimate the magnitude of and
relationships between values. There is emphasis on the efficiency of
perceiving data. The reading tone would be your best-fit approach
when the purpose of your work requires you to facilitate
understanding with a high degree of precision and detail. This would
also be relevant in situations when there is no need to seduce an
audience through your aesthetic treatment. Furthermore, it suits the
needs well when the subject matter does not inherently embody or
merit any form of visual stimulation to convey the essence of the
message more potently. The visual quality created with this tone
might be considered rather utilitarian, formed around a style that feels
consistent with adjectives like analytical, pragmatic, maybe even no-
frills.
Devices like bar charts, as you can see in Figure 3.7, are the poster
boys for this type of display. As you will learn later, the perceptual
accuracy enabled by using the size of a bar to represent quantitative
values makes these charts extremely effective options for visually
portraying data in a way that aids both general sense-making and
accurate point-reading. That’s why they are so ubiquitous.
Most of the visualisations you will ever produce will lean towards
this reading end of the tonal continuum. Indeed, you might ask why
would you ever seek to create anything but the most easily and
accurately readable representations of data? Surely anything that
compromises on this aim is undermining the principles of trustworthy
and accessible design? Well, that’s why the definitions around
purpose are so significant in their influence and why we need to
appreciate other perspectives.
Figure 3.7 Countries with the Most Land Neighbours
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‘There’s a strand of the data viz world that argues everything could be a
bar chart. That’s possibly true but also possibly a world without joy.’
Amanda Cox, Editor, The Upshot
Feeling tone: The lower end of this vertical dimension offers a
contrasting tone of voice to that of reading. When I introduced in
Chapter 1 the sequence of understanding – from perceiving to
interpreting and through to comprehending – the illustration I gave
was based on perceiving a bar chart. Here you could easily and
confidently estimate the values portrayed by the bar sizes.
Sometimes, though, your aims will not necessarily fit with the
singular desire to provide such a perceptually precise display:
sometimes you might justify placing greater importance on the feeling
of your data. At this side of the tonal spectrum there is more emphasis
placed on determining the gist of the big, medium and small values
and a general sense of the relationships that exist. Sometimes an ‘at-a-
glance’, high-level view is the most suitable way to portray a
subject’s values.
Again, let me address the likely objections from those spitting their
coffee at the very thought of any visualiser not giving the utmost
priority to precision, efficiency and accuracy in their work. To
appreciate why, on occasion, you might consider a different approach
it is worth reflecting again on the motive for visualising data. Visual
forms of data unquestionably offer a more revealing and more
efficient way to understand the quantities and relationships that exist
within data. It cannot be reasonably achieved either effectively or
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efficiently through non-visual forms. By visualising data you are
looking for something more and something different from what, let’s
say, a table of data can offer. The bar chart, by way of example, offers
that. However, on occasion you might need something even more
different than this.
In the project illustrated in Figure 3.8, you will see excerpts from an
analysis about the small number of families who have most financial
clout when it comes to providing funding for presidential candidates.
The data quantities are portrayed using Monopoly house pieces as a
metaphor of wealth. The red houses represent the small number of
families who have contributed nearly half of the initial campaign
funding. The green pieces are representative of the total households in
the US. You cannot count the pieces, you cannot even remotely
estimate their relative proportions, but you get the gist of the scales
involved as a proxy illustration of the remarkably disproportionate
balance and power of wealth. Furthermore, this use of the Monopoly
pieces is a symbolically strong metaphor as well as offering an
appealing, almost playful approach to portraying the data.
Figure 3.8 Buying Power: The Families Funding the 2016 Presidential
Election
There will be times when you will need to consider employing what might
be described as aesthetic seduction: some way of creating an appealing
form that attracts viewers and encourages them to engage with a subject
they might not have otherwise found relevant. This could involve a novel
visual ‘look’ that attracts – but also informs – or a functional feature that
attracts – but also performs. The influence of fun cannot be underestimated
here. I repeat, we are all humans with occasionally quite base needs.
Sometimes viewers crave something that stirs a more upbeat and upfront
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emotional engagement.
Some may argue that viewers will be encouraged to engage with a
visualisation if it is relevant to them, regardless of its appearance,
otherwise they should not be considered part of the target audience. That is
not true, unfortunately. Perhaps in a business or operational setting, the
needs of individuals, roles and groups are much more clear-cut and you
can apply a binary perspective like that quite easily. Outside in the real
world there are many more nuances. As a viewer your interest in a subject
may not materialise until after viewers have engaged with a visualisation.
It may be a consequence not the prerequisite. Had they not been somehow
attracted to view it in the first place they might never have reached that
point.
‘I love the idea of Edward Tufte’s assertion that “Graphical excellence
is that which gives to the viewer the greatest number of ideas in the
shortest time with the least ink in the smallest space.” But I found that
when I developed magazine graphics according to that philosophy, they
were most often met with a yawn. The reality is that Scientific
American isn’t required reading. We need to engage readers, as well as
inform them. I try to do that in an elegant, and refined, and smart
manner. To that end, I avoid illustrative details that distort the core
concept. But I’m happy to include them if the topic could benefit from a
welcoming gesture.’ Jen Christiansen, Graphics Editor at Scientific
American
‘On the one hand we had this great idea of doing something fun –
animated lifts racing up and down buildings while the user was on the
web page. But on the other hand this is The Financial Times and that
carries with it a responsibility to do things in a certain way. So we spent
time illustrating and designing to give the graphic high production
values, and it was then presented alongside an excellent piece of
journalism from our manufacturing correspondent. The result? An
undeniably fun user experience, but delivered in such a way that met FT
subscribers’ standards for high quality visuals and high quality
journalism.’ John Burn-Murdoch, Senior Data Visualisation
Journalist at Financial Times, having fun visualising the speed of
elevators in skyscrapers
On a similar note, sometimes you will be working with a subject – like
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wealth inequality, as we’ve just seen, or gun crime, as discussed earlier –
that has the potential to stir strong emotions. Any visualisation of this data
has to contend with decisions about how to handle the perpetual baggage
of feeling that comes as standard. Depending on the purpose of your work
there might be good reason to encapsulate and perhaps exploit these
emotions through your visualisation in a way that arguably a bar chart
simply may not be able to achieve. By embodying an emotional sensation
(fear, shock, fun, power, inequity) through your display, you might be able
to influence how your viewer experiences that most elusive stage of
understanding, comprehending. The task of reasoning ‘what does this
mean to me?’ is often a somewhat intangible notion but far less so when an
emotional chord is struck. Behaviours can be changed. The making of
decisions can be stirred. The taking of actions can be expedited. So long as
the audience’s needs, interest and setting are aligned this can be an entirely
suitable strategy.
For some in the visualisation field, this can be seen as manipulation and, to
a certain degree it probably is. As long as you are still faithful to the
underlying data and you have not achieved an outcome through
superficial, artificial or deceptive means, I believe it is an entirely
appropriate motive in the right circumstances. As ever, there is a balance
to be struck and you must remind yourself of the influence of the design
principles I introduced earlier to ensure that none of the choices you make
hinder the overall goal of facilitating the type of understanding your
context decrees.
It is important to note that any visualisation work that leans more towards
‘feeling’ is typically the exception and in a minority. However, a skilled
visualisation practitioner needs to have an adaptive view. They need to be
able to recognise and respond to those occasions when the purpose does
support an exceptional approach and a compromise beyond just serving the
most perpetually accurate and efficient reading of data is required.
The Purpose Map in Practice
A simple illustration of the role of the purpose map involves momentarily
focusing on a rather grave subject: data about offender executions. In
2013, the State of Texas reached the unenviable milestone of having
executed its 500th death-row prisoner since the resumption of capital
punishment in 1982. At the time of this landmark I came across a dataset
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curated by the Texas Department of Criminal Justice and published on its
website. This simply structured table of data (Figure 3.9) included striking
information about the offenders, their offences and their final statements –
a genuinely compelling source of data. Thinking about this subject and the
dataset helps to frame the essence of what role this purpose map can play,
especially in the tone dimension.
Since the milestone of 500 executions in 2013 the number has grown
significantly. For the purpose of this illustration, for now we will
consider the nature of this data as it was at the moment of this
milestone.
Figure 3.9 Image taken from Texas Department of Criminal Justice
Website
Imagine viewing this data from a high vantage point, like in a hot-air
balloon. The big picture is that there are 500 prisoners who have been
executed. That is the whole. Lowering the viewpoint, as you get a little
closer, you might see a breakdown of race, showing 225 offenders were
white, 187 black, 86 Hispanic and 2 defined as other. Lower still and you
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see that 4 offenders originated from Anderson County. Lower again
reveals that 112 offenders referred to God in their last statement. Down to
the lowest level – the closest vantage point – you see individuals and
individual items of data, such as Charles Milton, convicted in Tarrant
county, who was aged 34 when executed on 25 June 1985.
The view of the data has travelled from a figurative perspective to a non-
figurative one. The former is an abstraction of the data that effectively
supresses the underlying phenomena being about people and translates –
and maybe reduces it – into statistical quantities. People into numbers. The
latter perspective concerns a more literal and realistic expression of what
the data actually represents.
‘Data is a simplification – an abstraction – of the real world. So when
you visualize data, you visualize an abstraction of the world.’ Dr
Nathan Yau, Statistican and Author of Data Points
Going back to the discussion about judging tone, there are several different
potential ways of portraying this executed offenders data depending on the
purpose that has been defined.
Suppose you worked at the Texas Department of Criminal Justice as a
member of staff responsible for conducting and reporting data analysis.
You might be asked to analyse the resource implications of all offenders
currently on death row, looking at issues around their cost ‘footprint’. In
this case you might seek to strip away all the emotive qualities of the data
and focus only on its statistical attributes. You would likely aim for a
figurative or abstracted representation of the subject, reducing it to
fundamental statistical quantities and high-level relationships. Your
approach to achieve this would probably fit with the upper end of the tonal
dimension, portraying your work with a utilitarian style that facilitates an
efficient and precise reading of the data.
A different scenario may now involve your doing some visual work for a
campaign group with a pro-capital-punishment stance. The approach might
be to demonise the individuals, putting a human face to the offenders and
their offences. The motive is to evoke sensation, shock and anger to get
people to support this cause. Would a bar chart breakdown of the key
statistics accomplish this in tone? Possibly not.
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Another situation could see you working for a newspaper that had a
particularly liberal viewpoint and was looking to publish a graphic to mark
this sober milestone of 500 executions. You might avoid using the stern
imagery of the offenders’ mug shots and instead focus on some of the
human sentiments expressed in their last statements or on case studies of
some of the extremely young offenders for whom life was perhaps never
going to follow a positive path. To humanise or demonise the individuals
involved in this dataset is possible because there is such richness and
intimate levels of detail available from the data.
‘I have this fear that we aren’t feeling enough.’ Chris Jordan, Visual
Artist and Cultural Activist
It is worth reinforcing again that a figurative approach (reading) is
typically what most of your work will involve and require. Only a small
proportion will require a non-figurative (feeling) approach even with
emotive subjects. The whole point about introducing you to the alternative
perspective of the feeling tone is to prepare you for those occasions when
the desired purpose of your work requires more of a higher-level grasp of
data values or a deeper connection with subject matter through its data.
To complete this discussion, here are some final points to make about the
purpose map to further clarify and frame its scope.
Format: Firstly, it is important to stress that this map does not define
format in terms of print, digital or physical. Exploratory visualisations
will almost entirely be digital but exhibitory or explanatory projects
could be print or digital.
First thoughts not final commitment: Considering the definitions of
experience and tone now simply represents the beginning of this kind
of design thinking. As the workflow progresses you might change (or
need to change) your mind and pursue an alternative course,
especially when you get deeper into data work, the nature of which
may reveal a better fit with a completely different type of solution. I
will state again that in these early stages the things you will think
about will be the first occasion on which you think about them but not
the last. The benefit of starting this kind of thinking now is the
increased focus it affords from any sense of eliminating potential
types of visualisation from your concern that will have no relevance
to your context.
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Collective visual quality: Decisions around tone may not be solely
isolated to how data should be represented. There may be a broader
sense of overall visual mood or ‘quality’ that you are trying to convey
across the presentation design choices as well. As you will see, there
are other media assets (photos, videos, illustrations, text) that could
go towards achieving a certain tone for the project that does not
necessarily directly influence the tone of the data.
Not about a singular location: Some projects will involve just a
single chart and this makes it a far more straightforward prospect to
inform your definition of its best-fit location on this purpose map.
However, there will be other projects that you work on involving
multiple chart assets, multiple interactions, different pages and deeper
layers. So, when it comes to considering your initial vision through
the purpose map dimensions, you may recognise separate definitions
for each major elements. This will become much clearer as you get
deeper into the project – and can actually identify the need for
multiple assets.
The mantra proposed by Ben Schneiderman, one of the most
esteemed academics in this field – ‘Overview first, details on
demand’ – informs the idea of thinking about different layers of
readability and depth in your visualisation work accessed through
interactivity. Some of the chart types that you will meet in Chapter 6
can only ever hope to deliver a gist of the general magnitude of
values (the big, the small and the medium) and not their precise
details. A treemap, for example, is never going to facilitate the
detailed perceiving of values because it uses rectangular areas to
represent data values and our perceptual system is generally quite
poor at judging different area scales. Additionally, a treemap often
comprises a breakdown of many categorical values within the same
chart display, so it is very busy and densely packed. However, if you
have the capability to incorporate interactive features that allow the
user to enter via this first overview layer and then explore beneath the
surface, maybe clicking on a shape to reveal a pop-up with precise
value labels, you are opening up additional details.
In effect you have moved your viewer’s readability up the tonal
spectrum that began with more of a general feeling of data and then
moved towards the reading of data as a result of the interactive
operation. Sometimes a ‘gateway’ layer is required for your primary
view, to seduce your audience or to provide a big-picture overview
(feeling), and then you can let the audience move on to more
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perceptually precise displays of the data (reading) either through
interaction or perhaps by advancing through pages in a report or
slide-deck sequence.
In the Better Life Index, shown in Figure 3.10, the opening layer is based
around a series of charts that look like flowers. This is attractive, intriguing
and offers a nice, single-page, at-a-glance summary. The task of reading
the petal sizes with any degree of precision is hard but that is not the intent
of this first layer. The purpose is to get a balance between a form that
attracts the user and a function that offers a general sense of where the big,
medium and small values sit within the data. For those who want to read
the values with more precision, they are only a click away (on the flowers)
from viewing an alternative display using a bar chart to represent the same
values.
Figure 3.10 OECD Better Life Index
Figure 3.11 Losing Ground
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Increasingly there is a trend for projects to incorporate both
explanatory and exploratory experiences into the same overall project
– the term ‘explorable explanations’ has been coined to describe
them. A project like ‘Losing Ground’ by ProPublica (Figure 3.11) is
an example of this as it moves between telling a story about the
disappearing coastline of Louisiana and enabling users to interrogate
and adjust their view of the data at various milestone stages in the
sequence.
Harnessing Ideas
The discussions so far in this chapter have involved practical reasoning.
Before you move on to the immediate next stage of the design process –
working with data – it can be valuable to briefly allow yourself the
opportunity to harness your instinctive imagination.
Alongside your consideration of the purpose map, the other strand of
thinking about ‘vision’ concerns the earliest seeds of any ideas you may
have in mind for what this solution might comprise or even look like.
These might be mental manifestations of ideas you have formed yourself
or influenced or inspired by what you have seen elsewhere.
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‘I focus on structural exploration on one side and on the reality and the
landscape of opportunities in the other … I try not to impose any early
ideas of what the result will look like because that will emerge from the
process. In a nutshell I first activate data curiosity, client curiosity, and
then visual imagination in parallel with experimentation.’ Santiago
Ortiz, founder and Chief Data Officer at DrumWave, discussing the
role – and timing – of forming ideas and mental concepts
There are limits to the value of ideas and also to the role they are allowed
to play, as I will mention shortly, but your instincts can offer a unique
perspective if you choose to allow them to surface. If you have a naturally
analytical approach to visualisation this activity might seem to be the
wrong way round: how can legitimate ideas be formed until the data has
been explored? I understand that, and it is a step that some readers will
choose not to entertain until later in the process. However, do not rule it
out, see if liberating your imagination now adds value to your analytical
thinking later. There are several aspects to the concept and role of
harnessing ideas that I feel are valuable to consider at this primary stage:
Mental visualisation: This concerns the other meaning of
visualisation and is about embracing what we instinctively ‘see’ in
our mind’s eye when we consider the emerging brief for our task. In
Thinking Fast and Slow, by Daniel Kahneman, the author describes
two models of thought that control our thinking activities. He calls
these System 1 and System 2 thinking: the former is responsible for
our instinctive, intuitive and metaphorical thoughts; the latter is much
more ponderous, by contrast, much slower, and requiring of more
mental effort when being called upon. System 1 thinking is what you
want to harness right now: what are the mental impressions that form
quickly and automatically in your mind when you first think about the
challenge you’re facing?
You cannot switch off System 1 thoughts. You will not be able to
stop mental images formulating about what your mind’s eye sees
when thinking about this problem instinctively. So, rather than
stifling your natural mental habits, this earliest stage of the workflow
process presents the best possible opportunity to allow yourself space
to begin imagining.
What colours do you see? Sometimes instinctive ideas are reflections
of our culture or society, especially the connotations of colour usage.
What shapes and patterns strike you as being semantically aligned
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with the subject? This can be useful not just to inspire but also
possibly to obtain a glimpse into the similarly impulsive way the
minds of your audience might connect with a subject when
consuming the solution.
For example, Figure 3.12 shows the size of production for different
grape varieties across the wine industry. It uses a bubble chart to
create the impression of a bunch of grapes. You can clearly see how
this concept might have been formed in early sketches before the data
even arrived, based on the mental visualisation of what the shape of a
bunch of grapes looks like. It is consistent with the subject and offers
an immediate metaphor that means any viewer looking at the work
will immediately spot the connection between form and subject.
Figure 3.12 Grape expectations
Keywords: What terms of language come to mind when thinking
about the subject or the phenomena of your data? Figure 3.13 shows
some notes I made in capturing the instinctive keywords and colours
that came to mind when I was forming early thoughts and ideas about
a project to do with psychotherapy treatment in the Arctic.
The words reflected the type of language I felt would be important to
frame my design thinking, establishing a reference that could inform
the tone of voice of my work. The colours were somewhat arbitrary
and in the end I did not actually use them all, but they were indicative
of the tones I was seeking. I did, however, see through my intention
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to avoid the blacks and blues (as they would carry unwelcome and
clichéd connotations in this subject’s context).
Figure 3.13 Example of Keywords and Colour Swatch Ideas
Sketching: As well as taking notes, sketching ideas is of great value
to us here. I mentioned earlier that this is not about being a gifted
artist but recognising the freedom and speed when extracting ideas
from your mind onto paper. This is particularly helpful if you are
working with collaborators and want a low-fidelity sketch for
discussing plans, as well as in early discussions with stakeholders to
understand better each others’ take on the brief. For some people, the
most fluent and efficient way to ‘sketch’ is through their software
application of choice rather than on paper.
‘I draw to freely explore possibilities. I draw to visually understand
what I am thinking. I draw to evaluate my ideas and intuitions by seeing
them coming to life on paper. I draw to help my mind think without
limitations, without boundaries. The act of drawing, and the very fact
we choose to stop and draw, demands focus and attention. I use drawing
as my primary expression, as a sort of functional tool for capturing and
exploring thoughts.’ Giorgia Lupi, Co-founder and Design Director
at Accurat
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Regardless of whether your tool is the pen or the computer, just
sketch your ideas with whatever is the most efficient and effective
option given your time and confidence (see Figure 3.14). You will
likely refine your sketches later on and, indeed, eventually you will
move your attention completely away from pen and paper and onto
the tools you are using to create the final work.
Figure 3.14 Example of a Concept Sketch, by Giorgia Lupi
Research and inspiration: It is important to be sufficiently open to
influence and inspiration from the world around you. Exposing your
senses to different sources of reference both within and outside of
visualisation can only help to broaden the range of solutions you
might be able to conceive. Research the techniques that are being
used around the visualisation field, look through books and see how
others might have tackled similar subjects or curiosities (e.g. how
they have shown changes over time on a map).
Beyond visualisation consider any source of imagery that inspires
you: colours, patterns, shapes, and metaphors from everyday life
whose aesthetic qualities you just like. In addition to your notebook
and sketch pad, start a scrapbook or project mood board that compiles
the sources of inspiration you come across and helps you form ideas
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about the style, tone or essence of your project. They might not have
immediate value for the current project you are working on but may
materialise as useful for future work.
‘Recently taking up drawing has helped me better articulate the images I
see in my mind, otherwise I still follow up on all different types of
design and art outside information design/data visualisation. I try to
look at things outside my field as often as I can to keep my mind fresh
as opposed to only looking at projects from my field for inspiration.’
Stefanie Posavec, Information Designer
‘Look at how other designers solve visual problems (but don’t copy the
look of their solutions). Look at art to see how great painters use space,
and organise the elements of their pictures. Look back at the history of
infographics. It’s all been done before, and usually by hand! Draw
something with a pencil (or pen … but NOT a computer!) Sketch often:
The cat asleep. The view from the bus. The bus. Personally, I listen to
music – mostly jazz – a lot.’ Nigel Holmes, Explanation Graphic
Designer, on inspirations that feed his approach
‘It is easy to immerse yourself in a certain idea, but I think it is
important to step back regularly and recognise that other people have
different ways of interpreting things. I am very fortunate to work with
people whom I greatly admire and who also see things from a different
perspective. Their feedback is invaluable in the process.’ Jane Pong,
Data Visualisation Designer
Limitation of your ideas: There are important limitations to
acknowledge around the role of ideas. Influence and inspiration are
healthy: the desire to emulate what others have done is
understandable. Plagiarism, copying and stealing uncredited ideas are
wrong. There are ambiguities in any creative discipline about the
boundaries between influence and plagiarism, and the worlds of
visualisation and infographic design are not spared that challenge.
Being influenced by the research you do and the great work you see
around the field is not stealing, but if you do incorporate explicit
ideas influenced by others in your work, at the very least you should
do the noble thing and credit the authors, or even better seek them out
and ask them to grant you their approval. You do not have to credit
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William Playfair every time you use the bar chart, but there are
certain unique visual devices that will be unquestionably deserving of
attribution.
Secondly, data is your raw material, your ideas are not. As you will
see later, it is vital that you leave the main influence for your thinking
to emerge from the type, size and meaning of your data. It may be
that your ideas are ultimately incompatible with these properties of
the data, in which case you will need to set these aside, and perhaps
form new ones.
Eventually you will need to evolve from ideas and sketched concepts
to starting to develop a solution in your tool of choice. These early
ideas and sparks of creativity are vital and they should be embraced,
but do not be precious or stubborn, always maintain an open mind
and recognise that they have a limited role. Try to ignore the voices in
your head after a certain period!
Limitation of others’ ideas: Finally, there is the diplomatic
challenge of being faced with the prospect of taking on board other
people’s ideas. One of the greatest anxieties I face comes from
working with stakeholders who are unequivocally and emphatically
clear about what they think a solution should look like. Often your
involvement in a project may arrive after these ideas have already
been formed and have become the basis of the brief issued by the
stakeholders to you (‘Can you make this, please?’). This is where
your tactful but assured communicator’s skill set comes to the fore.
The ideas presented may be reasonable and well intended but it is
your responsibility to lead on the creation process and guide it away
from an early concept that simply may not work out. You can take
these idea on board but, as with the limitations of your own ideas,
there will be other factors with a greater influence – the nature of the
data, the type of curiosities you are pursuing, the essence of the
subject matter and the nature of the audience, among many other
things. These will be the factors that ultimately dictate whether any
early vision of potential ideas ends up being of value.
Summary: Formulating Your Brief
Establishing Your Project’s Context
Defining Your Origin Curiosity Why are we doing it: what type of
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curiosity has motivated the decision/desire to undertake this visualisation
project?
Personal intrigue: ‘I wonder what …’
Stakeholder intrigue: ‘He/she needs to know …’
Audience intrigue: ‘They need to know …’
Anticipated intrigue: ‘They might be interested in knowing …’
Potential intrigue: ‘There should be something interesting …’
Circumstances The key factors that will impact on your critical thinking
and shape your ambitions:
People: stakeholders, audience.
Constraints: pressures, rules.
Consumption: frequency, setting.
Deliverables: quantity, format.
Resources: skills, technology.
Defining Your Purpose The ‘so what?’: what are we trying to accomplish
with this visualisation? What is a successful ‘outcome’?
Establishing Your Project’s Vision
‘Purpose Map’ Plotting your expectation of what will be the best-fit type
of solution to facilitate the desired purpose:
What kind of experience? Explanatory, exhibitory or exploratory?
What tone of voice will it offer? The efficiency and perceptibility of
reading data vs the high-level, affective nature of feeling data?
Harnessing Ideas What mental images, ideas and keywords instinctively
come to mind when thinking about the subject matter of this challenge?
What influence and inspiration can you source from elsewhere that might
start to shape your thinking?
Tips and Tactics
Do not get hung up if you are struggling with some circumstantial
factors. Certain things may change in definition, some
things will emerge, some defined things will need to be reconsidered,
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some things are just always open.
Notes are so important to keep about any thoughts you have had that
express the nature of your curiosity, articulation of purpose, any
assumptions, things you know and do not know, where you might
need to get data from, who are the experts, questions, things to do,
issues/problems, wish lists …
Keep a ‘scrapbook’ (digital bookmarks, print clippings) of anything
and everything that inspires and influences you – not just data
visualisations. Log your ideas and inspire yourself.
This stage is about ambition management/skills – it is to your benefit
that you treat it with the thoroughness it needs. The negative impact
of any corners being cut here will be amplified later on.
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4 Working With Data
In Chapter 3 the workflow process was initiated by exploring the defining
matters around context and vision. The discussion about curiosity, framing
not just the subject matter of interest but also a specific enquiry that you
are seeking an answer to, in particular leads your thinking towards this
second stage of the process: working with data.
In this chapter I will start by covering some of the most salient aspects of
data and statistical literacy. This section will be helpful for those readers
without any – or at least with no extensive – prior data experience. For
those who have more experience and confidence with this topic, maybe
through their previous studies, it might merely offer a reminder of some of
the things you will need to focus on when working with data on a
visualisation project.
There is a lot of hard work that goes into the activities encapsulated by
‘working with data’. I have broken these down into four different groups
of action, each creating substantial demands on your time:
Data acquisition: Gathering the raw material.
Data examination: Identifying physical properties and meaning.
Data transformation: Enhancing your data through modification and
consolidation.
Data exploration: Using exploratory analysis and research techniques
to learn.
You will find that there are overlapping concerns between this chapter and
the nature of Chapter 5, where you will establish your editorial thinking.
The present chapter generally focuses more on the mechanics of
familiarisation with the characteristics and qualities of your data; the next
chapter will build on this to shape what you will actually do with it.
As you might expect, the activities covered in this chapter are associated
with the assistance of relevant tools and technology. However, the focus
for the book will remain concentrated on identifying which tasks you have
to undertake and look less at exactly how you will undertake these. There
will be tool-specific references in the curated collection of resources that
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are published in the digital companion.
4.1 Data Literacy: Love, Fear and Loathing
I frequently come across people in the field who declare their love for data.
I don’t love data. For me it would be like claiming ‘I love food’ when,
realistically, that would be misleading. I like sprouts but hate carrots. And
don’t get me started on mushrooms.
At the very start of the book, I mentioned that data might occasionally
prove to be a villain in your quest for developing confidence with data
visualisation. If data were an animal it would almost certainly be a cat: it
has a capacity to earn and merit love but it demands a lot of attention and
always seems to be conspiring against you.
I love data that gives me something interesting to do analysis-wise and
then, subsequently, also visually. Sometimes that just does not happen.
I love data that is neatly structured, clean and complete. This rarely exists.
Location data will have inconsistent place-name spellings, there will be
dates that have a mixture of US and UK formats, and aggregated data that
does not let me get to the underlying components.
You don’t need to love data but, equally, you shouldn’t fear data. You
should simply respect it by appreciating that it will potentially need lots of
care and attention and a shift in your thinking about its role in the creative
process. Just look to develop a rapport with it, embracing its role as the
absolutely critical raw material of this process, and learn how to nurture its
potential.
For some of you reading this book, you might have interest in data but
possibly not much knowledge of the specific activities involving data as
you work on a visualisation design solution. An assumed prerequisite for
anyone working in data visualisation is an appreciation of data and
statistical literacy. However, this is not always the case. One of the biggest
causes of failure in data visualisations – especially in relation to the
principle I introduced about ‘trustworthy design’ – comes from a poor
understanding of these numerate literacies. This can be overcome, though.
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‘When I first started learning about visualisation, I naively assumed that
datasets arrived at your doorstep ready to roll. Begrudgingly I accepted
that before you can plot or graph anything, you have to find the data,
understand it, evaluate it, clean it, and perhaps restructure it.’ Marcia
Gray, Graphic Designer
I discussed in the Introduction the different entry points from which people
doing data visualisation work come. Typically – but absolutely not
universally – those who join from the more creative backgrounds of
graphic design and development might not be expected to have developed
the same level of data and statistical knowledge than somebody from the
more numerate disciplines. If you are part of this creative cohort and can
identify with this generalisation, then this chapter will ease you through
the learning process (and in doing so hopefully dispel any myth that it is
especially complicated).
Conversely, many others may think they do not know enough about data
but in reality they already do ‘get’ it – they just need to learn more about
its role in visualisation and possibly realign their understanding of some of
the terminology. Therefore, before delving further into this chapter’s tasks,
there are a few ‘defining’ matters I need to address to cover the basics in
both data and statistical literacy.
Data Assets and Tabulation Types
Firstly, let’s consider some of the fundamentals about what a dataset is as
well as what shape and form it comes in.
When working on a visualisation I generally find there are two main
categories of data ‘assets’: data that exist in tables, known as datasets; and
data that exists as isolated values.
For the purpose of this book I describe this type of data as being raw
because it has not yet been statistically or mathematically manipulated
and it has not been modified in any other way from its original state.
Tabulated datasets are what we are mainly interested in at this point. Data
as isolated values refers to data that exists as individual facts and statistical
figures. These do not necessarily belong in, nor are they normally
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collected in, a table. They are just potentially useful values that are
dispersed around the Web or across reports: individual facts or figures that
you might come across during your data gathering or research stages. Later
on in your work you might use these to inform calculations (e.g. applying
a currency conversion) or to incorporate a fact into a title or caption (e.g.
78% of staff participated in the survey), but they are not your main focus
for now.
Tabulated data is unquestionably the most common form of data asset that
you will work with, but it too can exist in slightly different shapes and
sizes. A primary difference lies between what can be termed normalised
datasets (Figure 4.1) and cross-tabulated datasets (Figure 4.2).
A normalised dataset might loosely be described as looking like lists of
data values. In spreadsheet parlance, you would see this as a series of
columns and rows of data, while in database parlance it is the arrangement
of fields and records. This form of tabulated data is generally the most
detailed form of data available for you to work with. The table in Figure
4.1 is an example of normalised data where the columns of variables
provide different descriptive values for each movie (or record) held in the
table.
Figure 4.1 Example of a Normalised Dataset
Cross-tabulated data is presented in a reconfigured form where, instead of
displaying raw data values, the table of cells contain the results of
statistical operations (like summed totals, maximums, averages). These
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values are aggregated calculations formed from the relationship between
two variables held in the normalised form of the data. In Figure 4.2, you
will see the cross-tabulated result of the normalised table of movie data,
now showing a statistical summary for each movie category. The statistic
under ‘Max Critic Rating’ is formed from an aggregating calculation based
on the ‘Critic Rating’ and ‘Category’ variables seen in Figure 4.1.
Figure 4.2 Example of a Cross-tabulated Dataset
Typically, if you receive data in an already cross-tabulated form, you do
not have access to the original data. This means you will not be able to
‘reverse-engineer’ it back into its raw form, which, in turn, means you
have reduced the scope of your potential analysis. In contrast, normalised
data gives you complete freedom to explore, manipulate and aggregate
across multiple dimensions. You may choose to convert the data into
‘cross-tabulated’ form but that is merely an option that comes with the
luxury of having access to the detailed form of your data. In summary, it is
always preferable, where possible, to work with normalised data.
Data Types
One of the key parts of the design process concerns understanding the
different types of data (sometimes known as levels of data or scales of
measurement). Defining the types of data will have a huge influence on so
many aspects of this workflow, such as determining:
the type of exploratory data analysis you can undertake;
the editorial thinking you establish;
the specific chart types you might use;
the colour choices and layout decisions around composition.
In the simplest sense, data types are distinguished by being either
qualitative or quantitative in nature. Beneath this distinction there are
several further separations that need to be understood. The most useful
taxonomy I have found to describe these different types of data is based on
an approach devised by the psychologist researcher Stanley Stevens. He
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developed the acronym NOIR as a mnemonic device to cover the different
types of data you may come to work with, particularly in social research:
Nominal, Ordinal, Interval, and Ratio. I have extended this, adding onto
the front a ‘T’ – for Textual – which, admittedly, somewhat undermines
the grace of the original acronym but better reflects the experiences of
handling data today. It is important to describe, define and compare these
different types of data.
Textual (Qualitative)
Textual data is qualitative data and generally exists as unstructured streams
of words. Examples of textual data might include:
‘Any other comments?’ data submitted in a survey.
Descriptive details of a weather forecast for a given city.
The full title of an academic research project.
The description of a product on Amazon.
The URL of an image of Usain Bolt’s victory in the 100m at the 2012
Olympics.
Figure 4.3 Graphic Language: The Curse of the CEO
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In its native form, textual data is likely to offer rich potential but it can
prove quite demanding to unlock this. To work with textual data in an
analysis and visualisation context will generally require certain natural
language processing techniques to derive or extract classifications,
sentiments, quantitative properties and relational characteristics.
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An example of how you can use textual data is seen in the graphic of CEO
swear word usage shown in Figure 4.3. This analysis provides a
breakdown of the profanities used by CEOs from a review of recorded
conference calls over a period of 10 years. This work shows the two ways
of utilising textual data in visualisation. Firstly, you can derive categorical
classifications and quantitative measurements to count the use of certain
words compared to others and track their usage over time. Secondly, the
original form of the textual data can be of direct value for annotation
purposes, without the need for any analytical treatment, to include as
captions.
Working with textual data will always involve a judgement of reward vs
effort: how much effort will I need to expend in order to extract usable,
valuable content from the text? There are an increasing array of tools and
algorithmic techniques to help with this transformational approach but
whether you conduct it manually or with some degree of automation it can
be quite a significant undertaking. However, the value of the insights you
are able to extract may entirely justify the commitment. As ever, your
judgment of the aims of your work, the nature of your subject and the
interests of your audience will influence your decision.
Nominal (Qualitative)
Nominal data is the next form of qualitative data in the list of distinct data
types. This type of data exists in categorical form, offering a means of
distinguishing, labelling and organising values. Examples of nominal data
might include:
The ‘gender’ selected by a survey participant.
The regional identifier (location name) shown in a weather forecast.
The university department of an academic member of staff.
The language of a book on Amazon.
An athletic event at the Olympics.
Often a dataset will hold multiple nominal variables, maybe offering
different organising and naming perspectives, for example the gender, eye
colour and hair colour of a class of school kids.
Additionally, there might be a hierarchical relationship existing between
two or more nominal variables, representing major and sub-categorical
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values: for example, a major category holding details of ‘Country’ and a
sub-category holding ‘Airport’; or a major category holding details of
‘Industry’ and a sub-category holding details of ‘Company Names’.
Recognising this type of relationship will become important when
considering the options for which angles of analysis you might decide to
focus on and how you may portray them visually using certain chart types.
Nominal data does not necessarily mean text-based data; nominal values
can be numeric. For example, a student ID number is a categorical device
used uniquely to identify all students. The shirt number of a footballer is a
way of helping teammates, spectators and officials to recognise each
player. It is important to be aware of occasions when any categorical
values are shown as numbers in your data, especially in order to
understand that these cannot have (meaningful) arithmetic operations
applied to them. You might find logic statements like TRUE or FALSE
stated as a 1 and a 0, or data captured about gender may exist as a 1
(male), 2 (female) and 3 (other), but these numeric values should not be
considered quantitative values – adding ‘1’ to ‘2’ does not equal ‘3’ (other)
for gender.
Ordinal (Qualitative)
Ordinal data is still categorical and qualitative in nature but, instead of
there being an arbitrary relationship between the categorical values, there
are now characteristics of order. Examples of nominal data might include:
The response to a survey question: based on a scale of 1 (unhappy) to
5 (very happy).
The general weather forecast: expressed as Very Hot, Hot, Mild,
Cold, Freezing.
The academic rank of a member of staff.
The delivery options for an Amazon order: Express, Next Day, Super
Saver.
The medal category for an athletic event: Gold, Silver, Bronze.
Whereas nominal data is a categorical device to help distinguish values,
ordinal data is also a means of classifying values, usually in some kind of
ranking. The hierarchical order of some ordinal values goes through a
single ascending/descending rank from high or good values to low or bad
values. Other ordinal values have a natural ‘pivot’ where the direction
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changes around a recognisable mid-point, such as the happiness scale
which might pivot about ‘no feeling’ or weather forecast data that pivots
about ‘Mild’. Awareness of these different approaches to ‘order’ will
become relevant when you reach the design stages involving the
classifying of data through colour scales.
Interval (Quantitative)
Interval data is the less common form of quantitative data, but it is still
important to be aware of and to understand its unique characteristics. An
interval variable is a quantitative and numeric measurement defined by
difference on a scale but not by relative scale. This means the difference
between two values is meaningful but an arithmetic operation such as
multiplication is not.
The most common example is the measure for temperature in a weather
forecast, presented in units of Celsius. The absolute difference between
15°C and 20°C is the same difference as between 5°C and 10°C. However,
the relative difference between 5°C and 10°C is not the same as the
difference between 10°C and 20°C (where in both cases you multiply by
two or increase by 100%). This is because a zero value is arbitrary and
often means very little or indeed is impossible. A temperature reading of
0°C does not mean there is no temperature, it is a quantitative scale for
measuring relative temperature. You cannot have a shoe size or Body
Mass Index of zero.
Ratio (Quantitative)
Ratio data is the most common quantitative variable you are likely to come
across. It comprises numeric measurements that have properties of
difference and scale. Examples of nominal data might include:
The age of a survey participant in years.
The forecasted amount of rainfall in millimetres.
The estimated budget for a research grant proposal in GBP (£).
The number of sales of a book on Amazon.
The distance of the winning long jump at the 2012 Olympics in
metres.
Unlike interval data, for ratio data variables zero means something. The
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absolute difference in age between a 10 and 20 year old is the same as the
difference between a 40 and 50 year old. The relative difference between a
10 and a 20 year old is the same as the difference between a 40 and an 80
year old (‘twice as old’).
Whereas most of the quantitative measurements you will deal with are
based on a linear scale, there are exceptions. Variables about the strength
of sound (decibels) and magnitude of earthquakes (Richter) are actually
based on a logarithmic scale. An earthquake with a magnitude of 4.0 on
the Richter scale is 1000 times stronger based on the amount of energy
released than an earthquake of magnitude 2.0. Some consider these as
types of data that are different from ratio variables. Most still define them
as ratio variables but separate them as non-linear scaled variables.
If temperature values were measured in kelvin, where there is an
absolute zero, this would be considered a ratio scale, not an interval one.
Temporal Data
Time-based data is worth mentioning separately because it can be a
frustrating type of data to deal with, especially in attempting to define its
place within the TNOIR classification. The reason for this is that different
components of time can be positioned against almost all data types,
depending simply on what form your time data takes:
Textual: ‘Four o’clock in the afternoon on Monday, 12 March 2016’
Ordinal: ‘PM’, ‘Afternoon’, ‘March’, ‘Q1’
Interval: ‘12’, ‘12/03/2016’, ‘2016’
Ratio: ‘16:00’
Note that time-based data is separate in concern to duration data, which,
while often formatted in structures such as hh:mm:ss, should be seen as a
ratio measure. To work with duration data it is often useful to transform it
into single units of time, such as total seconds or minutes.
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Discrete vs Continuous
Another important distinction to make about your data, and something that
cuts across the TNOIR classification, is whether the data is discrete or
continuous. This distinction is influential in how you might analyse it
statistically and visually.
The relatively simple explanation is that discrete data is associated with all
classifying variables that have no ‘in-between’ state. This applies to all
qualitative data types and any quantitative values for which only a whole is
possible. Examples might be:
Heads or tails for a coin toss.
Days of the week.
The size of shoes.
Numbers of seats in a theatre.
In contrast, continuous variables can hold the value of an in-between state
and, in theory, could take on any value between the natural upper and
lower limits if it was possible to take measurements in fine degrees of
detail, such as:
Height and weight.
Temperature.
Time.
One of the classifications that is hard to nail down involves data that could,
on the TNOIR scale, arguably fall under both ordinal and ratio definitions
based on its usage. This makes it hard to determine if it should be
considered discrete or continuous. An example would be the star system
used for rating a movie or the happiness rating. When a star rating value is
originally captured, the likelihood is that the input data was discrete in
nature. However, for analysis purposes, the statistical operations applied to
data that is based on different star ratings could reasonably be treated
either as discrete classifications or, feasibly, as continuous numeric values.
For both star review ratings or happiness ratings decimal averages could
be calculated as a way of formulating average score. (The median and
mode would still be discrete.) The suitability of this approach will depend
on whether the absolute difference between classifying values can be
considered equal.
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4.2 Statistical Literacy
If the fear of data is misplaced, I can sympathise with anybody’s
trepidation towards statistics. For many, statistics can feel complicated to
understand and too difficult a prospect to master. Even for those relatively
comfortable with stats, it is unquestionably a discipline that can easily
become rusty without practice, which can also undermine your confidence.
Furthermore, the fear of making mistakes with delicate and rule-based
statistical calculations also depresses the confidence levels lower than they
need to be.
The problem is that you cannot avoid the need to use some statistical
techniques if you are going to work with data. It is therefore important to
better understand statistics and its role in visualisation, as you must do
with data. Perhaps you can make the problem more surmountable by
packaging the whole of statistics into smaller, manageable elements that
will dispel the perception of overwhelming complexity.
I do believe that it is possible to overstate the range and level of statistical
techniques most people will need to employ on most of their visualisation
tasks. The caveats are important as I know there will be people with
visualisation experience who are exposed to a tremendous amount of
statistical thinking in their work, but it is a relevant point.
It all depends, of course. From my experience, however, the majority of
data visualisation challenges will generally involve relatively
straightforward univariate and multivariate statistical techniques.
Univariate techniques help you to understand the shape, size and range of
quantitative values. Multivariate techniques help you to explore the
possible relationships between different combinations of variables and
variable types. I will describe some of the most relevant statistical
operations associated with these techniques later in this chapter, at the
point in your thinking where they are most applicable.
As you get more advanced in your work (and your confidence increases)
you might have occasion to employ inference techniques. These include
concepts such as data modelling and the use of regression analysis:
attempting to measure the relationships between variables to explore
correlations and (the holy grail) causations. Many of you will likely
experience visualisation challenges that require an understanding of
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probabilities, testing hypotheses and becoming acquainted with terms like
confidence intervals. You might use these techniques to assist with
forecasting or modelling risk and uncertainty. Above and beyond that, you
are moving towards more advanced statistical modelling and algorithm
design.
It is somewhat dissatisfactory to allocate only a small part of this text to
discussing the role of descriptive and exploratory statistics. However, for
the scope of this book, and seeking to achieve a pragmatic balance, the
most sensible compromise is just to flag up which statistical activities you
might need to consider and where these apply. It can take years to learn
about the myriad advanced techniques that exist and it takes experience to
know when and how to deploy all the different methods.
There are hundreds of books better placed to offer the depth of detail you
truly need to fulfil these activities and there is no real need to reinvent the
wheel – and indeed reinvent an inferior wheel. That statistics is just one
part of the visualisation challenge, and is in itself such a prolific field,
further demonstrates the variety and depth of this subject.
4.3 Data Acquisition
The first step in working with data naturally involves getting it. As I
outlined in the contextual discussion about the different types of trigger
curiosities, you will only have data in place before now if the opportunity
presented by the data was the factor that triggered this work. You will
recall this scenario was described as pursuing a curiosity born out of
‘potential intrigue’. Otherwise, you will only be in a position to know what
data you need after having established your specific or general motivating
curiosity. In these situations, once you have sufficiently progressed your
thinking around ‘formulating your brief’, you will need to switch your
thinking onto the task of acquiring your data:
What data do you need and why?
From where, how, and by whom will the data be acquired?
When can you obtain it?
What Data Do You Need?
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Your primary concern is to ensure you can gather sufficient data about the
subject in which you are interested to pursue your identified curiosity. By
‘sufficient’, I mean you will need to establish some general criteria in your
mind for what data you do need and what data you do not need. There is
no harm in getting more than you need at this stage but it can result in
wasted efforts, waste that you would do well to avoid.
Let’s propose you have defined your curiosity to be ‘I wonder what a map
of McDonald’s restaurant openings looks like over time?’. In this scenario
you are going to try to find a source of data that will provide you with
details of all the McDonald’s restaurants that have ever opened. A
shopping list of data items would probably include the date of opening, the
location details (as specific as possible) and maybe even a closing date to
ensure you can distinguish between still operating and closed-down
restaurants.
You will need to conduct some research, a perpetual strand of activity that
runs throughout the workflow, as I explained earlier. In this scenario you
might need first to research a bit of the history of McDonald’s restaurants
to discover, for instance, when the first one opened, how many there are,
and in which countries they are located. This will establish an initial sense
of the timeframe (number of years) and scale (outlets, global spread) of
your potential data. You might also discover significant differences
between what is considered a restaurant and what is just a franchise
positioned in shopping malls or transit hubs. Sensitivities around the
qualifying criteria or general counting rules of a subject are important to
discover, as they will help significantly to substantiate the integrity and
accuracy of your work.
Unless you know or have been told where to find this restaurant data, you
will then need to research from where the data might be obtainable. Will
this type of information be published on the Web, perhaps on the
commercial pages of McDonald’s own site? You might have to get in
touch with somebody (yes, a human) in the commercial or PR department
to access some advice. Perhaps there will be some fast-food enthusiast in
some niche corner of the Web who has already gathered and made
available data like this?
Suppose you locate a dataset that includes not just McDonald’s restaurants
but all fast-food outlets. This could potentially broaden the scope of your
curiosity, enabling broader analysis about the growth of the fast-food
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industry at large to contextualise MacDonald’s contribution to this.
Naturally, if you have any stakeholders involved in your project, you
might need to discuss with them the merits of this wider perspective.
Another judgement to make concerns the resolution of the data you
anticipate needing. This is especially relevant if you are working with big,
heavy datasets. You might genuinely want and need all available data.
This would be considered full resolution – down to the most detailed grain
(e.g. all details about all MacDonald’s restaurants, not just totals per city
or country). Sometimes, in this initial gathering activity, it may be more
practical just to obtain a sample of your data. If this is the case, what will
be the criteria used to identify a sufficient sample and how will you select
or exclude records? What percentage of your data will be sufficient to be
representative of the range and diversity (an important feature we will
need to examine next)? Perhaps you only need a statistical, high-level
summary (total number of restaurants opened by year)?
The chances are that you will not truly know what data you want or need
until you at least get something to start with and learn from there. You
might have to revisit or repeat the gathering of your data, so an attitude of
‘what I have is good enough to start with’ is often sensible.
From Where, How and By Whom Will the Data
Be Acquired?
There are several different origins and methods involved in acquiring data,
depending on whether it will involve your doing the heavy work to curate
the data or if this will be the main responsibility of others.
Curated by You
This group of data-gathering tasks or methods is characterised by your
having to do most of the work to bring the data together into a convenient
digital form.
Primary data collection: If the data you need does not exist or you need
to have full control over its provenance and collection, you will have to
consider embarking on gathering ‘primary’ data. In contrast to secondary
data, primary data involves you measuring and collecting the raw data
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yourself. Typically, this relates to situations where you gather quite small,
bespoke datasets about phenomena that are specific to your needs. It might
be a research experiment you have designed and launched for participants
to submit responses. You may manually record data from other
measurement devices, such as your daily weight as measured by your
bathroom scales, or the number of times you interacted face-to-face with
friends and family. Some people take daily photographs of themselves,
their family members or their gardens, in order to stitch these back
together eventually to portray stories of change. This data-gathering
activity can be expensive in terms of both the time and cost. The benefit
however is that you have carefully controlled the collection of the data to
optimise its value for your needs.
Manual collection and data foraging: If the data you need does not exist
digitally or in a convenient singular location, you will need to forage for it.
This again might typically relate to situations where you are sourcing
relatively small datasets. An example might be researching historical data
from archived newspapers that were only published in print form and not
available digitally. You might look to pull data from multiple sources to
create a single dataset: for example, if you were comparing the attributes
of a range of different cars and weighing up which to buy. To achieve this
you would probably need to source different parts of the data you need
from several different places. Often, data foraging is something you
undertake in order to finish off data collected by other means that might
have a few missing values. It is sometimes more efficient to find the
remaining data items yourself by hand to complete the dataset. This can be
somewhat time-consuming depending on the extent of the manual
gathering required, but it does provide you with greater assurance over the
final condition of the data you have collected.
Extracted from pdf files: A special subset of data foraging – or a
variation at least – involves those occasions when your data is digital but
essentially locked away in a pdf file. For many years now reports
containing valuable data have been published on the Web in pdf form.
Increasingly, movements like ‘open data’ are helping to shift the attitudes
of organisations towards providing additional, fully accessible digital
versions of data. Progress is being made but it will take time before all
industries and government bodies adopt this as a common standard. In the
meantime, there are several tools on the market (free and proprietary) that
will assist you in extracting tables of data from pdf files and converting
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these to more usable Excel or CSV formats.
Some data acquisition tasks may be repetitive and, should you possess
the skills and have access to the necessary resources, there will be scope
for exploring ways to automate these. However, you always have to
consider the respective effort and ongoing worth of your approach. If
you do go to the trouble of authoring an automation routine (of any
description) you could end up spending more time on that than you
would otherwise collecting by more manual methods. If it is going to be
a regular piece of analysis the efficiency gains from your automation
will unquestionably prove valuable going forward, but, for any one-off
projects, it may not be ultimately worth it
Web scraping (also known as web harvesting): This involves using
special tools or programs to extract structured and unstructured items of
data published in web pages and convert these into tabulated form for
analysis. For example, you may wish to extract several years’ worth of test
cricket results from a sports website. Depending on the tools used, you can
often set routines in motion to extract data across multiple pages of a site
based on the connected links that exist within it. This is known as web
crawling. Using the same example (let’s imagine), you could further your
gathering of test cricket data by programmatically fetching data back from
the associated links pointing to the team line-ups. An important
consideration to bear in mind with any web scraping or crawling activity
concerns rules of access and the legalities of extracting the data held on
certain sites. Always check – and respect – the terms of use before
undertaking this.
Curated by Others
In contrast to the list of methods I have profiled, this next set of data-
gathering approaches is characterised by other people having done most of
the work to source and compile the data. They will make it available for
you to access in different ways without needing the extent of manual
efforts often required with the methods presented already. You might
occasionally still have to intervene by hand to fine-tune your data, but
others would generally have put in the core effort.
Issued to you: On the occasions when you are commissioned by a
stakeholder (client, colleague) you will often be provided with the data you
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need (and probably much more besides), most commonly in a spreadsheet
format. The main task for you is therefore less about collection and more
about familiarisation with the contents of the data file(s) you are set to
work with.
Download from the Web: Earlier I bemoaned the fact that there are still
organisations publishing data (through, for example, annual reports) in pdf
form. To be fair, increasingly there are facilities being developed that
enable interested users to extract data in a more structured form. More
sophisticated reporting interfaces may offer users the opportunity to
construct detailed queries to extract and download data that is highly
customised to their needs.
System report or export: This is related more to an internal context in
organisations where there are opportunities to extract data from corporate
systems and databases. You might, for example, wish to conduct some
analysis about staff costs and so the personnel database may be where you
can access the data about the workforce and their salaries.
‘Don’t underestimate the importance of domain expertise. At the Office
for National Statistics (ONS), I was lucky in that I was very often
working with the people who created the data – obviously, not everyone
will have that luxury. But most credible data producers will now
produce something to accompany the data they publish and help users
interpret it – make sure you read it, as it will often include key findings
as well as notes on reliability and limitations of the data.’ Alan Smith
OBE, Data Visualisation Editor, Financial Times
Third-party services: There is an ever-increasing marketplace for data
and many commercial services out there now offer extensive sources of
curated and customised data that would otherwise be impossible to obtain
or very complex to gather. Such requests might include very large,
customised extracts from social media platforms like Twitter based on
specific keywords and geo-locations.
API: An API (Application Programme Interface) offers the means to
create applications that programmatically access streams of data from sites
or services, such as accessing a live feed from Transport for London (TfL)
to track the current status of trains on the London Underground system.
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When Can the Data Be Acquired?
The issue of when data is ready and available for acquisition is a delicate
one. If you are conducting analysis of some survey results, naturally you
will not have the full dataset of responses to work with until the survey is
closed. However, you could reasonably begin some of your analysis work
early by using an initial sample of what had been submitted so far. Ideally
you will always work with data that is as complete as possible, but on
occasions it may be advantageous to take the opportunity to get an early
sense of the nature of the submitted responses in order to begin preparing
your final analysis routines. Working on any dataset that may not yet be
complete is a risk. You do not want to progress too far ahead with your
visualisation workflow if there is the real prospect that any further data
that emerges could offer new insights or even trigger different, more
interesting curiosities.
4.4 Data Examination
After acquiring your data your next step is to thoroughly examine it. As I
have remarked, your data is your key raw material from which the
eventual visualisation output will be formed. Before you choose what meal
to cook, you need to know what ingredients you have and what you need
to do to prepare them.
It may be that, in the act of acquiring the data, you have already achieved a
certain degree of familiarity about its status, characteristics and qualities,
especially if you curated the data yourself. However, there is a definite
need to go much further than you have likely achieved before now. To do
this you need to conduct an examination of the physical properties and the
meaning of your data.
As you progress through the stages of this workflow, your data will likely
change considerably: you will bring more of it in, you will remove some of
it, and you will refine it to suit your needs. All these modifications will
alter the physical makeup of your data so you will need to keep revisiting
this step to preserve your critical familiarity.
Data Properties
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The first part of familiarising yourself with with your data is to undertake
an examination of its physical properties. Specifically you need to
ascertain its type, size and condition. This task is quite mechanical in many
ways because you are in effect just ‘looking’ at the data, establishing its
surface characteristics through visual and/or statistical observations.
What To Look For?
The type and size of your data involve assessing the characteristics and
amount of data you have to work with. As you examine the data you also
need to determine its condition: how good is its quality and is it fit for
purpose?
Data types: Firstly, you need to identify what data types you have. In
gathering this data in the first place you might already have a solid
appreciation about what you have before you, but doing this
thoroughly helps to establish the attention to detail you will need to
demonstrate throughout this stage. Here you will need to refer to the
definitions from earlier in the chapter about the different types of data
(TNOIR). Specifically you are looking to define each column or field
of data based on whether it is qualitative (text, nominal, ordinal) or
quantitative (interval, ratio) and whether it is discrete or continuous in
nature.
Size: Within each column or field you next need to know what range
of values exist and what are the specific attributes/formats of the
values held. For example, if you have a quantitative variable (interval
or ratio), what is the lowest and the highest value? In what number
format is it presented (i.e. how many decimal points or comma
formatted)? If it is a categorical variable (nominal or ordinal), how
many different values are held? If you have textual data, what is the
maximum character length or word count?
Condition: This is the best moment to identify any data quality and
completeness issues. Naturally, unidentified and unresolved issues
around data quality will come to bite hard later, undermining the
scope and, crucially, trust in the accuracy of your work. You will
address these issues next in the ‘transformation’ step, but for now the
focus is on identifying any problems. Things to look out for may
include the following:
Missing values, records or variables – Are empty cells assumed
as being of no value (zero/nothing) or no measurement (n/a,
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null)? This is a subtle but important difference.
Erroneous values – Typos and any value that clearly looks out of
place (such as a gender value in the age column).
Inconsistencies – Capitalisation, units of measurement, value
formatting.
Duplicate records.
Out of date – Values that might have expired in accuracy, like
someone’s age or any statistic that would be reasonably expected
to have subsequently changed.
Uncommon system characters or line breaks.
Leading or trailing spaces – the invisible evil!
Date issues around format (dd/mm/yy or mm/dd/yy) and basis
(systems like Excel’s base dates on daily counts since 1 January
1900, but not all do that).
How to Approach This?
I explained in the earlier ‘Data literacy’ section the difference in asset
types (data that exists in tables and data that exists as isolated values) and
also the difference in form (normalised data or cross-tabulated).
Depending on the asset and form of data, your examination of data types
may involve slightly different approaches, but the general task is the same.
Performing this examination process will vary, though, based on the tools
you are using. The simplest approach, relevant to most, is to describe the
task as you would undertake it using Excel, given that this continues to be
the common tool most people use or have the skills to use. Also, it is likely
that most visualisation tasks you undertake will involve data of a size that
can be comfortably handled in Excel.
‘Data inspires me. I always open the data in its native format and look at
the raw data just to get the lay of the land. It’s much like looking at a
map to begin a journey.’ Kim Rees, Co-founder, Periscopic
As you go through this task, it is good practice to note down a detailed
overview of what data you have, perhaps in the form of a table of data
descriptions. This is not as technical a duty as would be associated with the
creation of a data dictionary but its role and value are similar, offering a
convenient means to capture all the descriptive properties of your various
data assets.
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Inspect and scan: Your first task is just to scan your table of data
visually. Navigate around it using the mouse/trackpad, use the arrow
keys to move up or down and left or right, and just look at all the
data. Gain a sense of its overall dimension. How many columns and
how many rows does it occupy? How big a prospect might working
with this be?
Data operations: Inspecting your data more closely might require the
use of interrogation features such as sorting columns and doing basic
filters. This can be a quick and simple way to acquaint yourself with
the type of data and range of values.
Going further, once again depending on the technology (and
assuming you have normalised data to start with), you might apply a
cross-tabulation or pivot table to create aggregated, summary views
of different angles and combinations of your data. This can be a
useful approach to also check out the unique range of values that exist
under different categories as well as helping to establish how sub-
categories may relate other categories hierarchically. This type of
inspection will be furthered in the next step of the ‘working with data’
process when you will undertake deeper visual interrogations of the
type, size and condition of your data.
If you have multiple tables, you will need to repeat this approach for
each one as well as determine how they are related collectively and
on what basis. It could be that just considering one table as the
standard template, representative of each instance, is sufficient: for
example, if each subsequent table is just a different monthly view of
the same activity.
For so-called ‘Big Data’ (see the glossary definition earlier), it is less
likely that you can conduct this examination work through relatively
quick, visual observations using Excel. Instead it will need tools
based around statistical language that will describe for you what is
there rather than let you look at what is there.
Statistical methods: The role of statistics in this examination stage
generally involves relatively basic quantitative analysis methods to
help describe and understand the characteristics of each data variable.
The common term applied to this type of statistical approach is
univariate, because it involves just looking at one variable at a time
(the best opportunity to perform the analysis of multiple variables
comes later). Here are some different types of statistical analyses you
might find useful at this stage. These are not the only methods you
will ever need to use, but will likely prove to be among the most
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common:
Frequency counts: applied to categorical values to understand
the frequency of different instances.
Frequency distribution: applied to quantitative values to learn
about the type and shape of the distribution of values.
Measurements of central tendency describe the summary
attributes of a group of quantitative values, including:
the mean (the average value);
the median (the middle value if all quantities were arranged
from smallest to largest);
the mode (the most common value).
Measurements of spread are used to describe the dispersion of
values above and below the mean:
Maximum, minimum and range: the highest and lowest and
magnitude of spread of values.
Percentiles: the value below which x% of values fall (e.g.
the 20th percentile is the value below which 20% of all
quantitative values fall).
Standard deviation: a calculated measure used to determine
how spread out a series of quantitative values are.
Data Meaning
Irrespective of whether you or others have curated the data, you need to be
discerning about how much trust you place in it, at least to begin with. As
discussed in the ‘trustworthy design’ principle, there are provenance
issues, inaccuracies and biases that will affect its status on the journey
from being created to being acquired. These are matters you need to be
concerned with in order to resolve or at least compensate for potential
shortcomings.
Knowing more about the physical properties of your data does not yet
achieve full familiarity with its content nor give you sufficient
acquaintance with its qualities. You will have examined the data in a
largely mechanical and probably quite detached way from the underlying
subject matter. You now need to think a little deeper about its meaning,
specifically what it does – and does not – truly represent.
‘A visualization is always a model (authored), never a mould (replica),
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of the real. That’s a huge responsibility.’ Paolo Ciuccarelli, Scientific
Director of DensityDesign Research Lab at Politecnico di Milano
What Phenomenon?
Determining the meaning of your data requires that you recognise this is
more than just a bunch of numbers and text values held in the cells of a
table. Ask yourself, ‘What is it about? What activity, entity, instance or
phenomenon does it represent?’.
One of the most valuable pieces of advice I have seen regarding this task
came from Kim Rees, co-founder of Periscopic. Kim describes the process
of taking one single row of data and using that as an entry point to learn
carefully about what each value means individually and then collectively.
Breaking down the separation between values created by the table’s cells,
and then sticking the pieces back together, helps you appreciate the parts
and the whole far better.
‘Absorb the data. Read it, re-read it, read it backwards and understand
the lyrical and human-centred contribution.’ Kate McLean, Smellscape
Mapper and Senior Lecturer Graphic Design
You saw the various macro- and micro-level views applied to the context
of the Texas Department for Criminal Justice executed offenders
information in the previous chapter. The underlying meaning of this data –
its phenomenon – was offenders who had been judged guilty of
committing heinous crimes and had faced the ultimate consequence. The
availability of textual data describing the offenders’ last statements and
details of their crimes heightened the emotive potential of this data. It was
heavy stuff. However, it was still just a collection of values detailing dates,
names, locations, categories. All datasets, whether on executed offenders
or the locations of MacDonald’s restaurants, share the same properties as
outlined by the TNOIR data-type mnemonic. What distinguishes them is
what these values mean.
What you are developing here is a more semantic appreciation of your data
to substantiate the physical definitions. You are then taking that collective
appreciation of what your data stands for to influence how you might
decide to amplify or suppress the influence of this semantic meaning. This
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builds on the discussion in the last chapter about the tonal dimension,
specifically the difference between figurative and non-figurative
portrayals.
A bar chart (Figure 4.4) comprising two bars, one of height 43 and the
other of height 1, arguably does not quite encapsulate the emotive
significance of Barack Obama becoming the first black US president,
succeeding the 43 white presidents who served before him. Perhaps a more
potent approach may be to present a chronological display of 44
photographs of each president in order to visually contrast Mr Obama’s
headshot in the final image in the sequence with the previous 43.
Essentially, the value of 43 is almost irrelevant in its detail – it could be 25
or 55 – it is about there being ‘many’ of the same thing followed by the
‘one’ that is‘different’. That’s what creates the impact. (What will image
number 45 bring? A further striking ‘difference’ or a return to the standard
mould?)
Figure 4.4 US Presidents by Ethnicity (1789 to 2015)
Learning about the underlying phenomena of your data helps you feel its
spirit more strongly than just looking at the rather agnostic physical
properties. It also helps you in knowing what potential sits inside the data
– the qualities it possesses – so you are then equipped the best
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understanding of how you might want to portray it. Likewise it prepares
you for the level of responsibility and potential sensitivity you will face in
curating a visual representation of this subject matter. As you saw with the
case study of the ‘Florida Gun Crimes’ graphic, some subjects are
inherently more emotive than others, so we have to demonstrate a certain
amount of courage and conviction in deciding how to undertake such
challenges.
‘Find loveliness in the unlovely. That is my guiding principle. Often,
topics are disturbing or difficult; inherently ugly. But if they are
illustrated elegantly there is a special sort of beauty in the truthful
communication of something. Secondly, Kirk Goldsberry stresses that
data visualization should ultimately be true to a phenomenon, rather
than a technique or the format of data. This has had a huge impact on
how I think about the creative process and its results.’ John Nelson,
Cartographer
Completeness
Another aspect of examining the meaning of data is to determine how
representative it is. I have touched on data quality already, but inaccuracies
in conclusions about what data is saying have arguably a greater impact on
trust and are more damaging than any individual missing elements of data.
The questions you need to ask of your data are: does it represent genuine
observations about a given phenomenon or is it influenced by the
collection method? Does your data reflect the entirety of a particular
phenomenon, a recognised sample, or maybe even an obstructed view
caused by hidden limitations in the availability of data about that
phenomenon?
Reflecting on the published executed offenders data, there would be a
certain confidence that it is representative of the total population of
executions but with a specific caveat: it is all the executed offenders under
the jurisdiction of the Texas Department of Criminal Justice since 1982. It
is not the whole of the executions conducted across the entire USA nor is it
representative of all the executions that have taken place throughout the
history of Texas. Any conclusions drawn from this data must be boxed
within those parameters.
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The matter of judging completeness can be less about the number of
records and more a question of the integrity of the data content. This
executed offenders dataset would appear to be a trusted and reliable record
of each offender but would there/could there be an incentive for the
curators of this data not to capture, for example, the last statements as they
were explicitly expressed? Could they have possibly been in any way
sanitised or edited, for example? These are the types of questions you need
to pose. This is not aimless cynicism, it is about seeking assurances of
quality and condition so you can be confident about what you can
legitimately present and conclude from it (as well as what you should not).
Consider a different scenario. If you are looking to assess the political
mood of a nation during a televised election debate, you might consider
analysing Twitter data by looking at the sentiments for and against the
candidates involved. Although this would offer an accessible source of
rich data, it would not provide an entirely reliable view of the national
mood. It could only offer algorithmically determined insights (i.e. through
the process of determining the sentiment from natural language) of the
people who have a Twitter account, are watching the debate and have
chosen to tweet about it during a given timeframe.
Now, just because you might not have access to a ‘whole’ population of
political opinion data does not mean it is not legitimate to work on a
sample. Sometimes samples are astutely reflective of the population. And
in truth, if samples were not viable then most of the world’s analyses
would need to cease immediately.
A final point is to encourage you to probe any absence of data. Sometimes
you might choose to switch the focus away from the data you have got
towards the data you have not got. If the data you have is literally as much
as you can acquire but you know the subject should have more data about
it, then perhaps shine a light on the gaps, making that your story. Maybe
you will unearth a discovery about the lack of intent or will to make the
data available, which in itself may be a fascinating discovery. As
transparency increases, those who are not stand out the most.
‘This is one of the first questions we should ask about any dataset: what
is missing? What can we learn from the gaps?’ Jer Thorp, Founder of
The Office for Creative Research
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Any identified lack of completeness or full representativeness is not an
obstacle to progress, it just means you need to tread carefully with regard
to how you might represent and present any work that emerges from it. It
is about caution not cessation.
Influence on Process
This extensive examination work gives you an initial – but thorough –
appreciation of the potential of your data, the things it will offer and the
things it will not. Of course this potential is as yet unrealised. Furthering
this examination will be the focus of the next activity, as you look to
employ more visual techniques to help unearth the as-yet-hidden qualities
of understanding locked away in the data. For now, this examination work
takes your analytical and creative thinking forward another step.
Purpose map ‘tone’: Through deeper acquaintance with your data,
you will have been able to further consider the suitability of the
potential tone of your work. By learning more about the inherent
characteristics of the subject, this might help to confirm or redefine
your intentions for adopting a utilitarian (reading) or sensation-based
(feeling) tone.
Editorial angles: The main benefit of exploring the data types is to
arrive at an understanding of what you have and have not got to work
with. More specifically, it guides your thinking towards what possible
angles of analysis may be viable and relevant, and which can be
eliminated as not. For example, if you do not have any location or
spatial data, this rules out the immediate possibility of being able to
map your data. This is not something you could pursue with the
current scope of your dataset. If you do have time-based data then the
prospect of conducting analysis that might show changes over time is
viable. You will learn more about this idea of editorial ‘angle’ in the
next chapter but let me state now it is one of the most important
components of visualisation thinking.
Physical properties influence scale: Data is your raw material, your
ideas are not. I stated towards the end of Chapter 3 that you should
embrace the instinctive manifestations of ideas and seek influence
and inspiration from other sources. However, with the shape and size
of your data having such an impact on any eventual designs, you must
respect the need to be led by your data’s physical properties and not
just your ideas.
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Figure 4.5 OECD Better Life Index
In particular, the range of values in your data will shape things
significantly. The shape of data in the ‘Better Life Index’ project you
saw earlier is a good example. Figure 4.5 presents an analysis of the
quality of life across the 36 OECD member states. Each country is a
flower comprising 11 petals with each representing a different quality
of life indicator (the larger the petal, the better the measured quality
of life).
Consider this. Would this design concept still be viable if there were
20 indicators? Or just 3? How about if the analysis was for 150
countries? The connection between data range and chart design
involves a discerning judgement about ‘fit’. You need to identify
carefully the underlying shape of the data to be displayed and what
tolerances this might test in the shape of the possible design concepts
used.
‘My design approach requires that I immerse myself deeply in the
problem domain and available data very early in the project, to get a feel
for the unique characteristics of the data, its “texture” and the
affordances it brings. It is very important that the results from these
explorations, which I also discuss in detail with my clients, can
influence the basic concept and main direction of the project. To put it
in Hans Rosling’s words, you need to “let the data set change your mind
set”.’ Moritz Stefaner, Truth & Beauty Operator
Another relevant concern involves the challenge of elegantly handling
quantitative measures that have hugely varied value ranges and contain
(legitimate) outliers. Accommodating all the values into a single display
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can have a hugely distorting impact on the space it occupies. For example,
note the exceptional size of the shape for Avatar in Figure 4.6, from the
‘Spotlight on profitability’ graphic you saw earlier. It is the one movie
included that bursts through the ceiling, far beyond the otherwise entirely
suitable 1000 million maximum scale value. As a single outlier, in this
case, it was treated with a rather unique approach. As you can see, its
striking shape conveniently trespasses onto the space offered by the two
empty rows above. The result emphasises this value’s exceptional quality.
You might seldom have the luxury of this type of effective resolution, so
the key point to stress is always be acutely aware of the existence of
‘Avatars’ in your data.
Figure 4.6 Spotlight on Profitability
4.5 Data Transformation
Having undertaken an examination of your data you will have a good idea
about what needs to be done to ensure it is entirely fit for purpose. The
next activity is to work on transforming the data so it is in optimum
condition for your needs.
At this juncture, the linearity of a book becomes rather unsatisfactory.
Transforming your data is something that will take place before, during
and after both the examination and (upcoming) exploration steps. It will
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also continue beyond the boundaries of this stage of the workflow. For
example, the need to transform data may only emerge once you begin your
‘editorial thinking’, as covered by the next chapter (indeed you will likely
find yourself bouncing forwards and backwards between these sections of
the book on a regular basis). As you get into the design stage you will
constantly stumble upon additional reasons to tweak the shape and size of
your data assets. The main point here is that your needs will evolve. This
moment in the workflow is not going to be the only or final occasion when
you look to refine your data.
Two important notes to share upfront at this stage. Firstly, in accordance
with the desire for trustworthy design, any treatments you apply to your
data need to be recorded and potentially shared with your audience. You
must be able to reveal the thinking behind any significant assumptions,
calculations and modifications you have made to your data.
Secondly, I must emphasise the critical value of keeping backups. Before
you undertake any transformation, make a copy of your dataset. After each
major iteration remember to save a milestone version for backup purposes.
Additionally, when making changes, it is useful to preserve original
(unaltered) data items nearby for easy rollback should you need them. For
example, suppose you are cleaning up a column of messy data to do with
‘Gender’ that has a variety of inconsistent values (such as “M”, “Male”,
“male”, “FEMALE”, “F”, “Female”). Normally I would keep the original
data, duplicate the column, and then tidy up this second column of values.
I have then gained access to both original and modified versions. If you are
going to do any transformation work that might involve a significant
investment of time and (manual) effort, having an opportunity to refer to a
previous state is always useful in my experience.
There are four different types of potential activity involved in transforming
your data: cleaning, converting, creating and consolidating.
Transform to clean: I spoke about the importance of data quality
(better quality in, better quality out, etc.) in the examination section
when looking at the physical condition of the data. There’s no need to
revisit the list of potential observations you might need to consider
looking out for but this is the point where you will need to begin to
address these.
There is no single or best approach for how to conduct this task.
Some issues can be addressed through a straightforward ‘find and
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replace’ (or remove) operation. Some treatments will be possible
using simple functions to convert data into new states, such as using
logic formulae that state ‘if this, do this, otherwise do that’. For
example, if the value in the ‘Gender’ column is “M” make it “Male”,
if the value is “MALE” make it “Male” etc. Other tasks might be
much more intricate, requiring manual intervention, often in
combination with inspection features like ‘sort’ or ‘filter’, to find,
isolate and then modify problem values.
Part of cleaning up your data involves the elimination of junk. Going
back to the earlier scenario about gathering data about McDonald’s
restaurants, you probably would not need the name of the restaurant
manager, details of the opening times or the contact telephone
number. It is down to your judgement at the time of gathering the data
to decide whether these extra items of detail – if they were as easily
acquirable as the other items of data that you really did need – may
potentially provide value for your analysis later in the process. My
tactic is usually to gather as much data as I can and then reject/trim
later; later has arrived and now is the time to consider what to
remove. Any fields or rows of data that you know serve no ongoing
value will take up space and attention, so get rid of these. You will
need to separate the wheat from the chaff to help reduce your
problem.
Transform to convert: Often you will seek to create new data values
out of existing ones. In the illustration in Figure 4.7, it might be
useful to extract the constituent parts of a ‘Release Date’ field in
order to group, analyse and use the data in different ways. You might
use the ‘Month’ and ‘Year’ fields to aggregate your analysis at these
respective levels in order to explore within-year and across-year
seasonality. You could also create a ‘Full Release Date’ formatted
version of the date to offer a more presentable form of the release
date value possibly for labeling purposes.
Figure 4.7 Example of Converted Data Transformation
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Extracting or deriving new forms of data will be necessary when it
comes to handling qualitative ‘textual’ data. As stated in the ‘Data
literacy’ section, if you have textual data you will generally always
need to transform this into various categorical or quantitative forms,
unless its role is simply to provide value as an annotation (such as a
quoted caption or label). Some would argue that qualitative
visualisation involves special methods for the representation of data. I
would disagree. I believe the unique challenge of working with
textual data lies with the task of transforming the data: visually
representing the extracted and derived properties from textual data
involves the same suite of representation options (i.e. chart types) that
would be useful for portraying analysis of any other data types.
Here is a breakdown of some of the conversions, calculations and
extractions you could apply to textual data. Some of these tasks can
be quite straightforward (e.g. Using the LEN function in Excel to
determine the number of characters) while others are more technical
and will require more sophisticated tools or programmes dedicated to
handling textual data.
Categorical conversions:
Identify keywords or summary themes from text and convert
these into categorical classifications.
Identify and flag up instances of certain cases existing or
otherwise (e.g. X is mentioned in this passage).
Identify and flag up the existence of certain relationships (e.g. A
and B were both mentioned in the same passage, C was always
mentioned before D).
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Use natural language-processing techniques to determine
sentiments, to identify specific word types (nouns, verbs,
adjectives) or sentence structures (around clauses and
punctuation marks).
With URLs, isolate and extract the different components of
website address and sub-folder locations
Quantitative conversions:
Calculate the frequency of certain words being used.
Analyse the attributes of text, such as total word count, physical
length, potential reading duration.
Count the number of sentences or paragraphs, derived from the
frequency of different punctuation marks.
Position the temporal location of certain words/phrases in relation to
other words/phrases or compared to the whole (e.g. X was mentioned
at 1m51s).
Position the spatial location of certain words/phrases in relation to
other words/phrases or compared to the whole.
A further challenge that falls under this ‘converting’ heading will
sometimes emerge when you are working with data supplied by others in
spreadsheets. This concerns the obstacles created when trying to analyse a
data that has been formatted visually, perhaps in readiness for printing. If
you receive data in this form you will need to unpack and reconstruct it
into the normalised form described earlier, comprising all records and
fields included in a single table.
Any merged cells need unmerging or removing. You might have a
heading that is common to a series of columns. If you see this,
unmerge it and replicate the same heading across each of the relevant
columns (perhaps appending an index number to each header to
maintain some differentiation). Cells that have visual formatting like
background shading or font attributes (bold, coloured) to indicate a
value or status are useful when observing and reading the data, but for
analysis operations these properties are largely invisible. You will
need to create new values in actual data form that are not visual
(creating categorical values, say, or status flags like ‘yes’ or ‘no’) to
recreate the meaning of the formats. The data provided to you – or
that you create – via a spreadsheet does not need to be elegant in
appearance, it needs to be functional.
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Transform to create: This task is something I refer to as the hidden
cleverness, where you are doing background thinking to form new
calculations, values, groupings and any other mathematical or manual
treatments that really expand the variety of data available.
A simple example might involve the need to create some percentage
calculations in a new field, based on related quantities elsewhere
within your existing data. Perhaps you have pairs of ‘start date’ and
‘end date’ values and you need to calculate the duration in days for all
your records. You might use logic formula to assist in creating a new
variable that summarises another – maybe something like (in
language terms) IF Age < 18 THEN status = “Child”, ELSE status = “Adult”. Alternatively, you might want to create a calculation that standardised some quantities’ need to source base population figures for all the relevant locations in your data in order to convert some quantities into ‘per capita’ values. This would be particularly necessary if you anticipate wanting to map the data as this will ensure you are facilitating legitimate comparisons. Transform to consolidate: This involves bringing in additional data to help expand (more variables) or append (more records) to enhance the editorial and representation potential of your project. An example of a need to expand your data would be if you had details about locations only at country level but you wanted to be able to group and aggregate your analysis at continent level. You could gather a dataset that holds values showing the relationships between country and continent and then add a new variable to your dataset against which you would perform a simple lookup operation to fill in the associated continent values. Consolidating by appending data might occur if you had previously acquired a dataset that now had more or newer data (specifically, additional records) available to bring it up to date. For instance, you might have started some analysis on music record sales up to a certain point in time, but once you’d actually started working on the task another week had elapsed and more data had become available. Additionally, you may start to think about sourcing other media assets to enhance your presentation options, beyond just gathering extra data. You might anticipate the potential value for gathering photos (headshots of the people in your data), icons/symbols (country flags), links to articles (URLs), or videos (clips of goals scored). All of these would contribute to broadening the scope of your annotation options. Even though there is a while yet until we reach that particular layer of 188 design thinking, it is useful to start contemplating this as early possible in case the collection of these additional assets requires significant time and effort. It might also reveal any obstacles around having to obtain permissions for usage or sufficiently high quality media. If you know you are going to have to do something, don’t leave it too late – reduce the possibility of such stresses by acting early. 4.6 Data Exploration The examination task was about forming a deep acquaintance with the physical properties and meaning of your data. You now need to interrogate that data further – and differently – to find out what potential insights and qualities of understanding it could provide. Undertaking data exploration will involve the use of statistical and visual techniques to move beyond looking at data and begin to start seeing it. You will be directly pursuing your initially defined curiosity, to determine if answers exist and whether they are suitably enlightening in nature. Often you will not know for sure whether what you initially thought was interesting is exactly that. This activity will confirm, refine or reject your core curiosity and perhaps, if you are fortunate, present discoveries that will encourage other interesting avenues of enquiry. ‘After the data exploration phase you may come to the conclusion that the data does not support the goal of the project. The thing is: data is leading in a data visualization project – you cannot make up some data just to comply with your initial ideas. So, you need to have some kind of an open mind and “listen to what the data has to say”, and learn what its potential is for a visualisation. Sometimes this means that a project has to stop if there is too much of a mismatch between the goal of the project and the available data. In other cases this may mean that the goal needs to be adjusted and the project can continue.’ Jan Willem Tulp, Data Experience Designer To frame this process, it is worth introducing something that will be covered in Chapter 5, where you will consider some of the parallels between visualisation and photography. Before committing to take a photograph you must first develop an appreciation of all the possible 189 viewpoints that are available to you. Only then can you determine which of these is best. The notion of ‘best’ will be defined in the next chapter, but for now you need to think about identifying all the possible viewpoints in your data – to recognise the knowns and the unknowns. Widening the Viewpoint: Knowns and Unknowns At a news briefing in February 2002, the US Secretary of Defense, Donald Rumsfeld, delivered his infamous ‘known knowns’ statement: Reports that say that something hasn’t happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – the ones we don’t know we don’t know. And if one looks throughout the history of our country and other free countries, it is the latter category that tend to be the difficult ones. There was much commentary about the apparent lack of elegance in the language used and criticism of the muddled meaning. I disagree with this analysis. I thought it was probably the most efficient way he could have articulated what he was explaining, at least in written or verbal form. The essence of Rumsfeld’s statement was to distinguish awareness of what is knowable about a subject (what knowledge exists) from the status of acquiring this knowledge. There is a lot of value to be gained from using this structure (Figure 4.8) to shape your approach to thinking about data exploration. The known knowns are aspects of knowledge about your subject and about the qualities present in your data that you are aware of – you are aware that you know these things. The nature of these known knowns might mean you have confidence that the origin curiosity was relevant and the available insights that emerged in response are suitably interesting. You cannot afford to be complacent, though. You will need to challenge yourself to check that these curiosities are still legitimate and relevant. To support this, you should continue to look and learn about the subject through research, topping up your awareness of the most potentially 190 relevant dynamics of the subject, and continue to interrogate your data accordingly. Additionally, you should not just concentrate on this potentially quite narrow viewpoint. As I mentioned earlier, it is important to give yourself as broad a view as possible across your subject and its data to optimise your decisions about what other interesting enquiries might be available. This is where you need to consider the other quadrants in this diagram. Figure 4.8 Making Sense of the Known Knowns On occasion, though I would argue rarely, there may be unknown knowns, things you did not realise you knew or perhaps did not wish to acknowledge that you knew about a subject. This may relate to previous 191 understandings that have been forgotten, consciously ignored or buried. Regardless, you need to acknowledge these. For the knowledge that has yet to be acquired – the known unknowns and the even more elusive unknown unknowns – tactics are needed to help plug these gaps as far, as deep and as wide as possible. You cannot possibly achieve mastery of all the domains you work with. Instead, you need to have the capacity and be in position to turn as many unknowns as possible into knowns, and in doing so optimise your understanding of a subject. Only then will you be capable of appreciating the full array of viewpoints the data offers. To make the best decisions you first need to be aware of all the options. This activity is about broadening your awareness of the potentially interesting things you could show – and could say – about your data. The resulting luxury of choice is something you will deal with in the next stage. Exploratory Data Analysis As I have stated, the aim throughout this book is to create a visualisation that will facilitate understanding for others. That is the end goal. At this stage of the workflow the deficit in understanding lies with you. The task of addressing the unknowns you have about a subject, as well as substantiating what knowns already exist, involves the use of exploratory data analysis (EDA). This integrates statistical methods with visual analysis to offer a way of extracting deeper understanding and widening the view to unlock as much of the potential as possible from within your data. The chart in Figure 4.9 is a great demonstration of the value in combining statistical and visual techniques to understand your data better. It shows the results of nearly every major and many minor (full) marathon from around the world. On the surface, the distribution of finishing times reveals the common bell shape found in plots about many natural phenomenon, such as the height measurements of a large group of people. However, when you zoom in closer the data reveals some really interesting threshold patterns for finishing times on or just before the three-, four- and five-hour marks. You can see that the influence of runners setting themselves targets, often rounded to the hourly milestones, genuinely 192 appeared to affect the results achieved. Figure 4.9 What Good Marathons and Bad Investments Have in Common Although statistical analysis of this data would have revealed many interesting facts, these unique patterns were only realistically discoverable through studying the visual display of the data. This is the essence of EDA but there is no instruction manual for it. As John Tukey, the father of EDA, described: ‘Exploratory data analysis is an attitude, a flexibility, and a reliance on display, not a bundle of techniques’. There is no single path to undertaking this activity effectively; it requires a number of different technical, practical and conceptual capabilities. Instinct of the analyst: This is the primary matter. The attitude and flexibility that Turkey describes are about recognising the importance of the analyst’s traits. Effective EDA is not about the tool. There are many vendors out there pitching their devices as the magic option where we just have to ‘point and click’ to uncover a deep discovery. Technology inevitably plays a key role in facilitating this endeavour but the value of a good analyst cannot be underestimated: it is arguably more influential than the differentiating characteristics between one tool and the next. In the absence of a defined procedure for conducting EDA, an analyst needs to possess the capacity to recognise and pursue the scent of enquiry. A good analyst will have that special blend of natural inquisitiveness and the sense to know what approaches (statistical or visual) to employ and when. Furthermore, when these traits collide with a strong subject 193 knowledge this means better judgments are made about which findings from the analysis are meaningful and which are not. Reasoning: Efficiency is a particularly important aspect of this exploration stage. The act of interrogating data, waiting for it to volunteer its secrets, can take a lot of time and energy. Even with smaller datasets you can find yourself tempted into trying out myriad combinations of analyses, driven by the desire to find the killer insight in the shadows. ‘At the beginning, there’s a process of “interviewing” the data – first evaluating their source and means of collection/aggregation/computation, and then trying to get a sense of what they say – and how well they say it via quick sketches in Excel with pivot tables and charts. Do the data, in various slices, say anything interesting? If I’m coming into this with certain assumptions, do the data confirm them, or refute them?’ Alyson Hurt, News Graphics Editor, NPR Reasoning is an attempt to help reduce the size of the prospect. You cannot afford to try everything. There are so many statistical methods and, as you will see, so many visual means for seeing views of data that you simply cannot expect to have the capacity to try to unleash the full exploratory artillery. EDA is about being smart, recognising that you need to be discerning about your tactics. In academia there are two distinctions in approaches to reasoning – deductive and inductive – that I feel are usefully applied in this discussion: Deductive reasoning is targeted: You have a specific curiosity or hypothesis, framed by subject knowledge, and you are going to interrogate the data in order to determine whether there is any evidence of relevance or interest in the concluding finding. I consider this adopting a detective’s mindset (Sherlock Holmes). Inductive reasoning is much more open in nature: You will ‘play around’ with the data, based on your sense or instinct about what might be of interest, and wait and see what emerges. In some ways this is like prospecting, hoping for that moment of serendipity when you unearth gold. In this exploration process you ideally need to accommodate both 194 approaches. The deductive process will focus on exploring further targeted curiosities, the inductive process will give you a fighting chance of finding more of those slippery ‘unknowns’, often almost by accident. It is important to give yourself room to embark on these somewhat less structured exploratory journeys. I often think about EDA in the context of a comparison with the challenge of a ‘Where’s Wally?’ visual puzzle. The process of finding Wally feels somewhat unscientific. Sometimes you let your eyes race around the scene like a dog who has just been let out of the car and is torpedoing across a field. However, after the initial burst of randomness, perhaps subconsciously, you then go through a more considered process of visual analysis. Elimination takes place by working around different parts of the scene and sequentially declaring ‘Wally-free’ zones. This aids your focus and strategy for where to look next. As you then move across each mini- scene you are pattern matching, looking out for the giveaway characteristics of the boy wearing glasses, a red-and-white-striped hat and jumper, and blue trousers. The objective of this task is clear and singular in definition. The challenge of EDA is rarely that clean. There is a source curiosity to follow, for sure, and you might find evidence of Wally somewhere in the data. However, unlike the ‘Where’s Wally?’ challenge, in EDA you have the chance also to find other things that might change the definition of what qualifies as an interesting insight. In unearthing other discoveries you might determine that you no longer care about Wally; finding him no longer represents the main enquiry. Inevitably you are faced with a trade-off between spare capacity in time and attention and your own internal satisfaction that you have explored as many different angles of enquiry as possible. Chart types: This is about seeing the data from all feasible angles. The power of the visual means that we can easily rely on our pattern- matching and sense-making capabilities – in harmony with contextual subject knowledge – to make observations about data that appear to have relevance. The data representation gallery that you will encounter in Chapter 6 presents nearly 50 different chart types, offering a broad repertoire of options for portraying data. The focus of the collection is on chart types that could be used to communicate to others. However, within 195 this gallery there are also many chart types that help with pursuing EDA. In each chart profile, indications are given for those chart types that be particularly useful to support your exploratory activity. As a rough estimate, I would say about half of these can prove to be great allies in this stage of discovery. The visual methods used in EDA do not just involve charting, they also involve selective charting – smart charting, ‘smarting’ if you like? (No, Andy, nobody likes that). Every chart type presented in the gallery includes helpful descriptions that will give you an idea of their role and also what observations – and potential interpretations – they might facilitate. It is important to know now that the chart types are organised across five main families (categorical, hierarchical, relational, temporal, and spatial) depending on the primary focus of your analysis. The focus of your analysis will, in turn, depend on the types of data you have and what you are trying to see. ‘I kick it over into a rough picture as soon as possible. When I can see something then I am able to ask better questions of it – then the what- about-this iterations begin. I try to look at the same data in as many different dimensions as possible. For example, if I have a spreadsheet of bird sighting locations and times, first I like to see where they happen, previewing it in some mapping software. I’ll also look for patterns in the timing of the phenomenon, usually using a pivot table in a spreadsheet. The real magic happens when a pattern reveals itself only when seen in both dimensions at the same time.’ John Nelson, Cartographer, on the value of visually exploring his data Research: I have raised this already but make no apology for doing so again so soon. How you conduct research and how much you can do will naturally depend on your circumstances, but it is always important to exploit as many different approaches to learning about the domain and the data you are working with. As you will recall, the middle stage of forming understanding – interpreting – is about viewers translating what they have perceived from a display into meaning. They can only do this with domain knowledge. Similarly, when it comes to conducting exploratory analysis using visual methods, you might be able to perceive the charts you make, but without possessing or acquiring sufficient domain knowledge you will not know if what you are seeing is meaningful. Sometimes the consequence of this exploratory data analysis will only mean you 196 have become better acquainted with specific questions and more defined curiosities about a subject even if you possibly do not yet have any answers. The approach to research is largely common sense: you explore the places (books, websites) and consult the people (experts, colleagues) that will collectively give you the best chance of getting accurate answers to the questions you have. Good communication skills, therefore, are vital – it is not just about talking to others, it is about listening. If you are in a dialogue with experts you will have to find an approach that allows you to understand potentially complicated matters and also cut through to the most salient matters of interest. Statistical methods: Although the value of the univariate statistical techniques profiled earlier still applies here, what you are often looking to undertake in EDA is multivariate analysis. This concerns testing out the potential existence of a correlation between quantitative variables as well as determining the possible causation variables – the holy grail of data analysis. Typically, I find statistical analysis plays more of a supporting role during much of the exploration activity rather than a leading role. Visual techniques will serve up tangible observations about whether data relationships and quantities seem relevant, but to substantiate this you will need to conduct statistical tests of significance. One of the main exceptions is when dealing with large datasets. Here the first approach might be more statistical in nature due to the amount of data obstructing rapid visual approaches. Going further, algorithmic approaches – using techniques like machine learning – might help to scale the task of statistically exploring large dimensions of data – and the endless permutations they offer. What these approaches gain in productivity they clearly lose in human quality. The significance of this should not be underestimated. It may be possible to take a blended approach where you might utilise machine learning techniques to act as an initial battering ram to help reduce the problem, identifying the major dimensions within the data that might hold certain key statistical attributes and then conducting further exploration ‘by hand and by eye’. Nothings: What if you have found nothing? You have hit a dead end, discovering no significant relationships and finding nothing interesting about the shape or distribution of your data. What do you do? In these situations you need to change your mindset: nothing is usually something. Dead ends and discovering blind alleys are good 197 news because they help you develop focus by eliminating different dimensions of possible analysis. If you have traits of nothingness in your data or analysis –gaps, nulls, zeroes and no insights – this could prove to be the insight. As described earlier, make the gaps the focus of your story. There is always something interesting in your data. If a value has not changed over time, maybe it was supposed to – that is an insight. If everything is the same size, that is the story. If there is no significance in the quantities, categories or spatial relationships, make those your insights. You will only know that these findings are relevant by truly understanding the context of the subject matter. This is why you must make as much effort as possible to convert your unknowns into knowns. ‘My main advice is not to be disheartened. Sometimes the data don’t show what you thought they would, or they aren’t available in a usable or comparable form. But [in my world] sometimes that research still turns up threads a reporter could pursue and turn into a really interesting story – there just might not be a viz in it. Or maybe there’s no story at all. And that’s all okay. At minimum, you’ve still hopefully learned something new in the process about a topic, or a data source (person or database), or a “gotcha” in a particular dataset – lessons that can be applied to another project down the line.’ Alyson Hurt, News Graphics Editor, NPR Not always needed: It is important to couch this discussion about exploration in pragmatic reality. Not all visualisation challenges will involve much EDA. Your subject and your data might be immediately understandable and you may have a sufficiently broad viewpoint of your subject (plenty of known knowns already in place). Further EDA activity may have diminishing value. Additionally, if you are faced with small tables of data this simply will not warrant multivariate investigation. You certainly need to be ready and equipped with the capacity to undertake this type of exploration activity when it is needed, but the key point here is to judge when. Summary: Working with Data This chapter first introduced key foundations for the requisite data literacy 198 involved in visualisation, specifically the importance of the distinction between normalised and cross-tabulated datasets as well as the different types of data (using the TNOIR mnemonic): Textual (qualitative): e.g. ‘Any other comments?’ data submitted in a survey. Nominal (qualitative): e.g. The ‘gender’ selected by a survey participant. Ordinal (qualitative): e.g. The response to a survey question, based on a scale of 1 (unhappy) to 5 (very happy). Interval (quantitative): e.g. The shoe size of a survey participant. Ratio (quantitative): e.g. The age of a survey participant in years. You then walked through the four steps involved in working with data: Acquisition Different sources and methods for getting your data. Curated by you: primary data collection, manual collection and data foraging, extracted from pdf, web scraping (also known as web harvesting). Curated by others: issued to you, downloaded from the Web, system report or export, third-party services, APIs. Examination Developing an intimate appreciation of the characteristics of this critical raw material: Physical properties: type, size, and condition. Meaning: phenomenon, completeness. Transformation Getting your data into shape, ready for its role in your exploratory analysis and visualisation design: Clean: resolve any data quality issues. Create: consider new calculations and conversions. Consolidate: what other data (to expand or append) or other assets could be sought to enhance your project? Exploration Using visual and statistical techniques to see the data’s qualities: what insights does it reveal to you as you deepen your familiarity with it? 199 Tips and Tactics Perfect data (complete, accurate, up to date, truly representative) is an almost impossible standard to reach (given the presence of time constraints) so your decision will be when is good enough, good enough: when do diminishing returns start to materialise? Do not underestimate the demands on your time; working with data will always be consuming of your attention and effort: Ensure you have built plenty of time into your handling of this data stage. Be patient and persevere. Be disciplined: it is easy to get swallowed up in the potential hunt for discovering things from your data, attempting to explore every possible permutation. If your data does not already have a unique identifier it is often worth creating one to track your data preparation process. This is especially helpful if you need to preserve or revert to a very specific ordering of your data (e.g. if the rows have been carefully arranged in order to undertake cross-row calculations like cumulative or sub-totals). Clerical tasks like file management are important: maintain backups of each major iteration of data, employ good file organisation of your data and other assets, and maintain logical naming conventions. Data management practices around data security and privacy will be important in the more sensitive/confidential cases. Keep notes about where you have sourced data, what you have done with it, any assumptions or counting rules you have applied, ideas you might have for transforming or consolidating, issues/problems, things you do not understand. To learn about your data, its meaning and the subject matter to which it relates, you should build in time to undertake research in order to equip yourself suitably with domain knowledge. Anticipate and have contingency plans for the worst-case scenarios for data, such as the scarcity of data availability, null values, odd distributions, erroneous values, long values, bad formatting, data loss. Communicate. If you do not know anything about your data, ask: do not assume or stay ignorant. And then listen: always pay attention to key information. Attention to detail is of paramount importance at this stage, so get into good habits early and do not cut corners. Maintain an open mind and do not get frustrated. You can only work 200 with what you have. If it is not showing what you expected or hoped for, you cannot force it to say something that is simply not there. Exploratory Data Analysis is not about design elegance. Do not waste time making your analysis ‘pretty’, it only needs to inform you. 201 5 Establishing Your Editorial Thinking It is very easy to introduce every chapter with claims that each of these reached is the important stage but you have really now reached a critical juncture. This is the place in the process where you need to start to commit to a definitive pathway. The data you gathered during Chapter 4 was shaped by your trigger curiosity. You may have found qualities in the data that you feel reveal relevant insights in response to that pursuit. Alternatively, through exploring your data and researching your subject, you may have discovered new enquiries that might actually offer more interesting perspectives. Ahead of commencing the design and development of your solution you need to decide what you are actually going to do with this data: what are you going to show your audience? This is where editorial thinking becomes important. In my view it is one of the most defining activities that separates the best visualisers from the rest, possibly even more so than technical talent or design flair. In this this chapter you will learn about what editorial thinking means, the role it plays, what decisions you need to make and how you might do so. 5.1 What is Editorial Thinking? You will have noticed the common thread of curiosity that weaves its way through the preparatory activities of this workflow process. From the opening curiosity that initiated your work, you then effectively sought, gathered and became acquainted with your data in pursuit of some kind of answer. In this third stage, you will need to make some decisions. The essence of editorial thinking is demonstrating a discerning eye for what you are going to portray visually to your audience; the matter of how follows next. This stage is the critical bridge between your data work and your design work. In the first chapter I described how a single context can hold several 202 legitimate views of the truth. The glass that is half full of water is also half empty. It is also half full of air. Its water contents might be increasing or decreasing. Depending on your perspective, there are several legitimate ways of portraying this situation. In a nutshell, editorial thinking is about deciding which of the many viable perspectives offered by your data you will decide to focus on. To translate this to data visualisation, assume you have data that breaks down total organisational spend across many geographic regions over time. Your profiling of your audience has already informed your thinking that the main interest is in how this has changed over time. But at this point, having looked at the data closely, you have found some really interesting patterns in the spatial analysis. What are you going to do? Are you going to show your audience how this spend compares by region on a map, having now established that this might be of interest to them, or are you going to focus on still showing how it has changed over time by region? Perhaps you could show both. Do you need to show all the regions and include all the available time periods or just focus on some specific key moments? You have got to decide what you are going to do because you are about to face the task of picking chart types, deciding on a layout, possible interactivity, and many other presentation matters. When trying to explain the role of editorial thinking I find it helpful to consider some of the parallels that exists between data visualisation and photography, or perhaps more specifically, photojournalism. By translating into data visualisation some of the decisions involved in taking a photograph, you will find useful perspectives to help shape your editorial thinking. In turn this will have a huge bearing on the design choices that follow. There are three particular perspectives to consider: angle, framing and focus. ‘A photo is never an objective reflection, but always an interpretation of reality. I see data visualization as sort of a new photojournalism – a highly editorial activity.’ Moritz Stefaner, Truth & Beauty Operator Angle Think of a chart as being a photograph of data. As with a photograph, in visualisation you cannot show everything at once. A panoramic 360° view 203 of data is impossible to display at any moment and certainly not through the window of a single chart. You must pick an angle. ‘When the data has been explored sufficiently, it is time to sit down and reflect – what were the most interesting insights? What surprised me? What were recurring themes and facts throughout all views on the data? In the end, what do we find most important and most interesting? These are the things that will govern which angles and perspectives we want to emphasise in the subsequent project phases.’ Moritz Stefaner, Truth & Beauty Operator In photography the angle would be formed by the position from where you are standing when taking a shot. In visualisation this relates to the angle of analysis you intend to show: what are you measuring and by which dimension(s) are you breaking it down? Are you going to show how product sales have changed over time, or how sales look organised by regional hierarchically or how they compare on a map and over time? There are many different angles you could choose. You could also choose to show data from multiple different angles using several charts presented together. Your key consideration in determining each angle is whether it is relevant and sufficient. ‘It requires the discipline to do your homework, the ability to quiet down your brain and be honest about what is interesting.’ Sarah Slobin, Visual Journalist Relevant: Why is it worth providing a view of your data from this angle and not another one? Why is this angle of analysis likely to offer the most relevant and compelling window into the subject for your intended audience? Is it still relevant in light of the context of the origin curiosity – that is, have definitions evolved since familiarising yourself with the data, learning about its potential qualities as well as researching the subject at large? The judgement of relevance would be similar to the notion of newsworthiness in journalism. In that context, terms like timeliness, proximity, novelty, human interest and current prominence are all ingredients that shape what ultimately becomes news content. The ecosystem in which your work is consumed is likely to be much narrower 204 in size and diversity than it is for a newspaper, for example. Issues of human interest and novelty will seldom have a bearing on your judgement of relevance. Therefore, I believe it is realistic to reduce the list of factors that shape your thinking about relevance to three: What does your intended audience want or need to know? The various characteristics of your audience’s profile, matters discussed in Chapter 1 (accessible design) and Chapter 3 (contextual circumstances), should provide a good sense of this. Sometimes, you can simply ask the members of your intended audience: you might know who they are personally or at least be able to gather information about their needs. On other occasions, with a larger audience, you might need to consider creating personas: a small number of imagined identities that may be demographically representative of the types of viewer you expect to target. Ask yourself, if you were them, what would you want to know? What makes something relevant in your context? Part of your judgement will be to consider whether relevance is a product of the normal or the exceptional; often the worthiness of an item of news is based on it being exceptional rather than going through the repeated reporting of normality. Reciting the famous journalistic aphorism, you need to determine if you are reporting news of ‘dog bites man!’ or ‘man bites dog!’. A lack of relevance is a curse that strikes a lot of visualisation work. What you often see is evidence of data that has been worked up into a visual output just because it is available and just because visual things are appealing. There is almost a scattergun approach in hoping that someone, somewhere will find a connection to justify it as relevant. What do you want your audience to know? You might have the control to decide. Although you respect the possible expressed needs of your audience you might actually be better placed to determine what is truly relevant. Depending on the context, and your proximity to the subject and its data, you might have the autonomy to dictate on what it is you want to say, more so than what you think the audience want to see. Indeed, that audience may not yet know or be sufficiently domain aware to determine for itself what is relevant or otherwise. Sufficient: This is about judging how many angles you need. If a chart (generally) offers a single angle into your data, is that sufficiently representative of what you wish to portray? As I said earlier, you cannot 205 show everything in one chart. Maybe you need multiple charts offering a blend of different angles of analysis to sufficiently represent the most interesting dimensions of the subject matter. Perhaps showing a view of your data over time needs to be supplemented by a spatial view to provide the context for any interpretations. It is easy to find yourself being reluctant to commit to just a singular choice of angle. Even in a small dataset, there are typically multiple possible angles of analysis you could conduct. It is often hard to ignore the temptation of wanting to include multiple angles to serve more people’s interests. It is important not to fall into the trap of thinking that if you throw more and more additional angles of analysis into your work you will automatically enrich that work. Just because you have 100 photographs of your holiday, that does not mean you should show me them all. When I reflect on some of the work I have created down the years, I wish I had demonstrated better selection discipline – a greater conviction to exclude angles – to avoid additional content creeping in just because it was available. I often found it far too easy to see everything as being potentially interesting. And I still do (it’s the curse of the analyst). The real art is to find just enough of those angles that respond to the core essence of your – or your inherited – curiosity. ‘I think this is something I’ve learned from experience rather than advice that was passed on. Less can often be more. In other words, don’t get carried away and try to tell the reader everything there is to know on a subject. Know what it is that you want to show the reader and don’t stray from that. I often find myself asking others “do we need to show this?” or “is this really necessary?” Let’s take it out.’ Simon Scarr, Deputy Head of Graphics, ThomsonReuters Framing The next perspective to define about your editorial thinking contributes to the refinement of the angles you have selected. This concerns framing decisions. In photographic parlance this relates to choices about the field of view: what will be included inside the frame of the photograph and what will be left out? 206 Just like a photographer, a visualiser must demonstrate careful judgement about what to show, what not to show, and how to show it. This is effectively a filtering decision concerned with which data to include and exclude: All category values, or just a select few? All quantitative values or just those over a certain threshold? All data or just those between a defined start and end date period? Naturally, the type and extent of the framing you might need to apply will be influenced by the nature of your trigger curiosity, as well as factors like the complexity of the subject matter and the amount of data available to show. Further considerations like the setting (need rapid insights or OK for deeper, more prolonged engagement?) and output format will also have a bearing on this matter. One of the key motives of framing is to remove unnecessary clutter – there is only so much that can be accommodated in a single view before it becomes too busy, too detailed, and too small in resolution. There is only so much content your audience will likely be willing and able to process. Inevitably, a balance must be struck to find the most representative view of your content. If you zoom in, filtering away too much of the content, it might hide the important context required for perceiving values. Conversely, if you avoid filtering your content you may fail to make visible the most salient discoveries. Focus The third component of editorial thinking concerns what you might choose to focus on. This is not a function of filtering – that is the concern of framing – it is about emphasising what is more important in contrast to what is less important. The best photographs are able to balance light and colour, not just setting the mood of a situation but illuminating key elements within the frame that help to create depth. They provide a sense of visual hierarchy through their depth as well as the sizing and arrangement of each form. What needs to be brought into view in the foreground, left in the mid- ground, and maybe relegated to the background simply for context or 207 orientation? What needs to be bigger and more prominent and what can be less so? Whereas framing judgements were about reducing clutter, this is about reducing noise. If everything in a visualisation is shouting, nothing is heard; if everything is in the foreground, nothing stands out; if everything is large, nothing is dominant. Decisions about focus primarily concern the development of explanatory visualisations, because creating such a focus – surfacing insights through the astute use of colour or annotated accentuation – is a key purpose for that type of experience. Beyond colour, focus can be achieved through composition choices such as the way elements are more prominently sized and located or the way contents are positioned within a view. 5.2 The Influence of Editorial Thinking It is important to ground this discussion by explaining practically how these editorial perspectives will apply to your workflow process and, in particular, influence your design thinking. I described a chart as being like a photograph of the data, displaying a visual answer to a data-driven curiosity. Determining the choice of chart (technically, ‘data representation’) is just one part of the overall anatomy of a data visualisation. There are choices to be made about four other design layers, namely features of interactivity, annotation, colour and composition. Your decisions across this visualisation design anatomy are influenced, in a large way, by the editorial definitions you have will make about angle, framing and focus. They might not lead directly or solely to the final choices – there are many other factors to consider, as you have seen – but they will signpost the type of editorial qualities the visualisation will need to accommodate. Let’s look at two illustrations of the connection between editorial and design thinking to explain this. Example 1: The Fall and Rise of us Inequality The first example (Figure 5.1) is a chart taken from an article published in 208 the ‘Planet Money: The Economy Explained’ section of the US-based National Public Radio (NPR) website. The article is titled ‘The Fall and Rise of U.S. Inequality in 2 Graphs’. As the title suggests the full article includes two charts, but I just want to focus on the second one for the purpose of this illustration. Figure 5.1 The Fall and Rise of U.S. Inequality, in Two Graphs 209 Editorial Perspectives Let’s assess the editorial perspectives of angle, framing and focus as demonstrated by this work. Angle: The main angle of analysis can be expressed as: ‘What is the relationship between two quantitative measures (average income for the bottom 90% and for the top 1% of earners) and how has this changed over time (year)?’. This angle would be considered relevant because the relationship between the haves and the have-nots is a key indicator of wealth distribution. It is a topical and suitable choice of 210 analysis to include with any discussion about inequality in the USA. As I mentioned there is a second chart presented so it would be reasonable to say that the two sufficiently cover the necessary angles to support the article. Framing: The parameters that define the inclusion and exclusion of data in the displayed analysis involve filters for time period (1917 to 2012) and country (just for the USA). The starting point of the data commencing from 1917 may reflect a simple arbitrary cut-off point or a significant milestone in the narrative. More likely, it probably represents the earliest available data. One always has a basic desire to always want every chart to include the most up-to-date view of data. While it only reaches as far forward in time as 2012 (despite publication in 2015) the analysis is of such historical depth that it should be considered suitably representative of the subject matter. To just focus on the USA is entirely understandable. Focus: The visualisation includes a ‘time slider’ control that allows users to move the focus incrementally through each year, colouring each consecutive yearly marker for emphasis. The colours are organised into three classifications to draw particular attention to two main periods of noticeably different relationships between the two quantitative measures. Influence on Design Choices How do these identified editorial perspectives translate directly into design thinking? As you will learn in Chapters 6–10 any visualisation comprises five layers of design. Let’s have a look at how they might be influenced by editorial thinking. Data representation: The angle is what fundamentally shapes the data representation approach. In lay terms, it determines which chart type is used. In this example, the defined angle is to show the relationship between two quantitative measures over time (average income for bottom 90% vs. top 1% of earners). A suitable chart type to portray this visually is the scatter plot (as selected). As you will learn in the next chapter, the scatter plot belongs to the ‘relational’ family of chart types. Given there was also a dimension of time expressed in this angle, a chart type from the ‘temporal’ family of charts could have been used but with the main emphasis being on showing the relationships the scatter plot was the better choice. The 211 framing perspective defines what data will be included in the chosen chart: only data for the USA and the time period 1917–2012 is displayed. Interactivity: As you will discover in Chapter 7, the role of interactivity is to enable adjustments to what data is displayed and how it is displayed. The sole feature of interactivity in this project is offered through the ‘time slider’ control, which sequences the unveiling of the data points year by year in either a manual or automated fashion. The inclusion of such interactivity can be influenced by the editorial decisions concerning focus: unveiling the yearly values sequences the emphasis on the position – and emerging pattern – of each consecutive value. Annotation: The primary chart annotations on show here are the two arrows and associated captions, drawing attention to the two prominent patterns that support the general fall and then rise of inequality. Again, the inclusion of the captions would be a consequence of editorial thinking (focus) determining these respective patterns in the data should be emphasised to the viewer. Colour: As you will learn about in Chapter 9, one of the key applications of colour is to support editorial salience – how to emphasise content and direct the eye. As before, editorial focus would influence the decision to deploy four colour states within the chart: a default colour to show all points at the start of the animation and then three different emerging colours to separate the three clustered groups visually. Note that the final colour choices of red, green and orange tones are not directly informed by editorial thinking, as the identified value of using four different ones to draw out the focus is what drives this choice. Composition: This concerns all of the physical layout, shape and size decisions. In this example, the dimensions of editorial thinking have had limited influence over the composition choices. Although, recognising again that there are two charts in the full article, the focus perspective would have likely informed the decision to sequence the ordering of the charts: what made better sense to go first or last and why? Example 2: Why Peyton Manning’s Record Will Be Hard to Beat 212 In this second example, published on ‘TheUpshot’ section of the New York Times website, there are three charts presented in an article titled ‘Why Peyton Manning’s Record Will Be Hard to Beat’. Here I will look at all three charts. Editorial Perspectives Again, let’s assess the editorial perspectives of angle, framing and focus as demonstrated by this work. Figure 5.2 Why Peyton Manning’s Record Will Be Hard to Beat Angle: The first chart (Figure 5.2) displays the angle of analysis expressed as ‘How have quantitative values (NFL touchdown passes) broken down by category (quarterbacks) changed over time (year)?’. This analysis was relevant at the time due to the significance of Peyton Manning setting a new record for NFL quarterback touchdown passes, an historic moment and, according to the article, ‘evidence of how much the passing game has advanced through the history of the game’. Inspired by this achievement, the question posed by this article overall is whether the record will ever be bettered – which would have likely been the origin curiosity that drove the visualisation project in the first place. The article was time relevant because the record had just been achieved. On its own, this analysis would be deemed insufficient to support the overarching enquiry, as evidenced by the inclusion of two further charts that we will look at shortly. Framing: The parameters that define the inclusion and exclusion 213 framing relate to the time period (1930 to 19 October 2014) and qualifying quantitative threshold (minimum of 30 touchdown passes). It is representative of the truth at the moment of production (i.e. up to 19 October 2014) though clearly the data would no longer be up to date as soon as the next round of games took place. The judgment of the 30 touchdown passes threshold would either be informed by knowledge of the sport (and 30 TDs being a common measure) or more likely influenced by the shape of the data for every quarterback, indicating that it was a logical cut-off value. Focus: The chart emphasises the record holder as well as the other current players in order to orientate the significance of the achievement and to highlight other contemporary players who could have a chance of pursuing this record. It also emphasises previous record holders or noted players to show just how special the new record is. If you want to know the achievements of any other player, their career ‘lines’ and values come into focus through mouseover- driven interactivity. Figure 5.3 Why Peyton Manning’s Record Will Be Hard to Beat In the second chart (Figure 5.3), the same definitions stand for the angle and framing, but the focus has changed. This chart shows the same angle of analysis as seen in the first chart but is now composed of several small repeated charts, each one focusing on the career trajectories of a selected previous record holder. Focus: Colour is used to emphasise the previous record-holding players’ career lines with an illuminating background banding used to display the duration/era of their record standing. Value labels show 214 the number of touchdowns achieved. The final chart (Figure 5.4) has many similarities with the first chart. Once again it maintains the same consistent definition for framing and it has the same focus as the first chart but now there is a subtle difference in angle. Angle: This is now expressed as: ‘How have cumulative quantitative values (NFL touchdown passes) broken down by category (quarterbacks) changed over time (age)?’. The difference is the time measure being about age, not year. This is relevant as it provides an alternative view of the time measure, switching year for age to continue pursuing the curiosity over how long Manning’s record might last. More specifically it enquires if ‘the quarterback who will surpass Manning’s record is playing today?’. Incidentally, as the article concludes, it is going to be a very difficult record to beat. Figure 5.4 Why Peyton Manning’s Record Will Be Hard to Beat Influence on Design Choices Now, let’s switch the viewpoint again and look at how this visualisation’s design choices are directly informed by the editorial thinking. Data representation: As I have stated, the angle and framing dimensions are hugely influential in the reasoning of chart type 215 requirements. In each of the charts used we are being shown different perspectives around the central theme of how touchdown passes have changed over time for each qualifying quarterback. A line chart showing cumulative values for all the players was the most appropriate way of portraying this. Naturally, the line chart belongs to the ‘temporal’ family of chart types. Alternative angles of analysis may have explored the relationship angle between the measures of age and total touchdown passes. A scatter plot would have been ideal to display that angle, but the inclusion of the cumulative touchdown passes statistic, as portrayed using the line, made for a much more striking display of the trajectories. Interactivity: The only feature of interaction determined necessary here is achieved through a mouseover event in the first and third charts to reveal the names and total passes for any of the players who are presented as grey lines. This serves the interests of viewers who want to identify these background data values for ‘everyone else’. By introducing value labels only through interactivity it also means the busy-ness of labelling all values by default could be elegantly – and wisely – avoided. Annotation: This interactive labelling is a joint decision concerned with annotation. Elsewhere, the decision to include permanent annotated labels in each chart for category (player) and value (touchdown passes) provides emphasis in the first and third charts on the career achievements of Peyton Manning, the other current quarterbacks, and previous record holders. The second chart only labels the respective record holders who are the subject of each separate display. Colour: The approach to creating focus is further achieved with colour. In the main chart, emphasis is again drawn to Peyton Manning’s line, as the record holder (thick blue line), other current players (highlighted with a blue line) as well as previous record holders or noted players (dark grey line). For the second chart the light-blue coloured banding draws out the period of the records held by selected players down the years. This really helps the viewer to perceive the duration of their records. Composition: The further influence of the editorial decisions for focus would be seen through the sequencing of the charts in the article. Given the rigid dimensions of space in which the article exists, the decision to order the charts in the way they are presented will have been informed by the desired narrative that was required to 216 present analysis to support the articulated statement in the title. A closing point to make here is that the influence of editorial thinking does not just flow forwards into the design stages. Although presented as separate, consecutive stages, ‘working with data’ and ‘editorial thinking’ are strongly related and quite iterative: working with data influences your editorial perspectives; and your editorial perspectives in turn may influence activities around working with data. In the earlier stages of your development it is useful to create this sequential distinction in activities but in reality there will be much toing and froing. The data transformation activity, in particular, is essentially the key wormhole that links these two stages. Editorial definitions may trigger the need for more data to be gathered about the specific subject matter or some consolidation in detail to support the desired angles of analysis and the framing dimensions. The acquisition of new data will always then trigger a need to repeat the data examination activity. Editorial definitions might also influence the need for further calculations, groupings or general modifications to refine its preparedness for displaying the analysis. Summary: Establishing Your Editorial Thinking In this chapter you learnt about the three perspectives that underpin your editorial thinking. Angle Must be relevant in its potential interest for your audience. Must have sufficient quantities to cover all relevant views – but no more than required. Framing Applying filters to your data to determine the inclusion and exclusion criteria. Framing decisions must provide access to the most salient content but also avoid any distorting of the view of the data. 217 Focus Which features of the display to draw particular attention to? How to organise the visibility and hierarchy of the content? Tips and Tactics Data shapes the story, not the other way round: maintain this discipline throughout your work. If your data was especially riddled with gaps, perhaps consider making this the story: inverting attention towards the potential consequence, cause and meaning behind these gaps? There is always something interesting in your data: you just might not be equipped with sufficient domain knowledge to know this or it may not be currently relevant. Get to know the difference between relevant and irrelevant by researching and learning more about your subject. Communication: ask people better placed than you, who might have the subject knowledge, about what is truly interesting and relevant. A good title will often express the main curiosity or angle of analysis from the outset, giving viewers a clear idea about what the visualisation that follows will aim to answer or reveal. 218 Part C Developing Your Design Solution The Production Cycle Within the four stages of the design workflow there are two distinct parts. The first three stages, as presented in Part B of this book, were described as ‘The Hidden Thinking’ stages, as they are concerned with undertaking the crucial behind-the-scenes preparatory work. You may have completed them in terms of working through the book’s contents, but in visualisation projects they will continue to command your attention, even if that is reduced to a background concern. You have now reached the second distinct part of the workflow which involves developing your design solution. This stage follows a production cycle, commencing with rationalising design ideas and moving through to the development of a final solution. The term cycle is appropriate to describe this stage as there are many loops of iteration as you evolve rapidly between conceptual, practical and technical thinking. The inevitability of this iterative cycle is, in large part, again due to the nature of this pursuit being more about optimisation rather than an expectation of achieving that elusive notion of perfection. Trade- offs, compromises, and restrictions are omnipresent as you juggle ambition and necessary pragmatism. How you undertake this stage will differ considerably depending on the nature of your task. The creation of a relatively simple, single chart to be slotted into a report probably will not require the same rigour of a formal production cycle that the development of a vast interactive visualisation to be used by the public would demand. This is merely an outline of the most you will need to do – you should edit, adapt and participate the steps to fit with your context. There are several discrete steps involved in this production cycle: Conceiving ideas across the five layers of visualisation design. Wireframing and storyboarding designs. Developing prototypes or mock-up versions. 219 Testing. Refining and completing. Launching the solution. Naturally, the specific approach for developing your design solution (from prototyping through to launching) will vary hugely, depending particularly on your skills and resources: it might be an Excel chart, or a Tableau dashboard, an infographic created using Adobe Illustrator, or a web-based interactive built with the D3.js library. As I have explained in the book’s introduction, I’m not going to attempt to cover the myriad ways of implementing a solution; that would be impossible to achieve as each task and tool would require different instructions. For the scope of this book, I am focusing on taking you through the first two steps of this cycle – conceiving ideas and wireframing/storyboarding. There are parallels here with the distinctions between architecture (design) and engineering (execution) – I’m effectively chaperoning you through to the conclusion of your design thinking. To fulfil this, Part C presents a detailed breakdown of the many design options you will face when conceiving your visualisation design and provides you with an appreciation of the key factors that will influence the actual choices you make. The next few chapters are therefore concerned with the design thinking involved with each of these five layers of the visualisation design anatomy, namely: Chapter 6: Data representation Chapter 7: Interactivity Chapter 8: Annotation Chapter 9: Colour Chapter 10: Composition The sequencing of these layers is deliberate, based on the need to prioritise your attention: what will be included and how will it appear. Initially, you will need to make decisions about what choices to make around data representation (charts), interactivity and annotation. These are the layers that result in visible design content or features being included in your work. You will then complete your design thinking by making decisions about the appearance of these visible components, considering their colour and composition. 220 Conceiving: This will cover all your initial thinking across the various layers of design covered in the next few chapters. The focus here is on conceiving ideas based on the design options that seem to fit best with the preparatory thinking that has gone before during the first three stages. As you fine-tune your emerging design choices the benefit of sketching re- emerges, helping you articulate your thoughts into a rough visual form. As mentioned in Chapter 3, for some people the best approach involves sketching with the pen, for others it is best expressed through the medium of technical fluency. Whichever approach suits you best, it is helpful to start to translate your conceptual thinking into visual thinking, particularly when collaborating. This sketching might build on your instinctive sketched concepts from stage 1, but you should now be far better informed about the realities of your challenge to determine what is relevant and feasible. ‘I tend to keep referring back to the original brief (even if it’s a brief I’ve made myself) to keep checking that the concepts I’m creating tick all the right boxes. Or sometimes I get excited about an idea but if I talk about it to friends and it’s hard to describe effectively then I know that the concept isn’t clear enough. Sometimes just sleeping on it is all it takes to separate the good from the bad! Having an established workflow is important to me, as it helps me cover all the bases of a project, and feel confident that my concept has a sound logic.’ Stefanie Posavec, Information Designer Wireframing and storyboarding: Wireframing involves creating a low- fidelity illustration of the potential layout for those solutions that will generally occupy a single page of space, such as a simple interactive visualisation or an infographic. There is no need to be too precise just yet, you are simply mapping out what will be on your page/screen (charts, annotations), how they will be arranged and what things (interactive functions) it will do. If your project is going to require a deeper architecture, like a complex interactive, or will comprise sequenced views, like presentations, reports or animated graphics, each individual wireframe view will be weaved together using a technique called storyboarding. This maps out the relationships between all the views of your content to form an overall visual structure. Sometimes you might approach things the other way round, beginning with a high-level storyboard to provide a skeleton structure within which you can then form your more detailed thinking about the specific wireframe layouts within each page or view. 221 Prototypes/mock-ups: Whereas wireframing and storyboarding are characterised by the creation of low-fi ‘blueprints’, the development of mock-ups (for example, Figure C.1) or prototypes (the terms tend to be used interchangeably) involves advancing your decisions about the content and appearance of your proposed solution. This effectively leads to the development of a first working version that offers a reasonably close representation of what the finished product might look like. Figure C.1 Mockup designs for ‘Poppy Field’ Testing: Once you have an established prototype version, you must then seek to have it tested. Firstly, you do this ‘internally’ (i.e. by you or by collaborators/colleagues) to help iron out any obvious immediate problems. In software development parlance, this would be generally consistent with alpha testing. Naturally, beta follows alpha and this is where you will seek others to test it, evaluate it, and feedback on it. This happens regardless of the output format; it doesn’t need to be a digital, interactive project to merit being tested. There will naturally be many different aspects to your proposed solution that will need checking and evaluating. The three principles of good visualisation design that I presented earlier offer a sensible high-level structure to guide this testing: Trustworthy design testing concerns assessing the reliability of the work, in terms of the integrity of its content and performance. Are there any inaccuracies, mistakes or even deceptions? Are there any design choices that could lead to misunderstandings? Any aspects in how the data has been calculated or counted that could undermine 222 trust? If it is a digital solution, what is the speed of loading and are there any technical bugs or errors? Is it suitably responsive and adaptable in its use across different platforms? Try out various user scenarios: multiple and concurrent users, real-time data, all data vs sample data, etc. Ask the people testing your solution to try to break it so you can find and resolve any problems now. Accessible design testing relates to how intuitive or sufficiently well explained the work is. Do they understand how to read it and what all the encodings mean? Is the viewer provided with a sufficient level of assistance that would be required as per the characteristics of the intended audience? Can testers find the answers to the questions you intended them to find and quickly enough? Can they find answers to the questions they think are most relevant? Elegant design testing relates to questions such as: Is the solution suitably appealing in design? Are there any features which are redundant or superfluous design choices that are impeding the process of using the solution? Who you invite to test your work will vary considerably from one project to the next but generally you will have different possible people to consider participating in this task: Stakeholders: the ultimate customers/clients/colleagues who have commissioned the work may need to be included in this stage, if not for full testing then at least to engage them in receiving initial concept feedback. Recipients: you might choose a small sample of your target audience and invite those viewers to take part in initial beta testing. Critical friends: peers/team/colleagues with suitable knowledge and appreciation about the design process may offer a more sophisticated capacity to test out your work. You: sometimes (often) it may ultimately be down to you to undertake the testing, through either lack of access to other people or most typically a simple lack of time. To accomplish this effectively you have to find a way almost to detach yourself from the mindset of the creator and occupy that of the viewer: you need to see the wood and the trees. The timing of when to seek feedback through testing/evaluation will vary across different contexts again. Sometimes the pressure from stakeholders 223 who request to see progress will determine this. Otherwise, you will need to judge carefully the right moment to do so. You don’t want to get feedback when it is too late to change or you have invested too much effort creating a prototype that might require widespread changes in approach. Likewise, it can be risky showing far-too-undercooked concepts to stakeholders or testers when they might not have the capacity to realise this is just an early indication of the direction of travel. The least valuable form of testing feedback is when pedantic stakeholders spend time pointing out minutiae that of course need correcting but have no significance at this stage. No-one comes away with anything of value from this kind of situation. ‘We can kid ourselves that we are successful in what we “want” to achieve, but ultimately an external and critical audience is essential. Feedback comes in many forms; I seek it, listen to it, sniff it, touch it, taste it and respond.’ Kate McLean, Smellscape Mapper and Senior Lecturer Graphic Design Refining and completing: Based on the outcome of your testing process, this will likely trigger a need to revisit some of the issues that have emerged and resolve them satisfactorily. Editing your work involves: correcting issues; stripping away the superfluous content; checking and enhancing preserved content; adding extra degrees of sophistication to every layer of your design; improving the consistency and cohesion of your choices; double-checking the accuracy of every component. As your work heads towards a state of completion your mindset will need to shift from a micro-level checking back to a macro-level assessment of whether you have truly delivered against the contextual requirements and purpose of your project. In any creative process a visualiser is faced with having to declare work as being complete. Judging this can be quite a tough call to make in many projects. As I have discussed plenty of times, your sense of ‘finished’ often needs to be based on when you have reached the status of good enough. While the presence of a looming deadline (and at times increasingly agitated stakeholders) will sharpen the focus, often it comes down to a fingertip sense of when you feel you are entering the period of diminishing 224 returns, when the refinements you make no longer add sufficient value for the amount of effort you invest in making them. ‘You know you’ve achieved perfection in design, not when you have nothing more to add, but when you have nothing more to take away.’ Antoine de Saint-Exupéry, Writer, Poet, Aristocrat, Journalist, and Pioneering Aviator ‘Admit that nothing you create on a deadline will be perfect. However, it should never be wrong. I try to work by a motto my editor likes to say: No Heroics. Your code may not be beautiful, but if it works, it’s good enough. A visualisation may not have every feature you could possibly want, but if it gets the message across and is useful to people, it’s good enough. Being “good enough” is not an insult in journalism – it’s a necessity.’ Lena Groeger, Science Journalist, Designer and Developer at ProPublica ‘It was intimidating to release to the public a self-initiated project on such a delicate subject considering some limitation with content and data source. But I came to appreciate that it’s OK to offer a relevant way of looking at the subject, rather than provide a beginning-to-end conclusion.’ Valentina D’efilippo, Information Designer, discussing her ‘Poppy Field’ project that looked at the history of world conflicts and the resulting loss of life Launching: The nature of launching work will again vary significantly based, as always, on the context of your challenge. It may simply be emailing a chart to a colleague or you might be presenting your work to an audience. For other cases it could be a graphic going to print for a newspaper or involve an anxious go-live moment with the launch of a digital project on a website, to much fanfare and public anticipation. Whatever the context of your ‘launch’ stage, there are a few characteristic matters to bear in mind – these will not be relevant to all situations but over time you might need to consider their implications for your setting: Are you ready? Regardless of the scope of your work, as soon as you declare work completed and published you are at the mercy of your decisions. You are no longer in control of how people will interpret your work and in what way they will truly use it. If you have particularly large, diverse and potentially emotive subject matter, you will need to be ready for the questions and scrutiny that might head in your direction. 225 Communicating your work is a big deal. The need to publicise and sell its benefits is of particular relevance if you have a public-facing project (you might promote it strongly or leave it as a slow burner that spreads through ‘word of mouth’). For more modest and personal audiences you might need to consider directly presenting your work to these groups, coaching them through what it offers. This is particularly necessary on those occasions when you may be using a less than familiar representation approach. What ongoing commitment exists to support the work? This clearly refers to specific digital projects. Do you have to maintain a live data feed? Will it need to sustain operations with variable concurrent visitors? What happens if it goes viral – have you got the necessary infrastructure? Have you got the luxury of ongoing access to the skill sets required to keep this project alive and thriving? Will you need to revise, update and rerelease the project? As I discussed in the contextual circumstances, will you need to replicate this work on a repeated basis? What can you do to make the reproduction as seamless as possible? What is the work’s likely shelf life? Does it have a point of expiry after which it could be archived or even killed? How might you digitally preserve it beyond its useful lifespan? 226 6 Data Representation In this chapter you will explore in detail the first, and arguably the most significant, layer of the visualisation design anatomy: data representation. This is concerned with deciding in what visual form you wish to show your data. To really get under the skin of data representation, we are going to look at it from both theoretical and pragmatic perspectives. You will start by learning about the building blocks of visual encoding, the real essence of this discipline and something that underpins all data representation thinking. Whereas visual encoding is perhaps seen as the purist ‘bottom- up’ viewpoint, the ‘top-down’ perspective possibly offers more pragmatic value by framing your data representation thinking around the notion of chart types. For most people facing up to this stage of data representation, this is conceptually the more practical entry point from which to shape their decisions. To substantiate your understanding of this design layer you will take a tour through a gallery of 49 different chart type options, reflecting the many common and useful techniques being used to portray data visually in the field today. This gallery will then by supplemented by an overview of the key influencing factors that will inform and determine the choices you make. 6.1 Introducing Visual Encoding As introduced in the opening chapter, data representation is the act of giving visual form to your data. As viewers, when we are perceiving a visual display of data we are decoding the various shapes, sizes, positions and colours to form an understanding of the quantitative and categorical values represented. As visualisers, we are doing the reverse through visual encoding, assigning visual properties to data values. Visual encoding forms the basis of any chart or map-based data representation, along with the components of chart apparatus that help complete the chart display. There are many different ways of encoding data but these always comprise 227 combinations of two different properties, namely marks and attributes. Marks are visible features like dots, lines and areas. An individual mark can represent a record or instance of data (e.g. your phone bill for a given month). A mark can also represent an aggregation of records or instances (e.g. a summation of individual phone charges to produce the bill for a given month). A set of marks would therefore represent a set of records or instances (e.g. the 12 monthly phone bills for 2015). Attributes are variations applied to the appearance of marks, such as the size, position, or colour. They are used to represent the values held by different quantitative or categorical variables against each record or instance (or, indeed, each aggregation). If you had 12 marks, one for each phone bill during 2015, you could use the size attribute of each mark to represent the various phone bill totals. Figure 6.1 offers a more visual illustration. In the dataset there are six records, one for each record listed. ‘Gender’ is a categorical variable and ‘Years Since First Movie’ is a quantitative variable. ‘Male’ and ‘43’ are the specific values of these variables associated with Harrison Ford. In the associated chart, each actor from the table is represented by the mark of a line (or bar). This represents their record or instance in the table. Harrison Ford’s bar is proportionally sized in scale to represent the 43 years since his first movie and is coloured purple to distinguish his gender as ‘Male’. Each of the five other actors similarly has a bar sized according to the years since their first movie and coloured according to their gender. Figure 6.1 Illustration of Visual Encoding The objective of visual encoding is to find the right blend of marks and attributes that most effectively will portray the angle of analysis you wish to show your viewers. The factors that shape your choice and define the notion of what is considered ‘effective’ are multiple and varied in their influence. Before getting on to there, let’s take a closer look at the range of 228 different marks and attributes that are commonly found in the data representation toolkit. It is worth noting upfront that while the organisation of the ‘attributes’, in particular, suggests a primary role, several can be deployed to encode both categorical (nominal, ordinal) variables and quantitative variables. Furthermore, as you see in the bar chart in Figure 6.1, combinations of several attributes are often applied to marks (such as colour and size) to encode multiple values. Although beyond the scope of this book, there are techniques being developed in the field exploring the use of non-visual senses to portray data, using variations in properties for auditory (sound), haptic (touch), gustatory (taste) and olfactory (smell) senses. Figure 6.2 List of Mark Encodings Figure 6.3 List of Attribute Encodings 229 230 Grasping the basics of visual encoding and its role in data visualisation is one of the fundamental pillars of understanding this discipline. However, when it comes to the reality of considering your data representation options you do not necessarily need to always approach things from this somewhat bottom-up perspective. For most people’s needs when creating a data visualisation it is more pragmatic (and perhaps more comprehensible) to think about data representation from a top-down perspective in the shape of chart types. If marks and attributes are the ingredients, a chart ‘type’ is the recipe offering a predefined template for displaying data. Different chart types offer different ways of representing data, each one comprising unique combinations of marks and attributes onto which specific types of data can be mapped. Recall that I am using chart type as the all-encompassing term, though this is merely a convenient singular label to cover any variation of map, graph, plot and diagram based around the representation of data. Let’s work through a few examples to illustrate the relationship between some selected chart types demonstrating different combinations of marks and attributes. To begin with Figure 6.4, visualises the recent fortunes of the world’s billionaires. The display shows the relative ranking of each profiled billionaire in the rich list, grouping them by the different sectors of industry in which they have developed their wealth. This data is encoded using the point mark and two attributes of position. The point in this deployment is depicted using small caricature face drawings representative of each individual – effectively unique symbols to represent the distinct ‘category’ of each different billionaire. Note that these are points, as distinct from area marks, because their size is constant and insignificant in terms of any quantitative implication. The position in the allocated column signifies the industry the individuals are associated with, while the vertical position signifies the rank (higher position = higher rank towards number 1). For reference, this is considered a derivative of the univariate scatter 231 plot, which usually shows the dispersal of a range of absolute values rather than rank. Figure 6.4 Bloomberg Billionaires As seen in Chapter 1, the clustered bar chart in Figure 6.5 displays a series of line marks (normally described as bars). There are 11 pairs of bars, one for each of the football seasons included in the aggregated analysis. The attribute of colour is used to distinguish the bars between the two quantitative measures displayed: blue is for ‘games’, purple is for ‘goals’. The size dimension of ‘height’ (the widths are constant) along the y-axis scale then represents the quantitative values associated with each season and each measure. Figure 6.6 is called a bubble chart and displays a series of geometric area marks to represent the top 100 blog posts on my website based on their popularity over the previous 100 days. Each circle represents an individual post and is sized to show the quantitative value of ‘total visits’ and then coloured according to the seven different post categories I use to organise my content. Figure 6.5 Lionel Messi: Games and Goals for FC Barcelona 232 Figure 6.6 Image from the home page of visualisingdata.com 233 Figure 6.7 How the Insane Amount of Rain in Texas Could Turn Rhode Island Into a Lake 234 Figure 6.7 demonstrates the use of the form, which is more rarely used. My advice is that it should remain that way as it is hard for us to judge scales of volume in 2D displays. However, it can be of merit when values are extremely diverse in size as in this good example. The chart displayed contextualises the amount of water that had flowed into Texas reservoirs in the 30 days up to 27 May 2015. The size (volume) of a cube is used to display the amount of rain, with 8000 small cubes representing 1000 acre- feet of water (43,560,000 cubic feet or 1233.5 mega litres) to create the whole (8 million acre-feet), which is then compared against the heights of 235 the Statue of Liberty and what was then the world’s tallest building, the Burj Khalifa, to orient in height terms at least. 6.2 Chart Types For many people, creating a visualisation involves using tools that offer chart menus: you might select a chart type and then ‘map’ the records and variables of data against the marks and attributes offered by that particular chart type. Different tools will offer the opportunity to work with a different range of chart types, some with more than others. As you develop your capabilities in data visualisation and become more ‘expressive’ – trying out unique combinations of marks and attributes – your approach might lean more towards thinking about representation from a bottom-up perspective, considering the visual encodings you wish to deploy and arriving at a particular chart type as the destination rather than an origin. This will be especially likely if you develop or possess a talent for creating visualisations through programming languages. As the field has matured over the years, and a greater number of practitioners have been experimenting with different recipes of marks and attributes, there is now a broad range of established chart types. Once again I hesitate to use the universal label of chart type (some mapping techniques are not chart types per se) but it will suffice. While all of us are likely to be familiar with the ‘classic three’ – namely, the bar, pie and line chart – there are many other chart type options to consider. To acquaint you with a broader repertoire of charting options, over the coming pages I present you with a gallery. This offers a curated collection of some of the common and useful chart types being used across the field today. This gallery aims to provide you with a valuable reference that will directly assist your judgements, helping you to pick (conceptually, at least) from a menu of options. I have attempted to assign each chart to one of five main families based on their primary analytical purpose. What type of angle of analysis does each one principally show? Using the five-letter mnemonic CHRTS this should provide a useful taxonomy for organising your thinking about which chart or charts to use for your data representation needs. 236 I know what you’re thinking: ‘well that’s a suspiciously convenient acronym’! Honestly, if it was as intentional as that I would have tried harder to somehow crowbar in an ‘A’ family. OK, I did spend a lot of time, but I couldn’t find it and it’s now my life’s ambition to do so. Only then will my time on this planet have been truly worthwhile. In the meantime, CHRTS is close enough. Besides, vowels are hugely overrated. Each chart type presented is accompanied by an array of supporting details that will help you fully acquaint yourself with the role and characteristics of each option. A few further comments about what this gallery provides: The primary name used to label each chart type as well as some further alternative names that are often used An indication of which CHRTS family each chart belongs to, based on their specific primary role, as well as a sub-family definition for further classification An indicator for each chart type to show which ones I consider to be most useful for undertaking Exploratory Data Analysis (the black magnifying glass symbol) An indicator for whether I believe a chart would typically require interactive features to offer optimum usability (the black cursor symbol) A description of the chart’s representation: what it shows and what encodings (marks, attributes) it is comprised of A working example of the chart type in use with a description of what it specifically shows A ‘how to read’ guide, advising on the most effective and efficient approach to making sense of each chart type and what features to look out for Presentation tips offering guidance on some of the specific choices to be considered around interactivity, annotation, colour or composition 237 design ‘Variations and alternatives’ offer further derivatives and chart ‘siblings’ to consider for different purposes Exclusions: It is by no means an exhaustive list: the vast permutations of different marks and attributes prevents any finite limit to how one might portray data visually. I have, however, consciously excluded some chart types from the gallery mainly because they were not different enough from other charts that have been profiled in detail. I have mentioned charts that represent legitimate derivatives of other charts where necessary but simply did not deem it worthy to assign a whole page to profile them separately. The Voronoi treemap, for example, is really just a circular treemap that uses different algorithms to arrange its constituent pieces. While the construction task is different, its usage is not. The waterfall chart is a single stacked bar chart broken down into sequenced stages. Inclusions: I have wrestled with the rights and wrongs of including some chart types, unquestionably. The radar chart, for example, has many limitations and flaws but is not entirely without merit if deployed in a very specific way and only for certain contexts. By including profiles of partially flawed charts like these I am using the gallery as much to signpost their shortcomings so that you know to use them sparingly. There will be some purists gathering in angry mobs and foaming at the mouth in reaction to the audacity of my including the pie chart and word cloud. These have limited roles, absolutely, but a role nonetheless. Put down your pitchforks, return to your homes and have a good read of my caveats. Rather than being the poacher of all bad stuff, I think a gamekeeper role is equally important. Although I have excluded several charts on grounds of demonstrating only a slight variation on profiled charts, there are some types included that do exhibit only small derivations from other charts (such as the bar chart and the clustered bar, or the scatter plot and the bubble plot). In these cases I felt there was sufficient difference in their practical application, and they were in common usage, to merit their separate inclusion, despite sharing many similarities with other profiled siblings. ‘Interestingly, visualisations of textual data are not as developed as one 238 would expect. There is a great need for such visualisations given the amount of textual information we generate daily, from social media to news media and so on, not to mention all the materials generated in the past and that are now digitally available. There are opportunities to contribute to the research efforts of humanists as well as social scientists by devising ways to represent not only frequencies of words and topics, but also semantic content. However, this is not at all trivial.’ Isabel Meirelles, Professor, OCAD University (Toronto), discussing one of the many remaining unknowns in visualisation Categorical comparisons: All chart types can feasibly facilitate comparisons between categories, so why have a separate C family? Well, the distinction is that those charts belonging to the H, R, T and S families offer an additional dimension of analysis as well as providing comparison between categories. Dual families: Some charts do not fit just into a single family. Showing connected relationships (e.g. routes or flows) on a map is ticking the requirements across at least two or family groups (Relational, Spatial). In each case I have tried to best-fit the family classifications around the primary angle of analysis portrayed by each chart – what is the most prominent aspect that characterises each representation technique. Text visualisation: As I noted in the discussion about data types, when it comes to working with textual-based data you are almost always going to need to perform some transformation, maybe through value extraction or by applying a statistical technique. The text itself can otherwise largely function only as an annotated device. Chart types used to visualise text actually visualise the properties of text. For example, the word cloud visualises the quantitative frequency of the use of words: text might be the subject, but categories (words) and their quantities (counts) are the data mappings. Varieties of network diagrams might show the relationship between word usage, such as the sequence of words used in sentences (word trees), but these are still only made possible through some quantitative, categorical or semantic property being drawn from the original text. Dashboard: These methods are popular in corporate settings or any context where you wish to create instrumentation that offers both at- a-glance and detailed views of many different analytical and information monitoring dimensions. Dashboards are not a unique chart type themselves but rather should be considered projects that comprise multiple chart types from across the repertoire of options 239 presented in the gallery. Some of the primary demands of designing dashboards concern editorial thinking (what angles to show and why) and composition choices (how to get it all presented in a unified page layout). Small multiples: This is an invaluable technique for visualising data but not necessarily a chart type per se and, once again, more a concern for about editorial thinking and composition design. Small multiples involve repeated display of the same chart type but with adjustments to the framing of the data in each panel. For example, each panel may show the same angle of analysis but for different categories or different points in time. Small multiples are highly valued because they exploit the capabilities of our visual perception system when it comes to comparing charts in a simultaneous view, overcoming our weakness at remembering and recalling chart views when consumed through animated sequences or across different pages. A note about ‘storytelling’: Storytelling is an increasingly popular term used around data visualisation but I feel it is often misused and misunderstood, which is quite understandable as we all have different perspectives. I also feel it is worth clarifying my take on what I believe storytelling means practically in data visualisation and especially in this discussion about data representation, which is where it perhaps most logically resides in terms of how it is used. Stories are constructs based on the essence of movement, change or narrative. A line chart shows how a series of values have changed over a temporal plane. A flow map can reveal what relationships exist across a spatial plane between two points separated by distance – they may be evident of a journey. However, aside from the temporal and spatial families of charts, I would argue that no other chart family realistically offers this type of construct in and of itself. The only way to create a story from other types of charts is to incorporate a temporal dimension (video/slideshow) or provide a verbal/written narrative that itself involves a dimension of time through the sequence of its delivery. For example, a bar chart alone does not represent a story, but if you show a ‘before’ and ‘after’ pair of bar charts side by side or between slides, you have essentially created ‘change’ through sequence. If you show a bar chart with a stack on top of it to indicate growth between two points in time, well, you have added a time dimension. A network diagram shows relationships, but stood alone this is not a 240 story – its underlying structure and arrangement are in abstract space. Just as you do when showing friends a photograph from your holiday, you might use this chart as a prop to explain how relationships between some of the different entities presented are significant. Making the chart a prop allows you to provide a narrative. In this case it is the setting and delivery that are consistent with the notion of storytelling, not the chart itself. I made a similar observation about the role of exhibitory visualisations used as props within explanatory settings. A further distinction to make is between stories as being presented and stories as being interpreted. The famous six-word story ‘for sale: baby shoes, never worn’ by Ernest Hemingway is not presented as a story, the story is triggered in our mind when we dissect this passage and start to infer meaning, implication and context. The imagined bar chart I mentioned earlier in the book that could show the 43 white presidents and 1 black president is only presenting a story if it is accompanied by an explanatory narrative (in which case the chart was again really just a prop) or if you understand the meaning of the significance of this statistic without this description and are able to form the story in your own mind. Charts Comparisons Bar chart ALSO KNOWN AS Column chart, histogram (wrongly) REPRESENTATION DESCRIPTION A bar chart displays quantitative values for different categories. The chart comprises line marks (bars) – not rectangular areas – with the size attribute (length or height) used to represent the quantitative value for each category. 241 EXAMPLE Comparing the number of Oscar nominations for the 10 actors who have received the most nominations without actually winning an award. Figure 6.8 The 10 Actors with the Most Oscar Nominations but No Wins HOW TO READ IT & WHAT TO LOOK FOR Look at the axes so you know with which categorical value each bar is associated and what the range of the quantitative values is (min to max). Think about what high and low values mean: is it ‘good’ to be large or small? Glance across the entire chart to locate the big, small and medium bars and perform global comparisons to establish the high- level ranking of biggest > smallest. Identify any noticeable exceptions
and/or outliers. Perform local comparisons between neighbouring bars,
to identify larger than and smaller than relationships and estimate the
relative proportions. Estimate (or read, if labels are present) the absolute
values of specific bars of interest. Where available, compare the
quantities against annotated references such as targets, forecast, last
year, average, etc.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines,
in particular, can be helpful to increase the accuracy of the reading of
the quantitative values. If you have axis labels you should not need
direct labels on each bar – this will lead to label overload, so generally
decide between one or the other.
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COMPOSITION: The quantitative value axis should always start from
the origin value of zero: a bar should be representative of the true, full
quantitative value, nothing more, nothing less, otherwise the perception
of bar sizes will be distorted when comparing relative sizes. There is no
significant difference in perception between vertical or horizontal bars
though horizontal layouts tend to make it easier to accommodate and
read the category labels for each bar. Unlike the histogram, there should
be a gap, even if very small, between bars to keep each category’s value
distinct. Where possible, try to make the categorical sorting meaningful.
VARIATIONS & ALTERNATIVES
A variation in the use of bar charts is to show changes over time. You
would use a bar chart when the focus is on individual quantitative
values over time rather than (necessarily) the trend/change between
points, for which a line-chart would be best. ‘Spark bars’ are mini bar
charts that aim to occupy only a word’s length amount of space. They
are often seen in dashboards where space is at a premium and there is a
desire to optimise the density of the display. To show further
categorical subdivisions, you might consider the ‘clustered bar chart’ or
a ‘stacked bar chart’ if there is a part-to-whole angle. ‘Dot plots’ offer a
particularly useful alternative to the bar chart for situations where you
have to show large quantitative values with a narrow range of
differences.
Charts Comparisons
Clustered bar chart
ALSO KNOWN AS Clustered column chart, paired bar chart
REPRESENTATION DESCRIPTION
A clustered bar chart displays quantitative values for different major
categories with additional categorical dimensions included for further
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breakdown. The chart comprises line marks (bars) – not rectangular
areas – with the size attribute (length or height) used to represent the
quantitative value for each category and colours used to distinguish
further categorical dimensions.
EXAMPLE Comparing the number of Oscar nominations with the
number of Oscar awards for the 10 actors who have received the most
nominations.
Figure 6.9 The 10 Actors who have Received the Most Oscar
Nominations
HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with which categorical value each bar is
associated and what the range of the quantitative values is (min to max).
Learn about the colour associations to understand what sub-categories
the bars within each cluster represent. Glance across the entire chart to
locate the big, small and medium bars and perform global comparisons
to establish the high-level ranking of biggest > smallest. Identify any
noticeable exceptions and/or outliers. Perform local comparisons within
clusters to identify the size relationship (which is larger and by how
much?) and estimate (or read, if labels are present) the absolute values
of specific bars of interest.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines,
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in particular, can be helpful to increase the accuracy of the reading of
the quantitative values. If you have axis labels you should not need
direct labels on each bar – this will lead to label overload, so generally
decide between one or the other.
COMPOSITION: The quantitative value axis should always start from
the origin value of zero: a bar should be representative of the true, full
quantitative value, nothing more, nothing less, otherwise the perception
of bar sizes will be distorted when comparing relative sizes. If your
categorical clusters involve a breakdown of more than three bars, it
becomes a little too busy, so you might therefore consider giving each
cluster its own separate bar chart and using small multiples to show a
chart for each major category. Sometimes one bar might be slightly
hidden behind the other, implying a before and after relationship, often
when space is at a premium – just do not hide too much of the back bar.
There is no significant difference in perception between vertical or
horizontal bars though horizontal layouts tend to make it easier to
accommodate and read the category labels for each bar. The individual
bars should be positioned adjacent to each other with a noticeable gap
and then between each cluster to help direct the eye towards the
clustering patterns first and foremost. Where possible try to make the
categorical sorting meaningful.
VARIATIONS & ALTERNATIVES
Clustered bar charts are also sometimes used to show how two
associated sub-categories have changed over time (like the Lionel
Messi bar chart discussed in Chapter 1). Alternatives would include the
‘dot plot’ or, if you have just two categories forming the clusters and
these categories have a binary state (male, female or yes %, no %), the
‘back-to-back bar chart’ would be effective.
Charts Comparisons
Dot plot
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ALSO KNOWN AS Dot chart
REPRESENTATION DESCRIPTION
A dot plot displays quantitative values for different categories. In
contrast to the bar chart, rather than using the size of a bar, point marks
(typically circles but any ‘symbol’ is legitimate) are used with the
position along a scale indicating the quantitative value for each
category. Sometimes an area mark is used to indicate one value through
position and another value through size. Additional categorical
dimensions can be accommodated in the same chart by including
additional marks differentiated by colour or symbol.
EXAMPLE Comparing the number and percentage of PhDs awarded
by gender across different academic subjects.
Figure 6.10 How Nations Fare in PhDs by Sex
HOW TO READ IT & WHAT TO LOOK FOR
For single-series dot plots (i.e. just one dot per row), look at the axes so
you know with which categorical value each row is associated and what
the range of the quantitative values is (min to max). Where you have
multiple series dot plots (i.e. more than one dot), establish what the
different colours/symbols represent in terms of categorical breakdown.
Glance across the entire chart to locate the big, small and medium
values and perform global comparisons to establish the high-level
ranking of biggest > smallest. Identify any noticeable exceptions and/or
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outliers. Where you have multiple series look across each series of dot
values separately and then perform local comparisons within rows to
identify the relative position of each dot, observing the gaps, big and
small. Estimate the absolute values of specific dots of interest. Where
available, compare the quantities against annotated references such as
targets, forecast, last year, average, etc.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines,
in particular, can be helpful to increase the accuracy of the reading of
the quantitative values.
COMPOSITION: Given that the quantitative value axis does not need
to commence from a zero origin it is important to label clearly the axis
values when the baseline is not commencing from a minimum of zero.
There is no significant difference in perception between vertical or
horizontal arrangement though horizontal layouts tend to make it easier
to accommodate and read the category labels for each row. Where
possible try to make the categorical sorting meaningful, maybe
organising values in ascending/descending size order.
VARIATIONS & ALTERNATIVES
Alternatives would include the ‘bar chart’, to show the size of
quantitative values for different categories. The ‘connected dot plot’
would be used to focus on the difference between two measures. The
‘univariate scatter plot’ would be used to show the range of multiple
values across categories, to display the diversity and distribution of
values.
Charts Comparisons
Connected Dot Plot
247

ALSO KNOWN AS Barbell chart, dumb-bell chart
REPRESENTATION DESCRIPTION
A connected dot plot displays absolute quantities and quantitative
differences between two categorical dimensions for different major
categories. The display is formed by two points (normally circles but
any ‘symbol’ is legitimate) to mark the quantitative value positions for
two comparable categorical dimensions. There is a row of connected
dots for each major category. Colour or difference in symbol is
generally used to distinguish these points. Joining the two points
together is a connecting line which effectively represents the ‘delta’
(difference) between the two values.
EXAMPLE Comparing the typical salaries for women and men across
a range of different job categories in the US.
Figure 6.11 Gender Pay Gap US
HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with which major categorical values each
row is associated and what the range of the quantitative values is (min
to max). Determine which dots resemble which categorical dimension
(could be colour, symbol or a combination) and see if there is any
meaning behind the colouring of the connecting bars. Think about what
the quantitative values mean to determine whether it is a good thing to
be higher or lower. Glance across the entire chart to locate the big,
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small and medium connecting bars in each direction. Perform global
comparisons to establish the high-level ranking of biggest > smallest
differences as well as the highest and lowest values. There may be
deliberate sorting of the display based on one of the quantitative
measures. Identify any noticeable exceptions and/or outliers. Estimate
(or read, if labels are present) the absolute values, direction and size of
differences for specific categories of interest.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines,
in particular, can be helpful to increase the accuracy of the reading of
the quantitative values. Consider labelling categories adjacent to the
plotted points rather than next to the axis line (and possibly far away
from the values) to make it easier for the reader to understand the
category–row association.
COLOUR TIPS: Colour may be used to indicate and emphasise the
directional basis of the connecting line differences.
COMPOSITION: If the two plotted measures are very similar, and the
point markers effectively overlap, you will need to decide which should
be positioned on top. As the representation of the quantitative values is
through position along a scale and not size (it is the difference that is
sized, not the absolutes) the quantitative axis does not need to have a
zero origin. However, a zero origin can be helpful to establish the scale
of the differences. Where possible try to make the sorting meaningful
using any one of the three quantitative measures to optimise the layout.
VARIATIONS & ALTERNATIVES
Variations in the use of the ‘connected dot plot’ would show before and
after analysis between two points in time, possibly using the ‘arrow
chart’ to indicate the direction of change explicitly. Similarly, the
‘carrot chart’ uses line width tapering to indicate direction, the fatter
end the more recent values. The ‘univariate scatter plot’ would be used
to show the range of multiple values across categories, to display the
diversity and distribution of values rather than comparing differences
between values.
Charts Comparisons
249

Pictogram
ALSO KNOWN AS Isotype chart, pictorial bar chart, stacked shape
chart, tally chart
REPRESENTATION DESCRIPTION
A pictogram displays quantitative values for different major categories
with additional categorical dimensions included for further breakdown.
In contrast with the bar chart, rather than using the size of a bar,
quantities of point marks, in the form of symbols or pictures, are
stacked to represent the quantitative value for each category. Each point
may be representative of one or many quantitative units (e.g. a single
shape may represent 1000 people) but note that, unless you use symbol
portions, you will not be able to represent decimals. Pictograms may be
used to offer a more emotive (humanising or more light-hearted)
display than a bar can offer. Additional categorical dimensions can be
accommodated in the same chart by using marks differentiated by
variations in colour, symbol or picture. Always ensure the markers used
are as intuitively recognisable as possible and consider minimising the
variety as this makes it cognitively harder for the viewer to identify
associations easily and make sense of the quantities.
EXAMPLE Comparing the number of players with different facial hair
types across the four teams in the NHL playoffs in 2015.
Figure 6.12 Who Wins the Stanley Cup of Playoff Beards?
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the major categorical axis to establish with which category each
row is associated. Establish the mark associations to understand what
categorical dimensions each colour/shape variation represents. Glance
across the entire chart to locate the big, small and medium stacks of
shapes and perform global comparisons to establish the high-level
ranking of biggest > smallest. Identify any noticeable exceptions and/or
outliers. Perform local comparisons between neighbouring categories,
to identify larger than and smaller than relationships and estimate the
relative proportions. Estimate (or read, if labels are present) the absolute
values of specific groups of markers of interest.
PRESENTATION TIPS
ANNOTATION: The choice of symbol/ picture should be as
recognisably intuitive as possible and locate any legends as close as
possible to the display.
COLOUR TIPS: Maximise the variation in marker by using different
combinations in both colour and shape, rather than just variation of one
attribute.
COMPOSITION: If the quantities of markers exceed a single row, try
to make the number of units per row logically ‘countable’, such as
displaying in groups of 5, 10 or 100. To aid readability, make sure there
is a sufficiently noticeable gap between rows, otherwise sometimes the
eye struggles to form the distinct clusters of shapes for each category
displayed. Where possible try to make the categorical sorting
meaningful, maybe organising values in ascending/descending size
order.
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VARIATIONS & ALTERNATIVES
Extending the idea of using repeated quantities of representative
symbols, some applications take this further by using large quantities of
individual symbols to get across the feeling of magnitude and scale.
When showing a part-to-whole relationship, the ‘waffle chart’ can use
simple symbol devices to differentiate the constituent parts of a whole.
Charts Comparisons
Proportional shape chart
ALSO KNOWN AS Area chart (wrongly)
REPRESENTATION DESCRIPTION
A proportional shape chart displays quantitative values for different
categories. The chart is based on the use of different area marks, one for
each category, sized in proportion to the quantities they represent. By
using the quadratic dimension of area size rather than the linear
dimension of bar length or dot position, the shape chart offers scope for
displaying a diverse range of quantitative values within the same chart.
Typically the layout is quite free-form with no baseline or central
gravity binding the display together.
EXAMPLE Comparing the market capitalisation ($) of companies
involved in the legal sale of marijuana across different industry sectors.
Figure 6.13 For These 55 Marijuana Companies, Every Day is 4/20
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the shapes and their associated labels so you know with what
major categorical values each is associated. If there are only direct
labels, find the largest shape to establish its quantitative value as the
maximum and do likewise for the smallest – this will help calibrate the
size judgements. Otherwise, if it exists, acquaint yourself with the size
key. Glance across the entire chart to locate the big, small and medium
shapes and perform global comparisons to establish the high-level
ranking of biggest > smallest. Identify any noticeable exceptions and/or
outliers. Perform local comparisons between neighbouring shapes to
identify larger than and smaller than relationships and estimate the
relative proportions. Estimate (or read, if labels are present) the absolute
values of specific shapes of interest.
PRESENTATION TIPS
ANNOTATION: Sometimes a quantitative size key will be included
rather than direct labelling (usually when there are many shapes and
limited empty space) though direct labels will help overcome some of
the limitations of judging area size. You will have to decide how to
handle label positioning for those shapes with exceptionally small sizes.
COLOUR TIPS: Colours are not fundamentally necessary to encode
category (the position/separation of different shapes achieves that
already) but they can be useful as redundant encodings to make the
category even more immediately distinguishable.
COMPOSITION: Estimating and comparing the size of areas with
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accuracy is not as easy as it is for judging bar length or dot position, so
only use this chart type if you have a diverse range of quantitative
values. The geometric accuracy of the size calculations is paramount.
Mistakes are often made, in particular, with circle size calculations: it is
the area you are modifying, not the diameter/radius. Arrangement
approaches vary: sometimes you see the shapes anchored to a common
baseline (bottom or central alignment) while on other occasions they
might just ‘float’. If you use an organic shape, like a human figure, to
represent different quantities you need to adjust the entire shape area,
not just the height. Often the approach for this type of display is to treat
the figure as a rudimentary rectangular shape. Sometimes the volume of
a shape is used rather than area to represent quantitative values
(especially if there are almost exponentially different values to show)
but this increases the perceptual difficulty in estimating and comparing
values. Where possible try to make the categorical sorting meaningful,
maybe organising values in ascending/descending size order.
VARIATIONS & ALTERNATIVES
The ‘bubble chart’ uses clusters of sized bubbles to compare categorical
values and, sometimes, to represent part-to-whole analysis. The ‘nested
shape chart’ might include secondary, smaller area sizes nested within
each shape to display local part-to-whole relationships.
Charts Comparisons
Bubble chart
ALSO KNOWN AS Circle packing diagram
EXAMPLE Comparing the Public sector capital expenditure (£
million) on services by function of the UK Government during 2014/15.
REPRESENTATION DESCRIPTION
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A bubble chart displays quantitative values for different major
categories with additional categorical dimensions included for further
breakdown. It is based on the use of circles, one for each category, sized
in proportion to the quantities they represent. Sometimes several
separate clusters may be used to display further categorical dimensions,
otherwise the colouring of each circle can achieve this. It is similar in
concept to the proportional shape chart but differs through the typical
layout being based on clustering, which therefore also enables it as a
device for showing part-to-whole relationships as well.
Figure 6.14 UK Public Sector Capital Expenditure, 2014/15
HOW TO READ IT & WHAT TO LOOK FOR
Look at the shapes and their associated labels so you know with what
major categorical values each is associated, noting any size and colour
legends to assist in forming associations. If there are multiple clusters,
learn about the significance of the grouping/separation in each case. If
there are direct labels, find the largest shape to establish its quantitative
value as the maximum and do likewise for the smallest – this will help
calibrate other size judgements. Glance across the entire chart to locate
the big, small and medium shapes and perform global comparisons to
establish the high-level ranking of biggest > smallest. Identify any
noticeable exceptions and/or outliers. Perform local comparisons
between neighbouring shapes to identify larger than and smaller than
relationships and estimate the relative proportions. Estimate (or read, if
labels are present) the absolute values of specific shapes of interest. If
there are multiple clusters, note the general relative size and number of
members in each case.
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PRESENTATION TIPS
INTERACTIVITY: Bubble charts may often be accompanied by
interactive features that let users select or mouseover individual circles
to reveal annotated values for the quantity and category.
ANNOTATION: If interactivity is not achievable, a quantitative size
key should be included or direct labelling; the latter may make the
display busy (and be hard to fit into smaller circles) but will help
overcome some of the limitations of judging area size.
COLOUR TIPS: Colours are sometimes used as redundant encodings
to make the quantitative sizes even more immediately distinguishable.
COMPOSITION: Estimating and comparing the size of areas with
accuracy is not as easy as it is for judging bar length or dot position, so
only use this chart type if you have a diverse range of quantitative
values. The use of this chart will primarily be about facilitating a gist, a
general sense of the largest and smallest values. The geometric
accuracy of the circle size calculations is paramount. Mistakes are often
made with circle size calculations: it is the area you are modifying, not
the diameter/radius. If you wish to make your bubbles appear as 3D
spheres you are essentially no longer representing quantitative values
through the size of a geometric area mark; rather the mark will be a
‘form’ and so the size calculation will be based on volume, not area.
There is no categorical or quantitative sorting applied to the layout of
the bubble chart, instead the tools that offer these charts will generally
use a layout algorithm that applies a best-fit clustering to arrange the
circles radially about a central ‘gravity’ force.
VARIATIONS & ALTERNATIVES
When the collection of quantities represents a whole, this evolves into a
chart known as a ‘circle packing diagram’ and usually involves many
parts that pack neatly into a circular layout representing the whole.
Another variation of the packing diagram is when the adjacency
between circle ‘nodes’ indicates a connected relation, offering a
variation of the node–link diagram for showing networks of
relationships. The bubble plot also uses differently sized circles but the
position in each case is overlaid onto a scatter plot structure, based on
two dimensions of further quantitative variables. Removing the size
attribute (and effectively replacing area with point mark) you could
simply use the quantity of points clustered together for different
categories to create a ’tally chart’.
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Charts Comparisons
Radar chart
ALSO KNOWN AS Filled radar chart, star chart, spider diagram, web
chart
EXAMPLE Comparing the global competitive scores (out of 7) across
12 ‘pillars’ of performance for the United Kingdom.
REPRESENTATION DESCRIPTION
A radar chart shows values for three or more different quantitative
measures in the same display for, typically, a single category. It uses a
radial (circular) layout comprising several axes emerging from the
centre-like spokes on a wheel, one for each measure. The quantitative
values for each measure are plotted through position along each scale
and then joined by connecting lines to form a unique geometric shape.
Sometimes this shape is then filled with colour. A radar chart should
only be considered in situations where the cyclical ordering (and
neighbourly pairings) has some significance (such as data that might be
plotted around the face of a clock or compass) and when the
quantitative scales are the same (or similar) for each axis. Do not plot
values for multiple categories on the same radar chart, but use small
multiples formed of several radar charts instead.
Figure 6.15 Global Competitiveness Report 2014—2015
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HOW TO READ IT & WHAT TO LOOK FOR
Look around the chart and acquaint yourself with the quantitative
measure represented by each axis and note the sequencing of the
measures around the display. Is there any significance in this
arrangement that can assist in interpreting the overall shape? Note the
range of values along each independent axis so you understand what
positions along the scales mean in a value sense for each measure. Scan
the shape to locate the outliers both towards the outside (larger values)
and inside (smaller values) of the scales. It is more important to pay
attention to the position of values along an axis than the nature of the
connecting lines between axes, unless the axis scales are consistent or at
least if the relative position along the scale has the same implied
meaning. If the variable sequencing has cyclical relevance, the spiking,
bulging or contracting shape formed will give you some sense of the
balance of values. Perform local comparisons between neighbouring
axes to identify larger than and smaller than relationships. Estimate (or
read, if labels are present) the absolute values of specific shapes of
interest.
PRESENTATION TIPS
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ANNOTATION: The inclusion of visible annotated features like axis
lines, tick marks, gridlines and value labels can naturally aid the
readability of the radar chart. Gridlines are only relevant if there are
common scales across each quantitative variable. If so, the gridlines
must be presented as straight lines, not concentric arcs, because the
connecting lines joining up the values are themselves straight lines.
COLOUR TIPS: Often the radar shapes are filled with a colour,
sometimes with a degree of transparency to allow the background
apparatus to be partially visible.
COMPOSITION: The cyclical ordering of the quantitative variables
has to be of optimum significance as the connectors and shape change
for every different ordering permutation. This will have a major impact
on the readability and meaning of the resulting chart shape. As the axes
will be angled all around the radial display, you will need to make sure
all the associated labels are readable (i.e. not upside down or at difficult
angles).
VARIATIONS & ALTERNATIVES
A ‘polar chart’ is an alternative to the radar chart that removes some of
the main shortcomings caused by connecting lines in the radar chart. If
you have consistent value scales across the different quantitative
measures, a ‘bar chart’ or ‘dot plot’ would be a better alternative. While
not strictly a variation, ‘parallel coordinates’ display a similar technique
for plotting several independent quantitative measures in the same
chart. The main difference is that parallel coordinates use a linear layout
and can accommodate many categories in one display.
Charts Comparisons
Polar chart
ALSO KNOWN AS Coxcomb plot, polar area plot
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REPRESENTATION DESCRIPTION
A polar chart shows values for three or more different quantitative
measures in the same display. It uses a radial (circular) layout
comprising several equal-angled circular sectors like slices of a pizza,
one for each measure. In contrast to the radar chart (which uses position
along a scale), the polar chart uses variation in the size of the sector
areas to represent the quantitative values. It is, in essence, a radially
plotted bar chart. Colour is an optional attribute, sometimes used
visually to indicate further categorical dimensions. A polar chart should
only be considered in situations where the cyclical ordering (and
neighbourly pairings) has some significance (such as data that might be
plotted around the face of a clock or compass) and when the
quantitative scales are the same (or similar) for each axis.
EXAMPLE Comparing the quantitative match statistics across 14
different performance measures for a rugby union player.
Figure 6.16 Excerpt from a Rugby Union Player Dashboard
HOW TO READ IT & WHAT TO LOOK FOR
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Look around the chart and acquaint yourself with the quantitative
measures each sector represents and note the sequencing of the
measures around the display. Is there any significance in this
arrangement that can assist in interpreting the overall shape? Note the
range of values included on the quantitative scale and acquaint yourself
with any colour associations. Glance across the entire chart to locate the
big, small and medium sectors and perform global comparisons to
establish the high-level ranking of biggest > smallest. Identify any
noticeable exceptions and/or outliers. Perform local comparisons
between neighbouring variables to identify the order of magnitude and
estimate the relative sizes. Estimate (or read, if labels are present) the
absolute values of specific sectors of interest. Where available, compare
the quantities against annotated references such as targets, forecast, last
year, average, etc. If there is significance behind the sequencing of the
variables, look out for any patterns that emerge through spiking,
bulging or contracting shapes.
PRESENTATION TIPS
ANNOTATION: The inclusion of visible annotated features like tick
marks and value labels can naturally aid the readability of the polar
chart. Gridlines are only relevant if there are common scales across
each quantitative variable. If so, the gridlines must be presented as arcs
reflecting the outer shape of each sector. Connecting lines joining up
the values are themselves straight lines. Each sector typically uses the
same quantitative scale for each quantitative measure but, on the
occasions when this is not the case, each axis will require its own, clear
value scale.
COLOUR TIPS: Often polar chart sectors are filled with a meaningful
colour, sometimes with a degree of transparency to allow the
background apparatus to be partially visible.
COMPOSITION: The cyclical ordering of the quantitative variables
has to be of some significance to legitimise the value of the polar chart
over the bar chart. As the sectors will be angled all around the radial
display, you will need to make sure all the associated labels are
readable (i.e. not upside down or at difficult angles). The quantitative
values represented by the size of the sectors need to be carefully
calculated. It is the area of the sector, not the radius length, that will be
modified to portray the values accurately. If you make maximum
quantitative value equivalent to the largest sector area, all other sector
sizes can be calculated accordingly. Knowing how many different
quantitative variables you are showing means you can easily calculate
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the angle of any given sector. The quantitative measure axes should
always start from the origin value of zero: a sector should be
representative of the true, full quantitative value, nothing more, nothing
less, otherwise the perception of size will be distorted when comparing
relative sizes.
VARIATIONS & ALTERNATIVES
Unless the radial layout provides meaning through the notion of a
‘whole’ or through the cyclical arrangement of measures, you might be
best using a ‘bar chart’. Variations in approach tend to see
modifications in the sector shape with measure values represented by
individual bars lengths or, in the example of the Better Life Index
project, through variations in ‘petal’ sizes.
Charts Distributions
Range Chart
ALSO KNOWN AS Span chart, floating bar chart, barometer chart
REPRESENTATION DESCRIPTION
A range chart displays the minimum to maximum distribution of a
series of quantitative values for different categories. The display is
formed by a bar, one for each category, with the lower and upper
position of the bars shaped by the minimum and maximum quantitative
values in each case. The resulting bar lengths thus represent the range
of values between the two limits.
EXAMPLE Comparing the highest and lowest temperatures (°F)
recorded across the top 10 most populated cities during 2015.
Figure 6.17 Range of Temperatures Recorded in Top 10 Most
Populated Cities (2015)
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with what major categorical values each
range bar is associated and what the range of the quantitative values is
(min to max). Glance across the entire chart to locate the big, small and
medium bars and perform global comparisons to establish the high-
level ranking of biggest > smallest differences as well as the highest and
lowest values. Identify any noticeable exceptions and/or outliers.
Perform local comparisons between neighbouring bars, to identify
larger than and smaller than relationships and estimate the relative
proportions. There may be deliberate sorting of the display based on
one of the quantitative measures. Estimate (or read, if labels are
present) the absolute values of specific bars of interest. Where
available, compare the quantities against annotated references such as
targets, forecast, last year, average, etc.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines,
in particular, can be helpful to increase the accuracy of the reading of
the quantitative values. If you have axis labels you may not need direct
labels on each bar – this will be lead to label overload, so generally
decide between one or the other.
COMPOSITION: The quantitative value axis does not need to
commence from zero, unless it means something significant to the
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interpretation, as the range of values themselves does not necessarily
start from zero and the focus is more on the range and difference
between the outer values. There is no significant difference in
perception between vertical or horizontal layouts, though the latter tend
to make it easier to accommodate and read the category labels. Where
possible, try to make the categorical sorting meaningful, maybe
organising values in ascending/descending size order.
VARIATIONS & ALTERNATIVES
‘Connected dot plots’ will also emphasise the difference between two
selected measure values (as opposed to min/max) or where the
underlying data is a change over time between two observations. ‘Band
charts’ will often be used to show how the range of data values has
changed over time, displaying the minimum and maximum bands at
each time unit. These are often used in displays like weather forecasts.
Charts Distributions
Box-and-whisker plot
ALSO KNOWN AS Box plot
REPRESENTATION DESCRIPTION
A box-and-whisker plot displays the distribution and shape of a series
of quantitative values for different categories. The display is formed by
a combination of lines and point markers to indicate (through position
and length), typically, five different statistical measures. Three of the
statistical values are common to all plots: the first quartile (25th
percentile), the second quartile (or median) and the third quartile (75th
percentile) values. These are displayed with a box (effectively a wide
bar) positioned and sized according to the first and third quartile values
with a marker indicating the median. The remaining two statistical
values vary in definition: usually either the minimum and maximum
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values or the 10th and 90th percentiles. These statistical values are
represented by extending a line beyond the bottom and top of the main
box to join with a point marker indicating the appropriate position.
These are the whiskers. A plot will be produced for each major
category.
EXAMPLE Comparing the distribution of annual earnings 10 years
after starting school for graduates across the eight Ivy League schools.
Figure 6.18 Ranking the Ivies
HOW TO READ IT & WHAT TO LOOK FOR
Begin by looking at the axes so you know with which category each
plot is associated and what the range of quantitative values is (min to
max). Establish the specific statistics being displayed, by consulting any
legends or descriptions, especially in order to identify what the
‘whiskers’ are representing. Glance across the entire chart to locate the
main patterns of spread, identifying any common or noticeably different
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patterns across categories. Look across the shapes formed for each
category to learn about the dispersal of values: starting with the median,
then observing the extent and balance of the ‘box’ (the interquartile
range between the 25th and 75th percentiles) and then check the
‘whisker’ extremes. Is the shape balanced or skewed around the
median? Is the interquartile range wide or narrow? Are the whisker
extremes far away from the edges of the box? Then return to comparing
shapes across all categories to identify more precisely any interesting
differences or commonalities for each of the five statistical measures.
PRESENTATION TIPS
ANNOTATION: If you have axis labels you may not need direct labels
on each bar – this will lead to label overload, so generally decide
between one or the other.
COMPOSITION: The quantitative value axis does not need to
commence from zero, unless it means something significant to the
interpretation, as the range of values themselves do not necessarily start
from zero and the focus is on the statistical properties between the outer
values. There is no significant difference in perception between vertical
or horizontal box-and-whisker plots, though horizontal layouts tend to
make it easier to accommodate and read the category labels. Try to keep
a noticeable gap between plots to enable greater clarity in reading.
When you have several or many plots in the same chart, where possible
try to make the categorical sorting meaningful, maybe organising values
in ascending/descending order based on the median value.
VARIATIONS & ALTERNATIVES
Variations involve reducing the number of statistical measures included
in the display by removing the whiskers to just show the 25th and 75th
percentiles through the lower and upper parts of the box. The
‘candlestick chart’ (or OHLC chart) involves a similar approach and is
often used in finance to show the distribution and milestone values of
stock performances during a certain time frame (usually daily), plotting
the opening, highest, lowest and closing prices, using colour to indicate
an up or down trend.
Charts Distributions
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Univariate scatter plot
ALSO KNOWN AS 1D scatter plot, jitter plot
REPRESENTATION DESCRIPTION
A univariate scatter plot displays the distribution of a series of
quantitative values for different categories. In contrast to the box-and-
whisker plot, which shows selected statistical values, a univariate
scatter plot shows all values across a series. For each category, a range
of points (typically circles but any ‘symbol’ is legitimate) are used to
mark the position along the scale of the quantitative values. From this
you can see the range, the outliers and the clusters and form an
understanding about the general shape of the data.
EXAMPLE Comparing the distribution of average critics score (%)
from the Rotten Tomatoes website for each movie released across a
range of different franchises and movie theme collections.
Figure 6.19 Comparing Critics Scores for Major Movie Franchises
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know what each scatter row/column relates to
in terms of which category it is associated with and what the range of
the quantitative values is (min to max). If colour has been used to
emphasise or separate different marks, establish what the associations
are. Also, learn about how the design depicts multiple marks on the
same value – these may appear darker or indeed larger. Glance across
the entire chart to observe the main patterns of clustering and identify
any noticeable exceptions and/or outliers across all categories. Then
look more closely at the patterns within each scatter to learn about each
category’s specific dispersal of values. Look for empty regions where
no quantitative values exist. Estimate the absolute values of specific
dots of interest. Where available, compare the quantities against
annotated references such as the average or median.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like gridlines can be helpful
to increase the accuracy of the reading of the quantitative values. Direct
labelling is normally restricted to including values for specifically
noteworthy points only.
COLOUR: Colour may be used to establish focus of certain points
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and/or distinction between different sub-category groups to assist with
interpretation. When several points have the exact same value you
might need to use unfilled or semi-transparent filled circles to facilitate
a sense of value density.
COMPOSITION: The representation of the quantitative values is
based on position and not size, therefore the quantitative axis does not
need to have a zero origin. There is no significant difference in
perception between vertical or horizontal arrangement, though
horizontal layouts tend to make it easier to accommodate and read the
category labels. Where possible try to make the categorical sorting
meaningful, maybe organising values in ascending/descending size
order.
VARIATIONS & ALTERNATIVES
To overcome occlusion caused by plotting several marks at the same
value, a variation of the univariate scatter plot may see the points
replaced by geometric areas (like circles), where the position attribute is
used to represent a quantitative value along a scale and the size attribute
is used to indicate the frequency of observations of similar value.
Adding a second quantitative variable axis would lead to the use of a
’scatter plot’.
Charts Distributions
Histogram
ALSO KNOWN AS Bar chart (wrongly)
REPRESENTATION DESCRIPTION
A histogram displays the frequency and distribution for a range of
quantitative groups. Whereas bar charts compare quantities for different
categories, a histogram technically compares the number of
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observations across a range of value ‘bins’ using the size of lines/bars
(if the bins relate to values with equal intervals) or the area of
rectangles (if the bins have unequal value ranges) to represent the
quantitative counts. With the bins arranged in meaningful order (that
effectively form ordinal groupings) the resulting shape formed reveals
the overall pattern of the distribution of observations.
EXAMPLE Comparing the distribution of movies released over time
starring Michael Caine across five-year periods based on the date of
release in the US.
Figure 6.20 A Career in Numbers: Movies Starring Michael Caine
HOW TO READ IT & WHAT TO LOOK FOR
Begin by looking at the axes so you know what the chart depicts in
terms of the categorical bins and the range of the quantitative values
(zero to max). Glance across the entire chart to establish the main
pattern. Is it symmetrically shaped, like a bell or pyramid (around a
median or average value)? Is it skewed to the left or right? Does it dip
in the middle and peak at the edges (known as bimodal)? Does it have
several peaks and troughs? Maybe it is entirely random in its pattern?
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All these characteristics of ‘shape’ will inform you about the underlying
distribution of the data.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines
in particular can be helpful to increase the accuracy of the reading of
the quantitative values. Axis labels more than direct value labels tend to
be used so as not to crowd the shape of the histogram.
COMPOSITION: Unlike the bar chart there should be no (or at most a
very thin) gap between bars to help the collective shape of the
frequencies emerge. The sorting of the quantitative bins must be in
ascending order so that the reading of the overall shape preserves its
meaning. The number of value bins and the range of values covered by
each have a prominent influence over the appearance of the histogram
and the usefulness of what it might reveal: too few bins may disguise
interesting nuances, patterns and outliers; too many bins and the most
interesting shapes may be abstracted by noise above signal. There is no
singular best approach, the right choice simply arrives through
experimentation and iteration.
VARIATIONS & ALTERNATIVES
For analysis that looks at the distribution of values across two
dimensions, such as the size of populations for age across genders, a
‘back-to-back histogram’ (with male on one side, female on the other),
also commonly known as a ‘violin plot’ or ‘population pyramid’, is a
useful approach to see and compare the respective shapes. A ‘box-and-
whisker plot’ reduces the distribution of values to five key statistical
measures to describe key dimensions of the spread of values.
Charts Distributions
Word cloud
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ALSO KNOWN AS Tag cloud
REPRESENTATION DESCRIPTION
A word cloud shows the frequency of individual word items used in
textual data (such as tweets, comments) or documents (passages,
articles). The display is based around an enclosed cluster of words with
the font (not the word length) sized according to the frequency of usage.
In modifying the size of font this is effectively increasing the area size
of the whole word. All words have a different shape and size so this can
make it quite difficult to avoid the prominence of long words,
irrespective of their font size. Word clouds are therefore only useful
when you are trying to get a quick and rough sense of some of the
dominant keywords used in the text. They can be an option for working
with qualitative data during the data exploration stage, more so as a
means for reporting analysis to others.
EXAMPLE Comparing the frequency of words used in Chapter 1 of
this book.
Figure 6.21 Word Cloud of the Text from Chapter 1
HOW TO READ IT & WHAT TO LOOK FOR
The challenge with reading word clouds is to avoid being drawn to the
length and/or area of a word – they are simply attributes of the word,
not a meaningful representation of frequency. It is the size of the font
that you need to focus on. Scan the display to spot the larger text
showing the more frequently used words. Consider any words of
specific interest to see if you can find them; if they are not significantly
visible, that in itself could be revealing. While most word cloud
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generators will dismiss many irrelevant words, you might still need to
filter out perceptually the significance of certain dominantly sized text.
PRESENTATION TIPS
INTERACTIVITY: Interactive features that let users interrogate, filter
and scrutinise the words in more depth, perhaps presenting examples of
their usage in a passage, can be quite useful to enhance the value of a
word cloud.
ANNOTATION: While the absolutes are generally of less interest than
relative comparisons, to help viewers get as much out of the display as
possible a simple legend explaining how the font size equates to
frequency number can be useful.
COLOUR: Colours may be used as redundant encoding to accentuate
further the larger frequencies or categorically to create useful visual
separation.
COMPOSITION: The arrangement of the words within a word cloud
is typically based on a layout process. Although not random, this will
generally prioritise the placement of words to occupy optimum
collective space that preserves an overall shape (with essentially a
central gravity) over and above any arrangement that might better
enable direct comparison.
VARIATIONS & ALTERNATIVES
The alternative approach would be to use any other method in this
categorical family of charts that would more usefully display the counts
of text, such as a bar chart.
Charts Part-to-whole
Pie chart
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ALSO KNOWN AS Pizza chart
REPRESENTATION DESCRIPTION
A pie chart shows how the quantities of different constituent categories
make up a whole. It uses a circular display divided into sectors for each
category, with the angle representing each of the percentage
proportions. The resulting size of the sector (in area terms) is a spatial
by-product of the angle applied to each part and so offers an additional
means for judging the respective values. The role of a pie chart is
primarily about being able to compare a part to a whole than being able
to compare one part to another part. They therefore work best when
there are only two or three parts included. There are a few important
rules for pie charts. Firstly, the total percentage values of all sector
values must be 100%; if the aggregate is greater than or less than 100%
the chart will be corrupted. Secondly, the whole has to be meaningful –
often people just add up independent percentages but that is not what a
pie chart is about. Finally, the category values must represent exclusive
quantities; nothing should be counted twice or overlap across different
categories. Despite all these warnings, do not be afraid of the pie chart –
just use it with discretion.
EXAMPLE Comparing the proportion of eligible voters in the 2015
UK election who voted for the Conservative Party, for other parties and
who did not vote.
Figure 6.22 Summary of Eligible Votes in the UK General Election
2015
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HOW TO READ IT & WHAT TO LOOK FOR
Begin by establishing which sectors relate to what categories. This may
involve referring to a colour key legend or through labels directly
adjacent to the pie. Quickly scan the pie to identify the big, medium and
small sectors. Notice if there is any significance behind the ordering of
the parts. Unless there are value labels, you next will attempt to judge
the individual sector angles. This usually involves mentally breaking
the pie into 50% halves (180°) or 25% quarters (90°) and using those
guides to perceptually measure the category values. Comparing parts
against other parts with any degree of accuracy will only be possible
once you have formed estimates of the individual sector sizes. If you
are faced with the task of judging the size of many parts it is quite
understandable if you decide to give up quite soon.
PRESENTATION TIPS
ANNOTATION: The use of local labelling for category values can be
useful but too many labels can become cluttered, especially when
attempting to label very small angled sectors.
COLOUR: Colour is generally vital to create categorical separation
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and association of the different sectors so aim to use the difference in
colour hue and not colour saturation to maximise the visible difference.
COMPOSITION: Positioning the first slice at the vertical 12 o’clock
position gives a useful baseline to help judge the first sector angle
value. The ordering of sectors using descending values or ordinal
characteristics helps with the overall readability and allocation of effort.
Do not consider using gratuitous decoration (like 3D, gradient colours,
or exploding slices).
VARIATIONS & ALTERNATIVES
Sometimes a pie chart has a hole in the centre and is known as a
‘doughnut chart’, continuing the food-related theme. The function is
exactly the same as a pie but the removal of the centre, often to
accommodate a labelling property, removes the possibility of the reader
judging the angles at the origin. One therefore has to derive the angles
from the resulting arc lengths. If you want to display multiple parts
(more than three) the bar chart will be a better option and, for many
parts, the ‘treemap’ is best. Depending on the allocated space, a
‘stacked bar chart’ may provide an alternative to the pie. Unlike most
chart types, the pie chart does not work well in the form of small
multiples (unless there is only a single part being displayed). A ‘nested
shape chart’, typically based on embedded square or circle areas,
enables comparison across a series of one-part-to-whole relationships
based on absolute numbers, rather than percentages, where the wholes
may vary in size.
Charts Part-to-whole
Waffle chart
ALSO KNOWN AS Square pie, unit chart, 100% stacked shape chart
REPRESENTATION DESCRIPTION
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A waffle chart shows how the quantities of different constituent
categories make up a whole. It uses a square display usually
representing 100 point ‘cells’ through a 10 × 10 grid layout. Each
constituent category proportion is displayed through colour-coding a
proportional number of cells. Difference in symbol can also be used.
The role of the waffle chart is to simplify the counting of proportions in
contrast to the angle judgements of the pie chart, though the display is
limited to rounded integer values. This is easier when the grid layout
facilitates quick recognition of units of 10. As with the pie chart, the
waffle chart works best when you are showing how a single part
compares to the whole and perhaps offers greater visual impact when
there are especially small percentages of a whole. Rather than just
colouring in the grid cells, sometimes different symbols will be used to
associate with different categories. For example, you might see figures
or gender icons used to show the makeup of a given sample population.
EXAMPLE Comparing the proportion of total browser usage for
Internet Explorer and Chrome across key milestone moments.
Figure 6.23 The Changing Fortunes of Internet Explorer and Google
Chrome
HOW TO READ IT & WHAT TO LOOK FOR
Begin by establishing how the different shapes or colours are associated
with different categories. Assess the grid layout to understand the
dimension of the chart and the quantity of cell ‘units’ forming the
display (e.g. is it a 10 x 10 grid?). Quickly scan the chart to identify the
big, medium and small sectors. Notice if there is any significance
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behind the ordering of the parts. Unless there are value labels, you will
need to count/estimate the number of units representing each category
value. Comparing parts against other parts will only be possible once
you have established the individual part sizes. If several related waffle
charts are shown, possibly for different categories or points in time,
identify the related colours/shapes in each chart and establish the
patterns of size between and across the various charts, looking for
trends, declines and general differences.
PRESENTATION TIPS
ANNOTATION: Direct labelling can become very cluttered and hard
to incorporate elegantly without the need for long arrows.
COLOUR: Borders around each square cell are useful to help establish
the individual units, but do not make the borders too thick to the point
where they dominate attention.
COMPOSITION: Always start each row of values from the same side,
for consistency and to make it easier for people to estimate the values.
When you have several parts in the same waffle chart, where possible
try to make the categorical sorting meaningful, maybe organising values
in ascending/descending size order or based on a logical categorical
order.
VARIATIONS & ALTERNATIVES
Sometimes the waffle chart approach is used to show stacks of absolute
unit values and indeed there are overlaps in concept between this
variation in the waffle chart and potential applications of the pictogram.
Aside from the pie chart, a ‘nested shape chart’ will provide an
alternative way of showing a part-to-whole relationship while also
occupying a squarified layout.
Charts Part-to-whole
Stacked bar chart
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ALSO KNOWN AS
REPRESENTATION DESCRIPTION
A stacked bar chart displays a part-to-whole breakdown of quantitative
values for different major categories. The percentage proportion of each
categorical dimension or ‘part’ is represented by separate bars,
distinguished by colour, that are sized according to their proportion and
then stacked to create the whole. Sometimes the whole is standardised
to represent 100%, at other times the whole will be representative of
absolute values. Stacked bar charts work best when the parts are based
on ordinal dimensions, which enables ordering of the parts within the
stack to help establish the overall shape of the data. If the parts are
representative of nominal data, it is best to keep the number of
constituent categories quite low, as estimating the size of individual
stacked parts when there are many becomes quite hard.
EXAMPLE Comparing the percentage of adults (16–65 year olds)
achieving different proficiency levels in literacy across different
countries.
Figure 6.24 Literarcy Proficiency: Adult Levels by Country
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with what major categorical values each
bar is associated and what the quantitative values are, determining if it
is a 100% stacked bar or an absolute stacked bar (in which case identify
the min and the max). Establish the colour association to understand
what categories the bars within each stack represent. Glance across the
entire chart. If the categorical data is ordinal, and the sorting/colour of
the stacks is intuitive, you should be able to derive meaning from the
overall balance of colour patterns, especially where any annotated
gridlines help to guide your value estimation. If the categorical data is
nominal, seek to locate the dominant colours and the least noticeable
ones. Comparing across different stacked bars is made harder by the
lack of a common baseline for anything other than the bottom stack on
the zero baseline (and for 100% stacked bars, those final ones at the
top) and so a general sense of magnitude will be your focus. Study
closer the constituent parts within each stack to establish the high-level
ranking of biggest > smallest. Estimate (or read, if labels are present)
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the absolute values of specific stacked parts of interest.
PRESENTATION TIPS
ANNOTATION: Direct value labelling can become very cluttered
when there are many parts or stacks and you are comparing several
different major categories. You might be better with a table if that is
your aim. Definitely include value axis labels with logical intervals and
it is very helpful to annotate, through gridlines, key units such as the
25%, 50% and 75% positions when based on a 100% stacked bar chart.
COLOUR: If you are representing categorical ordinal data, colour can
be astutely deployed to give a sense of the general balance of values
within the whole, but this will only work if their sorting arrangement
within the stack is logically applied. For categorical nominal data,
ensure the stacked parts have sufficiently different colours so that their
distinct bar lengths can be efficiently observed.
COMPOSITION: Across the main categories, once again consider the
optimum sorting option, maybe organising values in
ascending/descending size order or based on a logical categorical order.
Judging the size of the stacks with accuracy is harder for those that are
not on the zero baseline, so maybe consider which ones are of most
importance to be more easily read and place those on the baseline.
VARIATIONS & ALTERNATIVES
The main alternative would be to use ‘multi-panel bar charts’, where
separate bar charts each include just one ’stack’/part and they are then
repeated for each subsequent constituent category. In the world of
finance the ‘waterfall chart’ is a common approach based on a single
stacked bar broken up into individual elements, almost like a step-by-
step narrative of how the components of income look on one side and
then how the components of expenditure look on the other, with the
remaining space representing the surplus or deficit. Like their unstacked
siblings, stacked bar charts can also be used to show how categorical
composition has changed over time.
Charts Part-to-whole
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Back-to-back bar chart
ALSO KNOWN AS Paired bar chart
REPRESENTATION DESCRIPTION
A back-to-back bar chart displays a part-to-whole breakdown of
quantitative values for different major categories. As with any bar chart,
the length of a bar represents a quantitative proportion or absolute value
for each part and across all major categories. In contrast to the stacked
bar chart, where the constituent bars are simply stacked to form a
whole, in a back-to-back bar chart the constituent parts are based on
diverging categorical dimensions with a ‘directional’ essence such as
yes/no, male/female, agree/disagree. The values for each dimension are
therefore presented on opposite sides of a shared zero baseline to help
reveal the shape and contrast differences across all major categories.
EXAMPLE Comparing the responses to a survey question asking for
opinions about ‘the government collection of telephone and Internet
data as part of anti-terrorism efforts’ across different demographic
categories.
Figure 6.25 Political Polarization in the American Public
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with which major categorical values each
bar is associated and what the range of the quantitative values is (min to
max). Establish what categorical dimensions are represented by the
respective sides of the display and any colour associations. Glance
across the entire chart to locate the big, small and medium bars and
perform global comparisons to establish the high-level ranking of
biggest > smallest. Repeat this for each side of the display, noticing any
patterns of dominance of larger values on either side. Identify any
noticeable exceptions and/or outliers. Perform local comparisons for
each category value to estimate the relative sizes (or read, if labels are
present) of each bar.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines
in particular can be helpful to increase the accuracy of the reading of
the quantitative values.
COLOUR: The bars either side of the axis do not need to be coloured
but often are to create further visual association.
COMPOSITION: The quantitative value axis should always start from
the origin value of zero: a bar should be representative of the true, full
quantitative value, nothing more, nothing less, otherwise the perception
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of bar sizes will be distorted when comparing relative sizes. There is no
significant difference in perception between vertical or horizontal bars,
though horizontal layouts tend to make it easier to accommodate and
read the category labels. Where possible try to make the categorical
sorting meaningful, maybe organising values in ascending/descending
size order or based on a logical categorical order.
VARIATIONS & ALTERNATIVES
Back-to-back bar charts facilitate a general sense of the shape of
diverging categorical dimensions. However, if you want to facilitate
direct comparison, a ‘clustered bar chart’ showing adjacent bars helps
to compare respective heights more precisely. For analysis that looks at
the distribution values across two dimensions, such as the size of
populations for age across genders, a ‘back-to-back histogram’ (with
male on one side, female on the other), also commonly known as a
‘violin plot’ or ‘population pyramid’, is a useful approach to see and
compare the respective shapes. Some back-to-back applications do not
show a part-to-whole relationship but simply compare quantities for
two categorical values. Further variations may appear as ‘back-to-back
area charts’ showing mutual change over time for two contrasting
states.
Charts Part-to-whole
Treemap
ALSO KNOWN AS Heat map (wrongly)
REPRESENTATION DESCRIPTION
A treemap is an enclosure diagram providing a hierarchical display to
show how the quantities of different constituent parts make up a whole.
It uses a contained rectangular layout (often termed ‘squarified’)
representing the 100% total divided into proportionally sized
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rectangular tiles for each categorical part. Colour can be used to
represent an additional quantitative measure, such as an indication of
amount of change over a time period. The absolute positioning and
dimension of each rectangle is organised by an underlying tiling
algorithm to optimise the overall space usage and to cluster related
categories into larger rectangle-grouped containers. Treemaps are most
commonly used, and of most value, when there are many parts to the
whole but they are only valid if the constituent units are legitimately
part of the same ‘whole’.
EXAMPLE Comparing the relative value of and the daily performance
of stocks across the S&P 500 index grouped by sectors and industries.
Figure 6.26 FinViz: Standard and Poor’s 500 Index
HOW TO READ IT & WHAT TO LOOK FOR
Look at the high-level groupings to understand the different containing
arrangements and establish what the colour association is. Glance
across the entire chart to seek out the big, small and medium individual
rectangular sizes and perform global comparisons to establish a general
ranking of biggest > smallest values. Also identify the largest through
to smallest container group of rectangles. If the colour coding is based
on quantitative variables, look out for the most eye-catching patterns at
the extreme end of the scale(s). If labels are provided (or offered
through interactivity), browse around the display looking for categories
and values of specific interest. As with any display based on the size of
the area of a shape, precise reading of values is hard to achieve and so it
is important to understand that treemaps can only aim to provide a
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single-view gist of the properties of the many components of the whole.
PRESENTATION TIPS
INTERACTIVITY: Typically, a treemap will be presented with
interactive features to enable selection/mouseover events to reveal
further annotated details and/or drill-down navigation.
ANNOTATION: Group/container labels are often allocated a cell of
space but these are not to be read as proportional values. Effective
direct value labelling becomes difficult as the rectangles get smaller, so
often only the most prominent values might be annotated. Interactive
features will generally offer visibility of the relevant labels where
possible.
COLOUR: Colour can also be used to provide further categorical
grouping distinction if not already assigned to represent a quantitative
measure of change.
COMPOSITION: As the tiling algorithm is focused on optimising the
dimensions and arrangement of the rectangular shapes, treemaps may
not always be able to facilitate much internal sorting of high to low
values. However, generally you will find the larger shapes appear in the
top left of each container and work outwards towards the smaller
constituent parts.
VARIATIONS & ALTERNATIVES
A variation of the treemap sees the rectangular layout replaced by a
circular one and the rectangular tiles replaced by organic shapes. These
are known as ‘Voronoi treemaps’ as the tiling algorithm is informed by
a Voronoi tessellation. The ‘circle packing diagram’, a variation of the
‘bubble chart’, similarly shows many parts to a whole but uses a non-
tessellating circular shape/layout. The ‘mosaic plot’ or ‘Marimekko
chart’ is similar in appearance to a treemap but, in contrast to the
treemap’s hierarchical display, presents a detailed breakdown of
quantitative value distributions across several categorical dimensions,
essentially formed by varied width stacked bars.
Charts Part-to-whole
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Venn diagram
ALSO KNOWN AS Set diagram, Euler diagram (wrongly)
REPRESENTATION DESCRIPTION
A Venn diagram shows collections of and relationships between
multiple sets. They typically use round or elliptical containers to
represent all different ‘membership’ permutations to include all
independent and intersecting containers. The size of the contained area
is (typically) not important: what is important is in which containing
region a value resides, which may be represented through the mark of a
text label or ‘point’.
EXAMPLE Comparing sets of permutations for legalities around
marijuana usage and same-sex marriage across states of the USA.
Figure 6.27 This Venn Diagram Shows Where You Can Both Smoke
Weed and Get a Same-Sex Marriage
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HOW TO READ IT & WHAT TO LOOK FOR
To read a Venn diagram firstly establish what the different containers
are representative of in terms of their membership. Assess the
membership of the intersections (firstly ‘all’, then ‘partial’ intersections
when involving more than two sets) then work outwards towards the
independent container regions where values are part of one set but not
part of others. Occasionally there will be a further grouping state
outside of the containers that represents values that have no
membership with any set at all.
PRESENTATION TIPS
ANNOTATION: Unless you are using point markers to represent
membership values, clear labels are vital to indicate how many or which
elements hold membership with each possible set combination.
COLOUR: Colour is often used to create more immediate distinction
between the intersections and independent parts or members of each
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container.
COMPOSITION: As the attributes of size and shape of the containers
are of no significance there is more flexibility to manipulate the display
to fit the number of sets around the constraint of real estate you are
facing and to get across the set memberships you are attempting to
show. The complexity of creating containers to accommodate all
combinations of intersection and independence states increases as the
number of sets increases, especially to preserve all possible
combinations of intersections between and independencies from all sets.
As the number of sets increases, the symmetry of shape reduces and the
circular containers are generally replaced with ellipses. While it is
theoretically possible to exceed four and five set diagrams, the ability of
readers to make sense of the displays diminishes and so they commonly
involve only two or three different sets.
VARIATIONS & ALTERNATIVES
A common variation or alternative to the Venn (but often mistakenly
called a Venn) is the ‘Euler diagram’. The difference is that an Euler
diagram does not need to present all possible intersections with and
independencies from all sets. A different approach to visualising sets
(especially larger numbers) can be achieved using the ‘UpSet’
technique.
Charts Hierarchies
Dendrogram
ALSO KNOWN AS Node–link diagram, layout tree, cluster tree, tree
hierarchy
REPRESENTATION DESCRIPTION
A dendrogram is a node–link diagram that displays the hierarchical
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relationship across multiple tiers of categorical dimensions. It displays a
hierarchy based on multi-generational ‘parent-and-child’ relationships.
Starting from a singular origin root node (or ‘parent’) each subsequent
set of constituent ‘child’ nodes, a tier below and represented by points,
is connected by lines (curved or straight) to indicate the existence of a
relationship. Each constituent node may have further sub-constituencies
represented in the same way, continuing down through to the lowest tier
of detail. Each ‘generational’ tier is presented at the same relative
distance from the origin. The layout can be based on either a linear tree
structure (typically left to right) or radial tree (outwards from the
centre).
EXAMPLE Showing a breakdown of the 200+ beer brands belonging
to SAB InBev across different countries grouped by continent.
Figure 6.28 The 200+ Beer Brands of SAB InBev
HOW TO READ IT & WHAT TO LOOK FOR
290

Reading a dendrogram will generally be a highly individual experience
based on your familiarity with the subject and your interest in exploring
certain hierarchical pathways. The main focus of attention will likely be
to find the main clusters from where most constituent parts branch out
and to contrast these with the thinner, lighter paths comprising fewer
parts. Work left to right (linear) or in to out (radial) through the
different routes that stoke your curiosity.
PRESENTATION TIPS
ANNOTATION: With labelling required for each node, depending on
the number of tiers and the amount of nodes, the size of the text will
need to be carefully considered to ensure readability and minimise the
effect of clutter.
COLOUR: Colour would be an optional choice for accentuating certain
nodes or applying some further visual categorisation.
COMPOSITION: There are several different layout options to display
tree hierarchies like the dendrogram. The common choice is a cluster
layout based on the ‘Reingold–Tilford’ tree algorithms that offers a
tidying and optimisation treatment for the efficiency of the arrangement
of the nodes and connections. The sequencing of sub-constituencies
under each node could be logically arranged in some more meaningful
way than just alphabetical, though the cataloguing nature of A–Z may
suit your purpose. The choice of a linear or radial tree structure will be
informed largely by the space you have to work in as well as by the
cyclical or otherwise nature of the content in your data. The main issue
is likely to be one of legibility if and when you have numerous layers of
divisions and many constituent parts to show in a single view.
VARIATIONS & ALTERNATIVES
More advanced applications of dendrograms are used to present
hierarchical clustering (in fields such as computational biology) and
apply more quantitative meaning to the length of the links and the
positioning of the nodes. The ‘tree hierarchy diagram’ offers a similar
tree structure but introduces quantitative attributes to the nodes using
area marks, such as circles, sized according to a quantitative value. An
alternative approach to the dendrogram could involve a ‘linear bracket’.
This might show hierarchical structures for data-related sporting
competitions with knock-out format. The outer nodes would be the
starting point representing all the participating competitors/teams. Each
subsequent tier would represent those participants who progressed to
the next round, continuing through to the finalists and eventual victors.
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Charts Hierarchies
Sunburst
ALSO KNOWN AS Adjacency diagram, icicle chart, multi-level pie
chart
EXAMPLE Showing a breakdown of the types of companies
responsible for extracting different volumes of carbon-based fuels
through various activities.
REPRESENTATION DESCRIPTION
A sunburst chart is an adjacency diagram that displays the hierarchical
and part-to-whole relationships across multiple tiers of categorical
dimensions. In contrast to the dendrogram, the sunburst uses layers of
concentric rings, one layer for each generational tier. Each ring layer is
divided into parts based on the constituent categorical dimensions at
that tier. Each part is represented by a different circular arc section that
is sized (in length; width is constant) according to the relative
proportion. Starting from the centre ‘parent’ tier, the outward adjacency
of the constituent parts of each tier represents the ‘parent-and-child’
hierarchical composition.
Figure 6.29 Which Fossil Fuel Companies are Most Responsible for
Climate Change?
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HOW TO READ IT & WHAT TO LOOK FOR
Reading a sunburst chart will be a highly individual experience based
on your familiarity with the subject and your interest in exploring
certain hierarchical pathways. The main focus of attention will likely be
to find the largest arc lengths, representing the largest single constituent
parts, and those layers or tiers with the most constituent parts. Work
from the centre outwards through the different routes that stoke your
curiosity. Depending on the deployment of colour, this may help you
identify certain additional categorical patterns.
PRESENTATION TIPS
INTERACTIVITY: Often interactive mouseover/selection events are
the only way to reveal the annotations here.
ANNOTATION: Labelling can be quite difficult to fit into the narrow
spaces afforded by small proportion ‘parts’. If interactivity is not an
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option you may decide to label only those parts that can accommodate
the text space.
COLOUR: Colours are often used to achieve further categorical
distinction.
COMPOSITION: Sometimes the parent–child (and other generational)
relationships could be legitimately reversed, so decisions need to be
made about the best hierarchy sequencing to suit the curiosities of the
audience. The sequencing of sub-constituencies under each node could
also be logically arranged in a meaningful way, more so than just
alphabetical, unless the cataloguing nature of A–Z ordering suits your
purpose.
VARIATIONS & ALTERNATIVES
Where the sunburst chart uses a radial layout, the ‘icicle chart‘ uses a
vertical, linear layout starting from the top and moving downwards. The
choice of a linear or radial tree structure will be informed largely by the
space you have to work in as well as by the legitimacy of the cyclical
nature of the content in your data. A variation on the sunburst chart
would be the ‘ring bracket’. This might show a reverse journey for
hierarchical data based on something like sporting competitions with
knock-out formats. The outer concentric partitions would represent the
participant competitors/teams at the start of the process. The length of
these arc line parts would be equally distributed across all constituent
parts with each subsequent tier representing ‘participants’ who progress
forward to the next ‘round’, continuing through to the finalists and
eventual victors in the centre.
Charts Correlations
Scatter plot chart
ALSO KNOWN AS Scatter graph
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REPRESENTATION DESCRIPTION
A scatter plot displays the relationship between two quantitative
measures for different categories. Scatter plots are used to explore
visually the potential existence, extent or absence of a significant
relationship between the plotted variables. The display is formed by
points (usually a dot or circle), representing each category and plotted
positionally along quantitative x- and y-axes. Sometimes colour is used
to distinguish categorical dimensions across all the points. Scatter plots
do not work too well if one or both of the quantitative measures has
limited variation in value as this especially causes problems of
‘occlusion’, whereby multiple instances of the similar values are plotted
on top of each other and essentially hidden from the reader.
EXAMPLE Exploring the relationship between life expectancy and the
percentage of healthy years across all countries.
Figure 6.30 How Long Will We Live — And How Well?
HOW TO READ IT & WHAT TO LOOK FOR
Learn what each quantitative axis relates to and make a note of the
range of values in each case (min to max). Look at what category or
observation each plotted value on the chart refers to and look up any
colour associations being used for categorical distinction. Scan the chart
looking for the existence of any diagonal trends that might suggest a
295

linear correlation between the variables, or note the complete absence
of any pattern, to mean no correlation. Annotations will often assist in
determining the significance of any patterns like this. Identify any
clusters of points and also look at the gaps, which can be just as
revealing. Some of the most interesting observations come from
individual outliers standing out separately from others. Look out for any
patterns formed by points with similar categorical colour. One approach
to reading the ‘meaning’ of the plotted positions involves trying to
break down the chart area into a 2 × 2 grid translating what marks
positioned in those general areas might mean – which corner is ‘good’
or ‘bad’ to be located in? Remember that ruling out significant
relationships can be just as useful as ruling them in.
PRESENTATION TIPS
ANNOTATION: Gridlines can be useful to help make the value
estimates clearer and reference lines (such as a trend line of best fit)
might aid interpretation. It is usually hard to make direct labelling of all
values work well. Firstly, it can be tricky making it clear which value
relates to which point, especially when several points may be clustered
together. Secondly, it creates a lot of visual clutter. Labelling choices
should be based on values that are of most interest to include editorially
unless interactive features enable annotations to be revealed through
selection or mouseover events. If possible, you might consider putting a
number inside the marker to indicate a count of the number of points at
the same position if this occurs.
COLOUR: If colours are being used to distinguish the different
categories, ensure these are as visibly different as possible. On the
occasion where multiple values may be plotted close to or on top of
each other, you might need to use semi-transparency to enable
overlapping of points to build up a recognisably darker colour
compared to other points, indicating an underlying stack of values at the
same location on the chart.
COMPOSITION: As the encoding of the plotted point values is based
on position along an axis, it is not necessary to start the axes from a
zero baseline, so just make the scale ranges as representative as possible
of the range of values being plotted. Ideally a scatter plot will have a
1:1 aspect ratio (equally as tall as it is wide), creating a squared area to
help patterns surface more evidently. If one quantitative variable (e.g.
weight) is likely to be affected by the other variable (e.g. height), it is
general practice to place the former on the y-axis and the latter on the x-
axis. If you have to use a logarithmic quantitative scale on either or both
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axes, you need to make this clear to readers so they avoid making
incorrect conclusions from the resulting patterns (that might imply
correlation if the values were linear, for example).
VARIATIONS & ALTERNATIVES
A ‘ternary plot’ is a variation of the scatter plot through the inclusion of
a third quantitative variable axis. The ‘bubble plot’ also incorporates a
third quantitative variable, this time through encoding the size of a
geometric shape (replacing the point marker). A ‘scatter plot matrix’
involves a single view of multiple scatter plots presenting different
combinations of plotted quantitative variables, used to explore possible
relationships among larger multivariate datasets. A ‘connected scatter
plot’ compares the shifting state of two quantitative measures over time.
Charts Correlations
Bubble plot
ALSO KNOWN AS Bubble chart
REPRESENTATION DESCRIPTION
A bubble plot displays the relationship between three quantitative
measures for different categories. Bubble plots are used visually to
explore the potential existence, extent or absence of a significant
relationship between the plotted variables. In contrast to the scatter plot,
the bubble plot plots proportionally sized circular areas, for each
category, across two quantitative axes with the size representing a third
quantitative measure. Sometimes colour is used to distinguish
categorical dimensions across all the shapes.
EXAMPLE Exploring the relationship between rates of murders,
burglaries (per 100,000 population) and population across states of the
USA.
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Figure 6.31 Crime Rates by State
HOW TO READ IT & WHAT TO LOOK FOR
Learn what each quantitative axis relates to and make a note of the
range of values in each case (min to max). Look at what category or
observation each plotted value on the chart refers to. Establish the
quantitative size associations for the bubble areas and look up any
colour associations being used for categorical distinction. Scan the chart
looking for the existence of any diagonal trends that might suggest a
linear correlation between the variables, or note the complete absence
of any pattern, to mean no correlation. Annotations will often assist in
determining the significance of any patterns like this. Identify any
clusters of points and also look at the gaps, which can be just as
revealing. Some of the most interesting observations come from
individual outliers standing out separately from others. Look out for any
patterns formed by points with similar categorical colour. What can you
learn about the distribution of small, medium or large circles: are they
clustered together in similar regions of the chart or quite randomly
scattered? One approach to reading the ‘meaning’ of the plotted
positions involves trying to break down the chart area into a 2 × 2 grid
translating what marks positioned in those general areas might mean –
which corner is ‘good’ or ‘bad’ to be located in? Remember that ruling
out significant relationships can be just as useful as ruling them in.
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Estimating and comparing the size of areas is not as easy as it is for
judging bar length or dot position. This means that the use of this chart
type will primarily be about facilitating a gist – a general sense of the
hierarchy of the largest and smallest values.
PRESENTATION TIPS
ANNOTATION: Gridlines can be useful to help make the value
estimates clearer and reference lines (such as a trend line of best fit)
might aid interpretation. It is usually hard to make direct labelling of all
values work well. Firstly, it can be tricky making it clear which value
relates to which point, especially when several points may be clustered
together. Secondly, it creates a lot of visual clutter. Labelling choices
should be based on values that are of most interest to include editorially
unless interactive features enable annotations to be revealed through
selection or mouseover events.
COLOUR: If colours are being used to distinguish the different
categories, ensure these are as visibly different as possible. When a
circle has a large value its size will often overlap in spatial terms with
other values. The use of outline borders and semi-transparent colours
helps with the task of avoiding occlusion (visually hiding values behind
others).
COMPOSITION: As the encoding of the plotted area marker values is
based on position along an axis, it is not necessary to start the axes from
a zero baseline – just make the scale ranges as representative as possible
of the range of values being plotted. Make sensible decisions about how
large to make the maximum bubble size; this will usually require trial
and error experimentation to find the right balance. Ideally a bubble plot
will have a 1:1 aspect ratio (equally as tall as it is wide), creating a
squared area to help patterns surface more evidently. If one quantitative
variable (e.g. weight) is likely to be affected by the other variable (e.g.
height), it is general practice to place the former on the y-axis and the
latter on the x-axis. Geometric accuracy of the circle size calculations is
paramount, since mistakes are often made with circle size calculations:
it is the area you are modifying, not the diameter/radius. If you wish to
make your bubbles appear as 3D spheres you are essentially no longer
representing quantitative values through the size of a geometric area
mark, rather the mark will be a ‘form’ and so the size calculation will
be based on volume, not area.
VARIATIONS & ALTERNATIVES
If the third quantitative variable is removed, the display would just
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become a ‘scatter plot’. Variations on the bubble plot might see the use
of different geometric areas as the markers, maybe introducing extra
meaning from the underlying data through the shape, size and
dimensions used.
Charts Correlations
Parallel coordinates
ALSO KNOWN AS Parallel sets
REPRESENTATION DESCRIPTION
Parallel coordinates display multiple quantitative measures for different
categories in a single display. They are used visually to explore the
relationships and characteristics of multi-dimensional, multivariate data.
Parallel coordinates are based on a series of parallel axes representing
different quantitative measures with independent axis scales. The
quantitative values for each measure are plotted and then connected to
form a single line. Each connected line represents a different category
record. Colour may be used to differentiate further categorical
dimensions. As more data is added the collective ’shape’ of the data
emerges and helps to inform the possibility of relationships existing
among the different measures. Parallel coordinates look quite
overwhelming but remember that they are almost always only used to
assist in exploratory work of large and varied datasets, more so than
being used for explanatory presentations of data. Generally the greater
the number of measures, the more difficult the task of making sense of
the underlying patterns will be, so be discerning in your choice of
which variables to include. This method does not work for showing
categorical (nominal) measures nor does it really offer value with the
inclusion of low-range, discrete quantitative variables used (e.g. number
of legs per human). Patterns will mean very little when intersecting with
such axes (they may be better deployed as a filtering parameter or a
coloured categorical separator).
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EXAMPLE Exploring the relationship between nutrient contents for 14
different attributes across 1,153 different items of food.
Figure 6.32 Nutrient Contents — Parallel Coordinates
HOW TO READ IT & WHAT TO LOOK FOR
Look around the chart and acquaint yourself with what each
quantitative measure axis represents. Also note what kind of sequencing
of measure has been used: are neighbouring measures significantly
paired? Note the range of values along each independent axis so you
understand what positions along the scales represent and can determine
what higher and lower positions mean. If colour has been used to group
related records then identify what these represent. Scan the overall mass
of lines to identify any major patterns. Study the patterns in the space
between each pair of adjacent axes. This is where you will really see the
potential presence or absence of, and nature of, relationships between
measures. The main patterns to identify involve the presence of parallel
lines (showing consistent relationships), lines converging in similar
directions (some correlation) and then complete criss-crossing (negative
relationship). Look out for any associations in the patterns across colour
groupings. Remember that ruling out significant relationships can be
just as useful as ruling them in.
PRESENTATION TIPS
INTERACTIVITY: Parallel coordinates are particularly useful when
offered with interactive features, such as filtering techniques, enabling
the user to interrogate and manipulate the display to facilitate visual
exploration. Additionally, the option to rearrange the sequence of the
measures can be especially useful.
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ANNOTATION: The inclusion of visible annotated features like axis
lines, tick marks, gridlines and value labels can naturally aid the
readability of the data but be aware of the impact of clutter.
COLOUR: When you are plotting large quantities of records,
inevitably there will be over-plotting and this might disguise the real
weight of values, so the variation in the darkness of colour can be used
to establish density of observations.
COMPOSITION: The ordering of the quantitative variables has to be
of optimum significance as the connections between adjacent axes will
offer the main way of seeing the local relationships: the patterns will
change for every different ordering permutation. Remember that the
line directions connecting records are often inconsequential in their
meaning unless neighbouring measures have a common scale and
similar meaning: the connections are more about establishing
commonality of pattern across records, rather than there being anything
too significant behind the absolute slope direction/length.
VARIATIONS & ALTERNATIVES
The ‘radar chart’ has similarities with parallel coordinates in that they
include several independent quantitative measures in the same chart but
on a radial layout and usually only showing data for one record in the
same display. A variation on the parallel coordinate would be the
‘Sankey diagram’, which displays categorical composition and
quantitative flows between different categorical dimensions or ‘stages’.
Charts Correlations
Heat map
ALSO KNOWN AS Matrix chart, mosaic plot
REPRESENTATION DESCRIPTION
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A heat map displays quantitative values at the intersection between two
categorical dimensions. The chart comprises two categorical axes with
each possible value presented across the row and column headers of a
table layout. Each corresponding cell is then colour-coded to represent a
quantitative value for each combination of category pairing. It is not
easy for the eye to determine the exact quantitative values represented
by the colours, even if there is a colour scale provided, so heat maps
mainly facilitate a gist of the order of magnitude.
EXAMPLE Exploring the connections between different Avengers
characters appearing in the same Marvel comic book titles between
1963 and 2015.
Figure 6.33 How the ‘Avengers’ Line-up Has Changed Over the Years
HOW TO READ IT & WHAT TO LOOK FOR
Learn what each categorical dimension relates to and make a note of the
range of values in each case, paying attention to the significance of any
ordering. Establish the quantitative value associations for the colour
scales, usually found via a legend. Glance across the entire chart to
locate the big, small and medium shades (generally darker = larger) and
perform global comparisons to establish the high-level ranking of
biggest > smallest. Scan across each row and/or column to see if there
are specific patterns associated with either set of categories. Identify
any noticeable exceptions and/or outliers. Perform local comparisons
between neighbouring cell’s areas, to identify larger than and smaller
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than relationships and estimate the relative proportions. Estimate (or
read, if labels are present) the absolute values of specific colour scales
of interest.
PRESENTATION TIPS
ANNOTATION: Direct value labelling is possible, otherwise a clear
legend to indicate colour associations will suffice.
COLOUR: Sometimes multiple different colour hues may be used to
subdivide the quantitative values into further distinct categorical
groups. Decisions about how many colour-scale levels and what
intervals each relates to in value ranges will affect the patterns that
emerge. There is no single right answer – you will arrive at it largely
through trial and error/experimentation – but it is important to consider,
especially when you have a diverse distribution of values.
COMPOSITION: Logical sorting (and maybe even sub-grouping) of
the categorical values along each axis will aid readability and may help
surface key relationships.
VARIATIONS & ALTERNATIVES
A ‘radial heat map’ offers a structure variation whereby the table may
be portrayed using a circular layout. As with any radial display this is
only really of value if the cyclical ordering means something for the
subject matter. A variation would see colour shading replaced by a
measure of pattern density, using a scale of ‘packedness’ to indicate
increasing quantitative values. An alternative approach would be the
‘matrix chart’ using size of a shape to indicate the quantitative or a
range of point marker to display categorical characteristics.
Charts Connections
Matrix chart
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ALSO KNOWN AS Table chart
REPRESENTATION DESCRIPTION
A matrix chart displays quantitative values at the intersection between
two categorical dimensions. The chart comprises two categorical axes
with each possible value presented across the row and column headers
of a table layout. Each corresponding cell is then marked by a
geometric shape with its area sized to represent a quantitative value and
colour often used visually to distinguish a further categorical
dimension. While they are most commonly seen using circles, you can
use other proportionally sized shapes.
EXAMPLE Exploring the perceived difficulty of fixtures across the
season for teams in the premier league 2013–14.
Figure 6.34 Interactive Fixture Molecules
HOW TO READ IT & WHAT TO LOOK FOR
Learn what each categorical dimension relates to and make a note of the
range of values in each case, paying attention to the significance of any
ordering. Establish the quantitative size associations for the area marks
and look up any colour associations being used, both usually found via
a legend. Glance across the entire chart to locate the big, small and
medium areas and perform global comparisons to establish the high-
level ranking of biggest > smallest. Scan across each row and/or column
to see if there are specific patterns associated with either set of
categories. Identify any noticeable exceptions and/or outliers. Perform
local comparisons between neighbouring circular areas, to identify
larger than and smaller than relationships and estimate the relative
proportions. Estimate (or read, if labels are present) the absolute values
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of specific geometric areas of interest.
PRESENTATION TIPS
ANNOTATION: Direct value labelling is possible, otherwise be sure
to include a clear size legend. Normally this will be more than sufficient
as the reader may simply be looking to get a gist of the order of
magnitude.
COLOUR: If colours are being used to distinguish the different
categories, ensure these are as visibly different as possible.
COMPOSITION: If there are large outlier values there may be
occasions when the size of a few circles outgrows the cell it occupies.
You might editorially decide to allow this, as the striking shape may
create a certain impact, otherwise you will need to limit the largest
quantitative value to be represented by the maximum space available
within the table’s cell layout. Logical sorting (and maybe even sub-
grouping) of the categorical values along each axis will aid readability
and may help surface key relationships. The geometric accuracy of the
circle size calculations is paramount. Mistakes are often made with
circle size calculations: it is the area you are modifying, not the
diameter/radius.
VARIATIONS & ALTERNATIVES
A variation may be to remove the quantitative attribute of the area
marker, replacing it with a point marker to represent a categorical status
to indicate simply a yes/no observation through the presence/absence of
a point or through the quantity of points to represent a total. An
application of this might be in calendar form whereby a marker in a
date cell indicates an instance of something. It could also employ a
broader range of different categorical options; in practice any kind of
marker (symbol, colour, photograph) could be used to show a
characteristic of the relationship at each coordinate cell. An alternative
might be the ‘heat map’ which colour-codes the respective cells to
indicate a relationship based on a quantitative measure.
Charts Connections
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Node–link diagram
ALSO KNOWN AS Network diagram, graph, hairballs
REPRESENTATION DESCRIPTION
Node–link diagrams display relationships through the connections
between categorical ‘entities’. The entry-level version of this type of
diagram displays entities as nodes (represented by point marks and
usually including a label) with links or edges (represented by lines)
depicting the existence of connections. The connecting lines will often
display an attribute of direction to indicate the influencer relationship.
In some versions a quantitative weighting is applied to the show
relationship strength, maybe through increased line width. Replacing
point marks with a geometric shape and using attributes of size and
colour is a further variation. Often the complexity seen in these displays
is merely a reflection of the underlying complexity of the subject and/or
system upon which the data is based, so oversimplifying can
compromise the essence of such content.
EXAMPLE Exploring the connections of voting patterns for
Democrats and Republicans across all members of the US House of
Representatives from 1949 to 2012.
Figure 6.35 The Rise of Partisanship and Super-cooperators in the U.S.
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HOW TO READ IT & WHAT TO LOOK FOR
The first thing to consider is what entity each node (point or circular
area) represents and what the links mean in relationship terms. There
may be several other properties to acquaint yourself with, including
attributes like the size of the node areas, the categorical nature of
colouring, and the width and direction of the connections. Across the
graph you will mainly be seeking out the clusters that show the nodes
with the most relationships (representative of influencers or hubs) and
those without (including outliers). Small networks will generally enable
you to look closely at specific nodes and connections and easily see the
emerging relationships. When datasets are especially large, consisting
of thousands of nodes and greater numbers of mutual connections, the
displays can seem overwhelmingly cluttered and will be too dense to
make many detailed observations at node–link level. Instead, just relax
and know that your readability will be about a higher level sense-
making of the clusters/hubs and main outliers.
PRESENTATION TIPS
INTERACTIVITY: Node–link diagrams are particularly useful when
offered with interactive features, enabling the user to interrogate and
manipulate the display to facilitate visual exploration. The option to
apply filters to reduce the busy-ness of the visual and enable isolation of
individual node connections helps users to focus on specific parts of the
network of interest.
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ANNOTATION: The extent of annotated features tends to be through
the inclusion of value labels for each entity. Accommodating the
relative word sizes on each node can be difficult to achieve with real
elegance (once again that is where interactivity adds value, through the
select/mouseover event to reveal the label).
COLOUR: Aside from the possible categorical colouring of each node,
decisions need to be made about the colour of the connecting lines,
especially on deciding how prominent these links will be in contrast to
the nodes.
COMPOSITION: Composition decisions are where most of the
presentation customisation exists. There are several common
algorithmic treatments used to compute custom arrangements to
optimise network displays, such as force-directed layouts (using the
physics of repulsion and springs to amplify relationships) and
simplifying techniques (such as edge bundling to aggregate/summarise
multiple similar links).
VARIATIONS & ALTERNATIVES
There are many derivatives of the node–link diagram, as explained,
based on variations in the use of different attributes. ‘Hive plots’ and
‘BioFabric’ offer alternative approaches based on replacing nodes with
vertices.
Charts Connections
Chord diagram
ALSO KNOWN AS Radial network diagram, arc diagram (wrongly)
REPRESENTATION DESCRIPTION
A chord diagram displays relationships through the connections
between and within categories. They are formed around a radial display
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with different categories located around the edge: either as individual
nodes or proportionally sized segments (arcs) of the circumference
according to a part-to-whole breakdown. Emerging inwards from each
origin position are curved lines that join with other related categorical
locations around the edge. The connecting lines are normally
proportionally sized according to a quantitative measure and a
directional or influencing relationship is often indicated. The perceived
readability of the chord diagram will always be influenced by the
quantity and range of values being plotted. Small networks will enable a
reader to look closely at specific categories and their connections to see
the emerging relationships easily; larger systems will look busy through
the network of lines but they can still provide windows into complex
networks of influence. Often the complexity seen in these displays is
merely a reflection of the underlying complexity of the subject and/or
system upon which the data is based, so oversimplifying can
compromise the essence of such content.
EXAMPLE Exploring the connections of migration between and
within 10 world regions based on estimates across five-year intervals
between 1990 and 2010.
Figure 6.36 The Global Flow of People
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HOW TO READ IT & WHAT TO LOOK FOR
First determine how categories are displayed around the circumference,
either as nodes or part-to-whole arcs, and identify each one
individually. Consider the implication of the radial sorting of these
categorical values and, if based on part-to-whole sizes, establish a sense
of the largest > smallest arc lengths. Colour-coding may be applied to
the categories so note any associations. Look inside the display to
determine what relationships the connecting lines represent and check
for any directional significance. Look closer at the tangled collection of
lines criss-crossing this space, noting the big values (usually through
line weight or width) and the small ones. Avoid being distracted by the
distance a line travels, which is just a by-product of the outer
categorical arrangement: a long connecting line is just as significant a
relationship as a short one. For this reason, pay close attention to any
connecting lines that have very short looping distances to adjacent
categories. Are there any patterns of lines heading towards or leaving
certain categories?
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PRESENTATION TIPS
INTERACTIVITY: Chord diagrams are particularly useful when
offered with interactive features, enabling the user to interrogate and
manipulate the display to facilitate visual exploration. The option to
apply filters to reduce the busy-ness of the visual and enable isolation of
individual node connections helps users to focus on specific parts of the
network of interest.
ANNOTATION: Annotated features tend to be limited to value
labelling of the categories around the circumference and, occasionally,
directly onto the base or ends of the connecting lines (usually just those
that are large enough to accommodate them).
COLOUR: Aside from the categorical colouring of each node,
decisions need to be made about the colour of the connecting lines,
especially on deciding how prominent these links will be in contrast to
the nodes. Sometimes the connections will match the origin or
destination colours, or they will combine the two (with a start and end
colour to match the relationship).
COMPOSITION: The main arrangement decisions come through
sorting, firstly by generating as much logical meaning from the
categorical values around the edge of the circle and secondly by
deciding on the sorting of the connecting lines in the z-dimension – if
many lines are crossing, there is a need to think about which will be on
top and which will be below. Showing the direction of connections can
be difficult as there is so little room for manoeuvring many more visual
attributes, such as arrows or colour changes. One common, subtle
solution is to pull the destination join back a bit, leaving a small gap
between the connecting line and the destination arc. This then contrasts
with connecting lines that emerge directly from the categorical arcs,
showing it is their origin.
VARIATIONS & ALTERNATIVES
The main alternatives would be to consider variations of the ‘node–link
diagram’ or, specifically, the ‘arc diagram’, which offers a further
variation on the theme of networked displays, placing all the nodes
along a baseline and forming connections using semi-circular arcs,
rather than using a graph or radial layout.
Charts Connections
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Sankey diagram
ALSO KNOWN AS Alluvial diagram
REPRESENTATION DESCRIPTION
Sankey diagrams display categorical composition and quantitative
flows between different categorical dimensions or ‘stages’. The most
common contemporary form involves a two-sided display, with each
side representing different (but related) categorical dimensions or
different states of the same dimension (such as ‘before and after’). On
each side there is effectively a stacked bar chart displaying
proportionally sized and differently coloured (or spaced apart)
constituent parts of a whole. Curved bands link each side of the display
to represent connecting categories (origin and destination) with the
proportionally sized band (its thickness) indicating the quantitative
nature of this relationship. Some variations involve multiple stages and
might present attrition through the diminution size of subsequent stacks.
Traditionally the Sankey has been used as a flow diagram to visualise
energy or material usage across engineering processes. It is closely
related to the ‘alluvial diagram’, which tends to show changes in
composition and flow over time, but the Sankey label is often applied to
these displays also.
EXAMPLE Exploring the seat changes among political parties
between the 2010 and 2015 UK General Elections.
Figure 6.37 UK Election Results by Political Party, 2010 vs 2015
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HOW TO READ IT & WHAT TO LOOK FOR
Based on the basic two-sided version of the Sankey diagram, look down
both sides of the chart to learn what states are represented and what the
constituent categories are. Pay close attention to the categorical sorting
and pick out the large and small values on each side. Then look at the
connecting lines, making observations about the largest and narrowest
bands and noting any that seem to be mostly redistributed into a
different category compared to those that just join with the same. Notice
any small break-off bands that seem to cross the height of the whole
chart, perhaps representing a more dramatic change or diversion
between states. As with most network-type visualisations, the perceived
readability of the Sankey diagram will always be influenced by the
quantity and range of values being plotted, as well as the number of
different states presented.
PRESENTATION TIPS
INTERACTIVITY: Sankey diagrams are particularly useful when
offered with interactive features, enabling the user to interrogate and
manipulate the display to facilitate visual exploration. The option to
apply filters to reduce the busy-ness of the visual and enable isolation of
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individual node connections helps users to focus on specific parts of the
network of interest.
ANNOTATION: Annotated features tend to be limited to value
labelling of the categories that make up each ‘state’ stack.
COLOUR: Colouring is often used visually to indicate the categories
of the connecting bands, though it can get a little complicated when
trying to combine a sense of change through an origin category colour
blending with a destination category colour when there has been a
switch.
COMPOSITION: The main arrangement decisions come through
sorting, firstly by generating as much logical meaning from the
categorical values within the stacks and, secondly, by deciding on the
sorting of the connecting lines in the z-dimension – if many lines are
crossing, there is a need to think about which will be on top and which
will be below. There is no significant difference between a landscape or
portrait layout, which will depend on the subject matter ‘fit’ and the
space within which you have to work. Try to ensure that the sorting of
the categorical dimensions is as logical and meaningful as possible.
VARIATIONS & ALTERNATIVES
The concept of a Sankey diagram showing composition and flow can
also be mapped onto a geographical projection as one of the variations
of the ‘flow map’. You could use a ‘chord diagram’ as an alternative to
show how larger networks are composed proportionally and in their
connections. Showing how component parts have changed over time
could just be displayed using a ‘stacked area chart’. A ‘funnel chart’ is a
much simplified display to show how a single value changes (usually
diminishing) across states, for topics like sales conversion. This often is
based on a funnel-like shape formed by a wide bar at the top (those
entering the system) and then gradually narrower bars, stage by stage
towards the end state.
Charts Trends
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Line chart
ALSO KNOWN AS Fever chart, stock chart
REPRESENTATION DESCRIPTION
A line chart shows how quantitative values for different categories have
changed over time. They are typically structured around a temporal x-
axis with equal intervals from the earliest to latest point in time.
Quantitative values are plotted using joined-up lines that effectively
connect consecutive points positioned along a y-axis. The resulting
slopes formed between the two ends of each line provide an indication
of the local trends between points in time. As this sequence is extended
to plot all values across the time frame it forms an overall line
representative of the quantitative change over time story for a single
categorical value. Multiple categories can be displayed in the same
view, each represented by a unique line. Sometimes a point (circle/dot)
is also used to substantiate the visibility of individual values. The lines
used in a line chart will generally be straight. However, sometimes
curved line interpolation may be used as a method of estimating values
between known data points. This approach can be useful to help
emphasise a general trend. While this might slightly compromise the
visual accuracy of discrete values if you already have approximations,
this will have less impact.
EXAMPLE Showing changes in percentage income growth for the Top
1% and Bottom 90% of earners in the USA between 1917 and 2012.
Figure 6.38 The Fall and Rise of U.S. Inequality, in 2 Graphs
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HOW TO READ IT & WHAT TO LOOK FOR
Firstly, learn about the axes: what is the time period range presented on
the x-axis (and in what order) and what is the range of quantitative
values shown on the y-axis, paying particular attention to the origin
value (which may not be zero)? Inside the chart, determine what
categories each line represents: for single lines this will usually be clear
from the chart title, for multiple lines you might have direct labelling or
a legend to learn colour associations. Think about what high and low
values mean: is it ‘good’ to be large/small, increasing or decreasing?
Glance at the general patterns (especially if there are many) looking for
observations such as any trends (short or long term), any sudden
moments of a rise or fall (V- or W -shapes, or inverted), any sense of
seasonal or cyclical patterns, any points of interest where lines cross
each other or key thresholds that are reached/exceeded. Can you
mentally extrapolate from the values shown any sense of a forecasted
trend? Avoid jumping to spurious interpretations if you see two line
series following a similar pattern; this does not necessarily mean that
one thing has caused the other, it might just be coincidence. Then look
more closely at categories of interest and at patterns around specific
moments in time, and pick out the peak, low, earliest and latest values
for each line. Where available, compare the changing quantities against
annotated references such as targets, forecast, previous time periods,
range bands, etc.
PRESENTATION TIPS
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INTERACTIVITY: Interactivity may be especially helpful if you have
many categories and wish to enable the user to isolate (in focus terms) a
certain line category of interest.
ANNOTATION: Chart apparatus devices like tick marks and gridlines
in particular can be helpful to increase the accuracy of the reading of
the quantitative values. If you have axis labels you should not need
direct labels on each value point – this will be label overload. You
might choose to annotate specific values of interest (highest, lowest,
specific milestones). Think carefully about what is the most useful and
meaningful interval for your time axis labelling. When several
categories are being shown, if possible, try directly to label the
categories shown by each line, maybe at the start or end position.
COLOUR: When many categories are shown it may be that only
certain emphasised lines of interest possess a colour and a label – the
rest are left in greyscale for context.
COMPOSITION: Composition choices are mostly concerned with the
chart’s dimensions: its aspect ratio, how high and wide to make it. The
sequencing of values tends to be left to right for the sequence of the
time-based x-axis and low rising to high values on the y-axis; you will
need a good (and clearly annotated) reason to break this convention.
Line charts do not always need the y-axis to start at zero, as we are not
judging the size of a bar, rather the position along an axis. You should
expect to see a zero baseline if zero has some critical significance in the
interpretation of the trends. If your y-axis origin is not going to be zero,
you might include a small gap between the x-axis and the minimum so
that it is not implied. Be aware that the upward and downward trends on
a line chart can seem more significant if the chart width is narrow and
less significant if it is more stretched out. There is no single rule to
follow here but a useful notion involves ‘banking to 45°’ whereby the
average slope angle across your chart heads towards 45°. While it is
impractical to actually measure this, judging by eye tends to be more
than sufficient.
VARIATIONS & ALTERNATIVES
Variations of the line chart may include the ‘cumulative line chart’ or
‘step chart’. ‘Spark lines’ are mini line charts that aim to occupy almost
only a word’s length amount of space. Often seen in dashboards where
space is at a premium and there is a desire to optimise the density of the
display. ‘Bar charts’ can also be used to show how values look over
time when there is perhaps greater volatility in the quantitative values
across the time period and when the focus is on the absolute values at
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each point in time, more so than trends. Sometimes a line chart can
show quantitative trends over continuous space rather than time. For
showing ranking over time, consider the ‘bump chart’, and for before
and after comparisons, the ‘slope graph’.
Charts Trends
Bump chart
ALSO KNOWN AS
REPRESENTATION DESCRIPTION
A bump chart shows how quantitative rankings for categories have
changed over time. They are typically structured around a temporal x-
axis with equal intervals from the earliest to latest point in time.
Quantitative rankings are plotted using joined-up lines that effectively
connect consecutive points positioned along a y-axis (typically top =
first). The resulting slopes formed between the two ends of each line
provide an indication of the local ranking trends between points in time.
As this sequence is extended to plot all values across the time frame it
forms an overall line representative of the ranking story for a single
categorical value. Multiple categories are often displayed in the same
view, showing how rankings have collectively changed over time.
Sometimes a point (circle/dot) mark is also used to substantiate the
connected visibility of category lines, as is colour (for the lines and/or
the points).
EXAMPLE Showing changes in rank of the most populated US cities
at each census between 1790 and 1890.
Figure 6.39 Census Bump: Rank of the Most Populous Cities at Each
Census, 1790—1890
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HOW TO READ IT & WHAT TO LOOK FOR
Firstly, you need to learn about the axes. What is the time period range
presented on the x-axis (and in what order)? What are the range of
quantitative rankings shown on the y-axis (check that the ranks start at 1
from the top downwards)? Inside the chart, determine what categories
each line represents: this might be explained through direct labelling, a
colour legend, interactivity or through differentiating point marker
attributes of colour/shape/pattern. Think about what high and low ranks
mean: is it ‘good’ to be high up the rankings and is it better to be
moving up or down? Consider the general patterns to look for
observations such as consistent trends (largely parallel lines) or
completely non-relational patterns (lines moving in all directions). Are
there any prominent stories of categories that have had a sudden rise or
fall (V- or W-shapes, or inverted)? Is there any evidence of seasonal or
cyclical patterns, any key points of interest where lines cross each other
or key thresholds that are reached/exceeded? Next, look more closely at
categories of interest and at patterns around specific moments in time,
and pick out the peak, low, earliest and latest values for each line.
PRESENTATION TIPS
INTERACTIVITY: Interactivity is usually necessary with bump
charts, especially if you have many categories and wish to enable the
user to isolate (in focus terms) a certain line category of interest.
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ANNOTATION: The ranking labels can be derived from the vertical
position along the scale so direct labelling is usually unnecessary. You
might choose to annotate specific values of interest (highest, lowest,
specific milestones). Think carefully about what is the most useful and
meaningful interval for your time axis labelling.
COLOUR: Often, with many categories to show in the same chart, the
big challenge is to distinguish each line, especially as they likely criss-
cross often with others. Using colour association can be useful for less
than 10 categories, but for more than that you really need to offer the
interactivity or maybe decide that only certain emphasised lines of
interest will possess a colour and the rest are left in greyscale for
context.
COMPOSITION: The sequencing of values tends to be left to right for
the sequence of the time-based x-axis with high rankings (low number)
on the y-axis moving downwards. You will therefore need a good (and
clearly annotated) reason to break this convention.
VARIATIONS & ALTERNATIVES
Alluvial diagrams (similar to Sankey diagrams) can show how rankings
have changed over time while also incorporating a component of
quantitative magnitude. This approach is effectively merging the ‘bump
chart’ with the ‘stacked area chart’. Consider ‘line charts’ and ‘area
charts’ if the ranking is of secondary interest to the absolute values.
Charts Trends
Slope graph chart
ALSO KNOWN AS Slope chart
REPRESENTATION DESCRIPTION
A slope graph shows a ‘before and after’ display of changes in
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quantities for different categories. The display is based on (typically)
two parallel quantitative axes with a consistent scale range to cover all
possible quantitative values. A line is plotted for each category
connecting the two axes together with the vertical position on each axis
representing the respective quantitative values. Sometime a dot is also
used to further substantiate the visibility of the value positions. These
connecting lines form slopes that indicate the upward, downward or
stable trend between points in time. The resulting display incorporates
absolute values, reveals rank and, of course, shows change between
time. Colours are often used visually to distinguish different categorical
lines, otherwise this can be used to surface visibly the major trend states
(up, down, no change). A slope graph works less well when all values
(or the majority) are going in the same direction; consider alternatives if
this is the case.
EXAMPLE Showing changes in the share of power sources across all
US states between 2004 and 2014.
Figure 6.40 Coal, Gas, Nuclear, Hydro? How Your State Generates
Power
HOW TO READ IT & WHAT TO LOOK FOR
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Firstly, learn about the axes: what are the two points in time being
presented and what is the possible range of quantitative values shown
on the y-axis, checking that the ranks start from the top down? Inside
the chart, learn what each category line relates to and determine what
categories each line represents: this might be explained through direct
labelling, a colour legend, or through interactivity. Think about what
upward, downward and stable trends mean: is it ‘good’ to be moving up
or down? Is it more interesting to show no change? Look at the general
patterns to observe such things as consistent trends (largely parallel
lines in either direction) or completely non-relational patterns (lines
moving in all directions). Colour may be used to accentuate the
distinction between upward and downward trends. Are there any
prominent stories of categories that have had a dramatic rise or fall?
Even if no values have dramatically altered, that in itself can be an
important finding, especially if change was expected. Next, look more
closely at categories of interest and pick out the highest and lowest
values on each side to learn about those stories. Look for the gaps
where there are no values, and at outlier values too, to see if some sit
outside the normal value clusters.
PRESENTATION TIPS
INTERACTIVITY: Depending on the number of category values
being presented, slope graphs can become quite busy, especially if there
are bunches of similar values and slope transitions. This also causes a
problem with accommodating multiple labels on the same value. On
these occasions you might find interactive slope graphs to help
filter/exclude certain values.
ANNOTATION: Labelling of each category will get busy, especially
when there are shared values, so you might choose to annotate specific
values of interest (highest, lowest, of editorial interest).
COLOUR: Often when you have many categories to show in the same
chart the big challenge is to distinguish each line, especially as they
likely criss-cross often with others. Using colour association can be
useful for less than 10 categories usually with direct labelling on the left
and/or right of the chart.
COMPOSITION: The aspect ratio of the slope graph (height and
width) will often be determined by the space you have to work with.
VARIATIONS & ALTERNATIVES
Rather than showing a before and after story, some slope graphs are
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used to show the relationship between different quantitative measures
for linked categories. In this case the connecting line is not indicative of
a directional relationship, just the relationship itself. An alternative
option would be the ‘connected dot plot’ which can also show before
and after stories and is a better option when all values are moving in the
same direction.
Charts Trends
Connected scatter plot
ALSO KNOWN AS Trail chart
REPRESENTATION DESCRIPTION
A connected scatter plot displays the relationship between two
quantitative measures over time. The display is formed by plotting
marks like a dot or circle for each point in time at the respective
coordinates along two quantitative x- and y-axes. The collection of
individual points is then connected (think of a dot-to-dot drawing
puzzle) using lines joining each consecutive point in time to form a
sequence of change. Generally there would only be a single connected
line plotted on a chart to avoid the great visual complexity of overlaying
several in one display. However, if multiple categories are to be
included, colour is typically used to distinguish each series.
EXAMPLE Showing changes in the daily price and availability of
Super Bowl tickets on the secondary market four weeks prior to the
event across five Super Bowl finals.
Figure 6.41 Holdouts Find Cheapest Super Bowl Tickets Late in the
Game
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HOW TO READ IT & WHAT TO LOOK FOR
Learn what each quantitative axis relates to and make a note of the
range of values in each case (min to max). Look at what each plotted
value on the chart refers to in terms of its date label and determine the
meaning of line direction. It usually helps to parse your thinking by
considering what higher/lower values mean for each quantitative axis
individually and then combining the joint meaning thereafter. Try to
follow the chart from the start to the end, mapping out in your mind the
sequence of a narrative as the values change in all directions and noting
the extreme values in the outer edges of your line’s reach. Look at the
overall pattern of the connected line: is it consistently moving in one
direction? Does it ebb and flow in all directions? Does it create a spiral
shape? Compare consecutive points for a more focused view of change
between two points.
PRESENTATION TIPS
INTERACTIVITY: The biggest challenge is making the connections
and the sequence as visible as possible. This becomes much harder
when values change very little and/or they loop back almost in spiral
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fashion, crossing back over themselves. It is especially hard to label the
sequential time values elegantly. One option to overcome this is
through interactivity and particularly through animated sequences
which build up the display, connecting one line at a time and unveiling
the date labels as time progresses. It is often the case that only one
series will be plotted. However, interactive options may allow the user
to overlay one or more for comparison, switching them on and off as
required.
ANNOTATION: Connected scatter plots are generally seen as one of
the most complex chart types for the unfamiliar reader to work out how
to read, given the amount of different attributes working together in the
display. It is therefore vital that as much help is given to the reader as
possible with ‘how to read’ guides and illustrations of what the different
directions of change mean.
COLOUR: Colour is only generally used to accentuate certain sections
of a sequence that might represent a particularly noteworthy stage of
narrative.
COMPOSITION: As the encoding of the plotted point values is based
on position along an axis, it is not necessary to start the axes from a
zero baseline – just make the scale ranges as representative as possible
of the range of values being plotted. Ideally a connected scatter plot will
have a 1:1 aspect ratio (equally as tall as it is wide), creating a squared
area to help patterns surface more evidently. If one quantitative variable
(e.g. weight) is likely to be affected by the other variable (e.g. height), it
is general practice to place the former on the y-axis and the latter on the
x-axis.
VARIATIONS & ALTERNATIVES
The ‘comet chart’ is to the connected scatter plot what the ‘slope graph’
is to the ‘line chart’ – a summarised view of the changing relationships
across two quantitative values between just two points in time.
Naturally a reduced variation of the connected scatter plot is simply the
‘scatter plot’ where there is no time dimension or elements of
connectedness.
Charts Trends
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Area chart
ALSO KNOWN AS
REPRESENTATION DESCRIPTION
A line chart shows how quantitative values for different categories have
changed over time. They are typically structured around a temporal x-
axis with equal intervals from the earliest to latest point in time.
Quantitative values are plotted using joined-up lines that effectively
connect consecutive points positioned along a y-axis. The resulting
slopes formed between the two ends of each line provide an indication
of the local trends between points in time. As this sequence is extended
to plot all values across the time frame it forms an overall line
representative of the quantitative change over time story for a single
categorical value. To accentuate the magnitude of the quantitative
values and the change through time the area beneath the line is filled
with colour. The height of each coloured layer at each point in time
reveals its quantity. Area charts can display values for several
categories, using stacks, to show also the changing part-to-whole
relationship.
EXAMPLE Showing changes in the average monthly price ($ per
barrel) of crude oil between 1985 and 2015.
Figure 6.42 Crude Oil Prices (West Texas Intermediate), 1985—2015
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HOW TO READ IT & WHAT TO LOOK FOR
Firstly, learn about the axes: what is the time period range presented on
the x-axis (and in what order) and what is the range of quantitative
values shown on the y-axis, paying particular attention to whether it is a
percentage or absolute based scale? Inside the chart, determine what
categories each area layer represents: for single areas this will usually
be clear from the chart title, for multiple areas you might have direct
labelling or a nearby legend to learn colour associations. Think about
what high and low values mean: is it ‘good’ to be large/small,
increasing or decreasing? Glance at the general patterns (especially if
there are many layers), looking at the visible ‘thickness’ of the coloured
layers. At what points are the values highest or lowest? When are they
growing or shrinking as the time axis moves along? If there are multiple
categories, which ones take up the largest and smallest slices of the
overall total? Are there any trends (short or long term), any sudden
moments of a rise or fall, any sense of seasonal or cyclical patterns? If
there are multiple categories, look more closely at individual layers of
interest.
PRESENTATION TIPS
ANNOTATION: Direct labelling of quantitative values will get far too
busy so you might choose to annotate specific values of interest
(highest, lowest, specific milestones). Think about the most useful
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interval for your axis labelling. As ever there is no single rule, so adopt
the Goldilocks principle of not too many, not too few. If you have a
stacked area chart, try directly to label the category layers shown as
closely as possible (if the heights allow it) or at least ensure any colour
associations are easily identifiable through a nearby legend. Think
carefully about what is the most useful and meaningful interval for your
time axis labelling.
COLOUR: If you are using a stacked area chart, ensure the categorical
layers have sufficiently different colours so that their distinct reading
can be efficiently performed.
COMPOSITION: Similar to the line chart, the area chart’s dimensions
should ideally utilise an aspect ratio that optimises the readability
through 45° banking (roughly judging the average slope angle). The
sequencing of values tends to be left to right for the sequence of the
time-based x-axis and low rising to high values on the y-axis; you will
need a good (and clearly annotated) reason to break this convention.
Unlike the line chart, the quantitative axis for area charts must start at
zero as it is the height of the coloured areas under each line that helps
readers to perceive the quantitative values. Do not have overlapping
categories on the same chart because it makes it very difficult to see
(imagine hills behind hills, peaking out and then hiding behind each
other). Rather than stacking categories you might consider using small
multiples, especially as this will present the respective displays from a
common baseline (and make reading sizes a little easier).
VARIATIONS & ALTERNATIVES
Like area charts, ‘alluvial diagrams’ display proportional stacked layers
for multiple categories showing the absolute value change over time.
However, they also show the evolving ranks, switching the relative
ordering of each layer of values based on the current magnitude. Some
deployments of the area chart are not plotted over time but over
continuous dimensions of space, perhaps showing the changing nature
of a given quantitative measure along a given route. When you have
many concurrent layers to show and these layers start and stop at
different times, a ‘slope graph’ is worth considering.
Charts Trends
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Horizon chart
ALSO KNOWN AS
EXAMPLE Showing percentage changes in price for selected food
items in the USA between 1990 and 2015.
REPRESENTATION DESCRIPTION
Horizon charts show how quantitative values for different categories
have changed over time. They are valuable for showing changes over
time for multiple categories within space-constrained formats (such as
dashboards). They are structured around a series of rows each showing
changes in quantitative values for a single category. The temporal x-
axis has equal intervals from the earliest to latest point in time.
Quantitative values are plotted using joined-up lines that connect
consecutive points positioned along a value y-axis. The resulting slopes
formed between the ends of each line provide an indication of the local
trends between two points in time. As this sequence is extended to plot
all values across the time frame it forms an overall line representative of
the quantitative changes. To accentuate the magnitude of the
quantitative values the area beneath the line is filled with colour.
Negative values are highlighted in one colour, positive values in
another colour. Variations in colour lightness are used to indicate
different degrees or bands of magnitudes, with the extremes getting
darker. Negative value areas are then flipped from underneath the
baseline to above it, joining the positive values but differentiated in
their polarity by colour. Finally, like slicing off layers of a mountain,
each distinct threshold band that sits above the imposed maximum y-
axis scale is chopped off and dropped down to the baseline, in front of
its foundation base. The final effect shows overlapping layers of
increasingly darker colour-shaded areas all occupying the same vertical
space with combinations of height, colour and shade representing the
values.
Figure 6.43 Percentage Change in Price for Select Food Items, Since
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1990
HOW TO READ IT & WHAT TO LOOK FOR
Firstly, learn about the category rows: what do they represent and in
what order are they presented? Next, the chart axes: what is the time
period range presented on the x-axis (and in what order) and what is the
range of quantitative values shown on the y-axis, paying attention to
whether it is a percentage or absolute value scale? Next, what are the
colour associations (for positive and negative values) and the different
shaded banding thresholds? Think about what high and low values
mean: is it ‘good’ to be large/small, increasing or decreasing? Glance at
the general patterns over time, looking at the most visible dark areas of
each colour polarity: where have values reached a peak in either
direction? Maybe then separate your reading between looking at the
positive value insights and then the negative ones: which chunks of
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colour are increasing in value (darker) or shrinking (getting lighter) as
the time axis moves along? Where can you see most empty space,
indicating low values? Are there any trends (short or long term), any
sudden moments of a rise or fall, any sense of seasonal or cyclical
patterns, any points of interest where lines cross each other or key
thresholds that are reached/exceeded? Then look more closely at
categories of interest, assessing their own patterns around specific
moments in time and picking out the peak, low, earliest and latest
values for each row.
PRESENTATION TIPS
ANNOTATION: The decisions around annotations are largely reduced
to labelling the category rows. Such is the busy-ness of the chart areas
that any direct labelling is going to clutter the display too much: horizon
charts are less about precise value reading and more about getting a
sense of the main patterns, so avoid the temptation to over-label. Think
carefully about what is the most useful and meaningful interval for your
time axis labelling.
COLOUR: Colour decisions mainly concern the choices of quantitative
scale bandings to show the positive and negative value ranges.
COMPOSITION: The height of the chart area in which you can
accommodate a single row of data will have an influence on the entire
construction of the horizon chart. It will often involve an iterative/trial
and error process, looking at the range of quantitative values across
each category, establishing the most sensible and meaningful thresholds
within these range and then fixing the y-axis scales accordingly. Try to
ensure the sorting of the main categorical rows is as logical and
meaningful as possible.
VARIATIONS & ALTERNATIVES
An alternative to the horizon chart is the entry-level single category
‘area chart’, which does not suffer the same constraints of restrictions to
the vertical scale. For space-constrained displays, ‘spark lines’ would
offer an option suitable to such situations and easily accommodate
multiple category displays.
Charts Trends
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Stream graph
ALSO KNOWN AS Theme river
REPRESENTATION DESCRIPTION
A stream graph shows how quantitative values for different categories
have changed over time. They are generally used when you have many
constituent categories at any given point in time and these categories
may start and stop at different points in time (rather than continue
throughout the presented time frame). As befitting the name, their
appearance is characterised by a flowing, organic display of meandering
layers. They are typically structured around a temporal x-axis with
equal intervals from the earliest to latest point in time. Quantitative
values are plotted using joined-up lines that effectively connect
consecutive points to quantify the height above a local baseline, which
is not a stable zero baseline but rather a shifting shape formed out of
other category layers. To accentuate the size of the category’s height at
any given point the area beneath the line is filled with colour. The
height of each coloured layer at each point in time reveals its quantity.
This colour is often used to further represent a quantitative value scale
or to associate with categorical colours. The stacking arrangement of
the different categorical streams goes above and below the central axis
line to optimise the layout but not with any implication of polarity.
EXAMPLE Showing changes in the total domestic gross takings ($US)
and the longevity of all movies released between 1986 and 2008.
Figure 6.44 The Ebb and Flow of Movies: Box Office Receipts 1986—
2008
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HOW TO READ IT & WHAT TO LOOK FOR
Firstly, determine what is the time period presented on the x-axis (and
in what order). In most stream graphs you do not see the quantitative y-
axis scale because the level of reading is more about getting a gist for
the main patterns in a relative sense rather than an absolute one. You
might find that the colouring of layers has a quantitative scale or
categorical association so look for any keys. Also, you will often find
guides to help estimate the quantitative heights of each layer. Think
about what high and low values mean: is it ‘good’ to be large/small,
increasing or decreasing? Glance at the general patterns over time.
Remember that above or below means nothing in the sense of polarity
of values, so your focus is on the entirety of the collective shape. Look
for the largest peaks and the shallowest troughs, possible seasonal
patterns or the significant moments of change. Note where these
patterns occur in relation to the timescale. Can you see any prominently
tall (big values) or wide (long-duration) layers? Notice when layers start
and end, noting times when there are many concurrent categories and
when there are few. Pick out the layers of personal interest and assess
their patterns over time. Do not spend too much effort trying to estimate
precise values of height, but keep your focus on the bigger picture level.
It is often useful to rotate the display so the streams are travelling
vertically, offering a different perspective and removing the instinct to
see positive values above and negative values below the central axis.
PRESENTATION TIPS
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INTERACTIVITY: If interactivity is a possibility, this could enable
selection or mouseover events to reveal annotated values at any given
point in time or to filter the view.
ANNOTATION: Chart apparatus devices are generally of limited use
in a stream graph with the priority on a general sense of pattern more
than precision value reading. Direct labelling of categories is likely to
be quite busy but may be required, at least to annotate the most
interesting patterns (highest, lowest, specific milestones). Think
carefully about what is the most useful and meaningful interval for your
time axis labelling.
COLOUR: Ensure any colour associations or size guides are easily
identifiable through a nearby legend.
COMPOSITION: Composition choices are firstly concerned with the
landscape or portrait layout. This will largely be informed by the format
and space of your outputs and the meaning of the data. The stream
layers are often smoothed, giving them an aesthetically organic
appearance, both individually and collectively. This is achieved via
curved line interpolation.
VARIATIONS & ALTERNATIVES
The fewer categorical series you have in your data, the more likely a
stacked ‘area chart’ is going to best-fit your needs. You could consider
a stacked ‘bar chart’ over time also, but there is less chance of
maintaining the connected visibility of continuous categorical series via
a singular shape.
Charts Activities
Connected timeline
ALSO KNOWN AS Relationship timeline, storyline visualisations,
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swim-lane chart
REPRESENTATION DESCRIPTION
A connected timeline displays the duration, milestones and categorical
relationships across a range of categorical ‘activities’. It represents a
particularly diverse and creative way of showing changes over time and
so involves many variations in approach. The structure is generally
formed of time-based quantitative x-axis and categorical y-axis lanes.
Each categorical activity will commence at a point in time and from
within a vertical category ‘family’. Over time, the line will progress,
possibly switching to a different categorical lane position as the nature
of the activity alters. The lines may be of fixed width or proportionally
weighted to represent a quantitative measure. Some activity lines may
cease, restart or merge with others to build a multi-faceted narrative.
Colour can also be used to present further relevant detail. The main
issue with any connected timeline approach is simply the complexity of
the content and the number of moving parts crossing over the display.
As there are many entry points into reading such a timeline there can be
inefficiency in the reading process, but this is usually proportional
simply to the subject at hand and you may not wish to see these nuances
being removed.
EXAMPLE Showing changes in US major college football programme
allegiance to different conferences between 1965 and 2015.
Figure 6.45 Tracing the History of N.C.A.A. Conferences
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know what the major categorical ‘lanes’
represent and what the range of date values is (min to max). Then try to
determine what each categorical activity line represents. As there are so
many derivatives there is no single reading strategy, but generally
glance across the entire chart noting the sequence of the activities; there
is usually a sequential logic attached to their sorting based on the start
date milestone in particular. Follow the narrative from left to right,
noting observations about any big, small and medium weighted lines
and spotting any moment when they connect with, overlap or detach
from other activities. Are there any major convergences or divergences
in pattern? Any hubs of dense activity and other sparse moments? Look
for the length of lines to determine the long, medium and short
durations of activity. Where available, compare the activities against
annotated references about other key milestone dates that might hold
some significance or influence.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines
in particular can be helpful to increase the accuracy of the reading of
both the quantitative values and the activity ‘lanes’, which may be
coloured to help recognise divisions between categories. Direct
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labelling is usually seen in these timelines to help maintain associations
across the display with the categories of characters or activities, perhaps
annotating the consequence or cause of lines merging, etc. Think
carefully about what is the most useful and meaningful interval for your
time axis labelling.
COLOUR: Even if colour does not have a direct association with given
activities, it can be a useful property to highlight certain features of the
narrative, sometimes acting as a container device to group activities
together, even if just for a momentary time period.
COMPOSITION: Where possible, try to make the categorical sorting
meaningful, maybe organising values in ascending/descending size
order. The vertical (y) or horizontal (x) sequencing of time will depend
on the amount of data to show and the space you have to work with.
Also, depending on the narrative, the past > present ordering may be
reversed.
VARIATIONS & ALTERNATIVES
There are similarities with the organic nature of the ‘alluvial diagram’,
which shows ranking and quantitative change over time for a number of
concurrent categories. When there are fewer inter-activity relationships
and more discrete categories are involved, then the ‘Gantt chart’ offers
an alternative way of showing this analysis.
Charts Activities
Gantt chart
ALSO KNOWN AS Range chart, floating bar chart
REPRESENTATION DESCRIPTION
A Gantt chart displays the start and finish points and durations for
different categorical ‘activities’. The display is commonly used in
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project management to illustrate the breakdown of a schedule of tasks
but can be a useful device to show any data based on milestone dates
and durations. The chart is structured around a time-based quantitative
x-axis and a categorical y-axis. Each categorical activity is represented
by lines positioned according to the start moment and then stretched out
to the finish point. There may be several start/finish durations within the
same activity row. Sometimes points are used to accentuate the
start/finish positions and the line may be coloured to indicate a relevant
categorical value (e.g. separating completed vs ongoing).
EXAMPLE Showing the events of birth, death and period serving in
office for the first 44 US Presidents.
Figure 6.46 A Presidential Gantt Chart
HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with what major categorical values each
Gantt bar is associated and what the range of the date values is (min to
max). Follow the narrative, noting the sequence of the categories –
there is usually a sequential sorting based on the start date milestone.
Glance across the entire chart and perform global comparisons to
establish the high-level ranking of biggest > smallest durations (based
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on the length of the line) as well as early and late milestones. Identify
any noticeable exceptions and/or outliers. Perform local comparisons
between neighbouring bars to identify proportional differences and any
connected dependencies. Estimate (or read, if labels are present) the
absolute values for specific categories of interest. Where available,
compare the activities against annotated references about other key
milestone dates that might hold some significance or influence.
PRESENTATION TIPS
ANNOTATION: Chart apparatus devices like tick marks and gridlines
(or row band-shading) in particular can be helpful to increase the
accuracy of the reading of the start point and duration of activities along
the timeline. If you have axis labels you may not need direct labels for
the values shown with each duration bar – this will be label overload, so
generally decide between one or the other. Think carefully about what
is the most useful and meaningful interval for your time axis labelling.
COMPOSITION: There is no significant difference in perception
between vertical or horizontal Gantt charts, though horizontal layouts
are more metaphorically consistent with the concept of reading time.
Additionally, these layouts tend to make it easier to accommodate and
read the category labels. Where possible, try to sequence the categorical
‘activities’ in a way that makes for the most logical reading, either
organised by the start/finish dates or maybe the durations (depending on
which has most relevance).
VARIATIONS & ALTERNATIVES
Variations might involve the further addition of different point markers
(represented by combinations of symbols and/or colours) along each
activity row to indicate additional milestone details, using the ‘instance
chart’. An emerging trend in technique terms involves preserving the
position of activity lines adjacent to other concurrent activities, rather
than fixing them to stay within discrete rows. Sometimes there is much
more fluidity and less ‘discreteness’ in the relationships between
activity, so approaches like the ‘connected timeline’ may be more
fitting.
Charts Activities
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Instance chart
ALSO KNOWN AS Milestone map, barcode chart, strip plot
REPRESENTATION DESCRIPTION
An instance chart displays individual moments or instances of
categorical ‘activities’. There are many variations in approach for this
kind of display but generally you will find a structure based on a time-
based quantitative x-axis and a categorical y-axis. For each categorical
activity, instances of note are represented by different point markers
that indicate along the timeline when something has happened. The
point markers may have different combinations of symbols and colours
to represent different types of occurrences, but avoid having too many
different combinations so that viewers do not have to learn an entirely
new alphabet of meaning.
EXAMPLE Showing the instances of different Avengers characters
appearing in Marvel’s comic book titles between 1963 and 2015.
Figure 6.47 How the ‘Avengers’ Line-up Has Changed Over the Years
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HOW TO READ IT & WHAT TO LOOK FOR
Look at the axes so you know with what major categorical values each
row of instances is associated and what the range of the date values is
(min to max). Look up any legend that will explain what (if any)
associations exist between the instance markers and their
colour/symbol. Glance down the y-axis noting the sequence of the
categories; there is usually a sequential logic attached to their sorting
based on the start date milestone in particular. Follow the narrative,
noting observations about the type and frequency of instances being
plotted. Look across the entire chart to locate the headline patterns of
clustering and identify any noticeable exceptions and/or outliers. Look
across the patterns within each row individually to learn about each
category’s dispersal of instances. Look for empty regions where no
marks appear. How do all these patterns relate to the time frame
displayed? Where available, compare the activities against annotated
references about other key milestone dates that might hold some
significance or influence.
PRESENTATION TIPS
ANNOTATION: The main annotation properties will be used to serve
the role of explaining the associations between marks and attributes
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through clear legends/keys.
COMPOSITION: Where possible, try to sequence the categorical
‘activities’ in a way that makes for the most logical reading, either
organised by the start/finish dates or maybe the durations (depending on
which has most relevance).
VARIATIONS & ALTERNATIVES
Some variations may see the size of a geometric shape used instead of
just a point to indicate also a quantitative measure to go with the
instance. The marking of an instance through a ‘when’ moment could
also be based on data that talks about positional moments within a
sequence. If the basic activity is reduced to a start/finish moment then
the ‘Gantt chart’ will be the best-fit option.
Charts Overlays
Choropleth map
ALSO KNOWN AS Heat map
REPRESENTATION DESCRIPTION
A choropleth map displays quantitative values for distinct, definable
spatial regions on a map. Each geographic region is represented by a
polygonal area based on its outline shape, with each distinct shape then
collectively arranged to form the entire landscape. (Note that most tools
for mapping have a predetermined reference between a region name and
the dimensions of the regional polygon.) Each area is colour-coded to
represent a quantitative value based on a scale with colour variation
intervals that (typically) go from a light tint for smaller values to a dark
shade for larger values. Choropleth maps should only be used when the
quantitative measure is directly associated with and continuously
relevant across the spatial region on which it will be displayed.
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Similarly, if your quantitative measure is about or related to the
consequence of more people living in an area, interpretations may be
distorting, so consider transforming your data to per capita or per acre
(or other spatial denominator) to standardise the analysis accordingly.
EXAMPLE Mapping the percentage change in the populations of
Berlin’s districts across new and native Berliners since the fall of the
Berlin Wall.
Figure 6.48 Native and New Berliners — How the S-Bahn Ring
Divides the City
HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Establish the colour-scale value associations, usually found
via a legend. Glance across the entire chart to locate the dark, light and
medium shades (generally darker = larger) and perform global
comparisons to establish the high-level ranking of biggest values >
smallest. Identify any noticeable exceptions and/or outliers. Beware
making judgements about the significance of prominent large
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geographical areas: size is an attribute of the underlying region, not the
significance of the measure displayed. Gradually zoom in your focus to
perform increasingly local comparisons between neighbouring regional
areas to identify any noticeable consistencies or inconsistencies
between their values. Estimate (or read, if labels are present) the
absolute values of specific regions of interest.
PRESENTATION TIPS
ANNOTATION: Directly labelling the regional areas with
geographical details and the value they hold is likely to lead to too
much clutter. You might include only a limited number of regional
labels to provide spatial context and orientation.
COLOUR: Legends explaining the colour scales should ideally be
placed as close to the map display as possible. The border colour and
stroke width for each spatial area should be distinguishable to define the
shape but not so prominent as to dominate attention – usually a subtle
grey- or white-coloured thin stroke will be fine. As well as variation in
colour scales, sometimes pattern or textures may add an extra layer of
detail to the value status of each region. When including a projected
mapping layer image in the background, ensure it is not overly
competing for visual prominence by making it light in colour and
possibly semi-transparent. Do not include any unnecessary geographical
details that add no value to the spatial orientation or interpretation and
clutter the display (e.g. roads, building structures).
COMPOSITION: With Earth being a sphere, there are many different
mapping projections for representing the regions of the world on a
plane surface. Be aware that the transformation adjustments made by
some map projections can distort the size of regions of the world,
inflating their size relative to other regions.
VARIATIONS & ALTERNATIVES
Some choropleth maps may be used to indicate categorical association
rather than quantitative measurements. Alternative thematic mapping
approaches to representing quantitative values might include the
‘proportional symbol map’ and the ‘dot density map’. This is a variation
that involves plotting a representative quantity of dots equally (but
randomly) across and within a defined spatial region. The position of
individual dots is therefore not to be read as indicative of precise
locations but used to form a measure of quantitative density. This offers
a useful alternative to the choropleth map, especially when categorical
separation of the dots through colour is of value. ‘Dasymetric mapping’
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is similar in approach to choropleth mapping but breaks the constituent
regional areas into much more specific, almost custom-drawn, sub-
regions to better represent the realities of the distribution of human and
physical phenomena within a given spatial boundary.
Charts Overlays
Isarithmic map
ALSO KNOWN AS Contour map, isopleth map, isochrone map
REPRESENTATION DESCRIPTION
An isarithmic map displays distinct spatial surfaces on a map that share
the same quantitative classification. All spatial regions (transcending
geo-political boundaries) that share a certain quantitative value or
interval are formed by interpolated ‘isolines’ connecting points of
similar measurement to form distinct surface areas. Each area is then
colour-coded to represent the relevant quantitative value. The scale of
colour variation intervals differs between deployments but will typically
range from a light tint for smaller values to a dark shade for larger
values. An isarithmic map would be used in preference to a choropleth
map when the patterns of data being displayed transcend the distinct
regional polygons. They could be used to show temperature bandings or
smoothed regions of political attitudes.
EXAMPLE Mapping the degree of dialect similarity across the USA.
Figure 6.49 How Y’all, Youse and You Guys Talk
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HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Establish the colour scale value associations, usually found
via a legend. Glance across the entire chart to locate the dark, light and
medium shades (generally darker = larger) and perform global
comparisons to establish the high-level ranking of biggest values >
smallest. Identify any noticeable exceptions and/or outliers, including
regions that appear in isolation from their otherwise related values and
notable for their position adjacent to very different shaded regions. Note
that any interpolation used to smooth the joins between data points to
form organic surfaces will inevitably reduce the precision of the
surfaces in their relationship to land position. Gradually zoom in your
focus to perform increasingly local comparisons between neighbouring
regional areas to identify any noticeable consistencies or inconsistencies
between their values. Estimate the absolute values of specific regions of
interest.
PRESENTATION TIPS
ANNOTATION: Directly labelling the surface areas to show the
quantitative value or range they represent will be too cluttered. You
might include only a limited number of regional labels to provide
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spatial context and orientation.
COLOUR: Legends explaining the colour scales should ideally be
placed as close to the map display as possible. If using visible contour
or boundary lines there is a clear implication of a location being inside
or outside the line, so make these lines as prominent in colour as
possible according to the precision of their representation. If the
smoothing of the surface locations has been applied the representation
of these areas should similarly avoid looking definitive. You therefore
might consider subtle colour gradation/overlapping between different
regions to capture appropriately the underlying ‘fuzziness’ of the data.
As well as colour scales, sometimes pattern or textures may add an
extra layer of detail to the value status of each surface region. When
including a projected mapping layer image in the background, ensure it
is not overly competing for visual prominence by making it light in
colour and possibly semi-transparent. Do not include any unnecessary
geographical details that add no value to the spatial orientation or
interpretation and clutter the display (e.g. roads, building structures).
COMPOSITION: Be aware that the transformation adjustments made
by some map projections can distort the size of regions of the world,
inflating their size relative to other regions.
VARIATIONS & ALTERNATIVES
There are specific applications of isarithmic maps used for showing
elevation (‘contour maps’), atmospheric pressure (‘isopleth maps’) or
travel–time distances (‘isochrone maps’). Sometimes you might use
isarithmic maps to show a categorical status (perhaps even a binary
state) rather than a quantitative scale.
Charts Overlays
Proportional symbol map
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ALSO KNOWN AS Graduated symbol map
REPRESENTATION DESCRIPTION
A proportional symbol map displays quantitative values for locations on
a map. The values are represented via proportionally sized areas
(usually circles), which are positioned with the centre mid-point over a
given location coordinate. Colour is sometimes used to introduce
further categorical distinction.
EXAMPLE Mapping the origin and size of funds raised across the 22
major candidates running for US President during the first half of 2015.
Figure 6.50 Here’s Exactly Where the Candidates’ Cash Came From
HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Establish the area size value associations, usually found via
a legend. Glance across the entire chart to locate the large, medium and
small shapes and perform global comparisons to establish the high-level
ranking of biggest values > smallest. Identify any noticeable exceptions
and/or outliers. Gradually zoom in your focus to perform increasingly
local comparisons between neighbouring regional areas to identify any
noticeable consistencies or inconsistencies between their values.
Estimate (or read, if labels are present) the absolute values of specific
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regions of interest. Also note where there are no markers. If colour is
being used to further break down the categories of the values shown,
identify any grouped patterns that emerge.
PRESENTATION TIPS
INTERACTIVITY: Interaction may be helpful to reveal location and
value labels through selection or mouseover events.
ANNOTATION: Directly labelling the shapes with geographical
details and the value they hold is likely to lead to too much clutter. You
might therefore include only a limited number of regional labels to
provide spatial context and orientation. Legends explaining the size
scales – and any colour associations – should ideally be placed as close
to the map display as possible. Avoid including unnecessary
geographical details that add no value to the spatial orientation or
interpretation and clutter the display (e.g. roads, building structures).
COLOUR: Sometimes the circular shapes are filled, at other times they
remain unfilled. If colours are being used to distinguish the different
categories, ensure these are as visibly different as possible. When a
circle has a large value its shape will transgress well beyond the origin
of its geographical location, intruding on and overlapping with other
neighbouring values. The use of outline borders and semi-transparent
colours helps with the task of avoiding occlusion (visually hiding values
behind others). When including a projected mapping layer image in the
background, ensure it is not overly competing for visual prominence by
making it light in colour and possibly semi-transparent.
VARIATIONS & ALTERNATIVES
Variations may see the typical circle replaced by squares and
geographical space replaced by anatomical regions. Alternatives to the
proportional symbol map include the ‘choropleth map’, which colour-
codes regions, or the ‘dot map’, which uses a dot to represent an
instance of something. Avoid the temptation to turn the circle symbols
into pie charts; it is not a good look. If you absolutely positively have to
show a part-to-whole relationship, only show two categories, as per the
recommended practice for pies.
Charts Overlays
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Prism map
ALSO KNOWN AS Isometric map, spike map, datascape
REPRESENTATION DESCRIPTION
A prism map displays quantitative values for locations on a map. The
values are represented via proportionally sized lines, appearing as 3D
bars, that typically cover a fixed surface area of space and are just
extended in height proportionally to represent the quantitative value for
that location. Being able to judge the dimensions of 3D forms in a 2D
view is very difficult, so they are only ever really used to create a gist of
the profile of values, enabling recognition of the main peaks in
particular.
EXAMPLE Mapping the population of trees for each 180 square km of
land across the globe.
Figure 6.51 Trillions of trees
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HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Establish the area size value associations, usually found via
a legend. Glance across the entire chart to locate the large, medium and
small shapes and perform global comparisons to establish the high-level
ranking of biggest values > smallest. Identify any noticeable exceptions
and/or outliers. Gradually zoom in your focus to perform increasingly
local comparisons between neighbouring regional areas to identify any
noticeable consistencies or inconsistencies between their values.
Estimate (or read, if labels are present) the absolute values of specific
regions of interest. Also note where there are no bars emerging from the
surface.
PRESENTATION TIPS
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INTERACTIVITY: Ideally prism maps would be provided with
interactive features that allow panning around the map region to offer
different viewing angles to overcome the perceptual difficulties of
judging the dimensions of 3D forms in a 2D view. Without this, smaller
values will be hidden behind the larger forms, just as smaller buildings
are hidden by skyscrapers in a city.
ANNOTATION: Directly labelling the prism shapes is infeasible – at
most you might include only a limited number of labels to provide
spatial context and orientation against the largest forms. Legends
explaining the size scales should ideally be placed as close to the map
display as possible.
COLOUR: Most tools that enable this type of mapping will likely have
visual property settings for a faux light effect, helping the physical
shapes to emerge more prominently through light and shadow. Ensure
colour assist in helping the shape of the forms to be as visible as
possible, maybe with opacity to enable smaller values to be not entirely
hidden behind any larger ones. When including a mapping layer image
on the surface, ensure it is not overly competing for visual prominence
by making it light in colour and possibly semi-transparent. Do not
include any unnecessary geographical details that add no value to the
spatial orientation or interpretation and clutter the display (e.g. roads,
building structures).
COMPOSITION: Be aware that the transformation adjustments made
by some map projections can distort the size of regions of the world,
inflating their size relative to other regions.
VARIATIONS & ALTERNATIVES
Alternatives to the prism map, especially to avoid 3D form, include the
‘proportional symbol map’, which uses proportionally sized geometric
shapes, and the ‘choropleth map’, which colour-codes regional shapes.
Charts Overlays
353

Dot map
ALSO KNOWN AS Dot distribution map, pointillist map, location
map, dot density map
REPRESENTATION DESCRIPTION
A dot map displays the geographic density and distribution of
phenomena on a map. It uses a point marker to indicate a categorical
‘observation’ at a geographical coordinate, which might be plotting
instances of people, notable sites or incidences. The point marker is
usually a filled, small dot. Colour can be used to distinguish categorical
classifications. Sometimes a dot represents a one-to-one phenomenon
(i.e. a single record at that location) and sometimes a dot will represent
one-to-many phenomena (i.e. for an aggregated statistic whereby the
location represents a logical mid-point). As the proliferation of GPS
recording devices increases, the accuracy and prevalence of detailed
location marked incidences are leading to increased potential for this
type of approach. However, think carefully about the potential
sensitivity of directly plotting a phenomenon or data incidence at a
given location.
EXAMPLE Mapping each resident of the USA based on the location at
which they were counted during the 2010 Census across different
ethnicities.
Figure 6.52 The Racial Dot Map
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HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the phenomenon that is being represented.
Establish the unit of this measure (is it a one-to-one relationship or one-
to-many?) by referring to a legend. If categorical colours have been
deployed, establish the different classifications and associations. Scan
the chart looking for the existence of noticeable clusters as well as the
widely dispersed (and maybe empty) regions. Some of the most
interesting observations come from individual outliers that stand out
separately from others. Are there any patterns between the presence of
dots and their geographical location? Are there any patterns across the
points with similar categorical colour? Gradually zoom in your focus to
perform increasingly local assessments between neighbouring regional
areas to identify any noticeable consistencies or inconsistencies
between their patterns.
PRESENTATION TIPS
INTERACTIVITY: One method for dealing with plotting high
quantities of observations is to provide interactive semantic zoom
features, whereby each time a user zooms in by one level of focus, the
unit quantity represented by each dot decreases, from a one-to-many
towards a one-to-one relationship.
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ANNOTATION: Direct labelling is not necessary, just provide a
limited number of regional labels to offer spatial context and
orientation. Legends explaining the dot unit scale and any colour
associations should ideally be placed as close to the map display as
possible.
COLOUR: If colours are being used to distinguish the different
categories, ensure these are as visibly different as possible. When
including a mapping layer image in the background, ensure it is not
overly competing for visual prominence by making it light in colour
and possibly semi-transparent. Do not include any unnecessary
geographical details that add no value to the spatial orientation or
interpretation and clutter the display (e.g. roads, building structures).
COMPOSITION: Dot maps must always be displayed on a map that
demonstrates an equal-area projection as the precision of the plotted
locations is paramount. From a readability perspective, try to find a
balance between making the size of the dots small enough to preserve
their individuality but not too tiny to be indecipherable.
VARIATIONS & ALTERNATIVES
A ‘dot density map’ is a variation that involves plotting a representative
quantity of dots equally (but randomly) across and within a defined
spatial region. The position of individual dots is therefore not to be read
as indicative of precise locations but used to form a measure of
quantitative density. This offers a useful alternative to the choropleth
map, especially when categorical separation of the dots through colour
is of value. Plotting the location of an incidence of a phenomenon can
transcend geographical mapping to any spatial display, such as the seat
layout and availability at a theatre or on a flight, or showing the key
patterns of play across a sports pitch.
Charts Overlays
Flow map
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ALSO KNOWN AS Connection map, route map, stream map, particle
flow map
REPRESENTATION DESCRIPTION
A flow map shows the characteristics of the movement or flow of a
phenomenon across spatial regions. It is often formed using line marks
to map flow and combinations of attributes to display the characteristics
of this flow. Examples might include the patterns of traffic and travel
across or between given routes, the dynamics of the patterns of weather,
or the movement patterns of people or animals. There is no fixed
template for a flow map but it generally displays characteristics of
origin and destination (positions on a map), route (using organic or
vector paths), direction (arrow or tapered line width), categorical
classification (colour) and some quantitative measure (line weight or
motion speed).
EXAMPLE Mapping the average number of vehicles using Hong
Kong’s main network of roads during 2011.
Figure 6.53 Arteries of the City
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HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the phenomenon that is being displayed.
Establish the association of all visible attributes to understand fully their
classification and representation, such as the use of quantitative scales
(colour, line size or width) or categorical associations (colour). Scan the
chart looking for the existence of patterns of movement, maybe through
clustering or common direction, and identify any main hubs and
densities within the network. Find the large and the small, the dense and
the sparse, and draw out any patterns formed by colour classifications.
Gradually zoom in your focus to perform increasingly local assessments
between neighbouring regional areas to identify any noticeable
consistencies or inconsistencies between their patterns.
PRESENTATION TIPS
INTERACTIVITY: Animated sequences will be invaluable to convey
motion if the nature of the flow being presented has the relevant physics
of movement.
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ANNOTATION: Annotation needs will be unique to each approach
and the inherent complexity or otherwise of the display. Often the
general patterns may offer the sufficient level of readability without the
need for imposing amounts of value labels.
COLOUR: The colour relationship needs careful consideration to get
the right balance between the intricacies of the foreground data layer
and the background mapping layer image. Ensure the background is not
overly competing for visual prominence by making it light in colour
and possibly semi-transparent. Do not include any unnecessary
geographical details that add no value to the spatial orientation or
interpretation, but do include those features that have a direct
association with the subject matter (such as roads, routes, etc.).
COMPOSITION: Some degree of geographic distortion of routes or
connecting lines may be required practically to display flow data.
Choices like interpolation of lines to smooth an activities route or the
merging of relatively similar pathways may be entirely legitimate but
ensure that this is made clear to the reader.
VARIATIONS & ALTERNATIVES
There are naturally many variations in how you might show flow. It
generally differs between whether you are showing point A to point B
‘connection maps’, more nuanced ‘route maps’ or surface phenomena
such as ‘particle flow maps’.
Charts Distortions
Area cartogram
ALSO KNOWN AS Contiguous cartogram, density-equalizing map
EXAMPLE Mapping the measures of climate change responsibility
compared to vulnerability across all countries.
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REPRESENTATION DESCRIPTION
An area cartogram displays the quantitative values associated with
distinct definable spatial regions on a map. Each geographic region is
represented by a polygonal area based on its outline shape with the
collective regional shapes forming the entire landscape. (Note that most
tools for mapping have a predetermined reference between a region
name and the dimensions of the regional polygon.) Quantitative values
are represented by proportionately distorting (inflating or deflating) the
relative size of and, to some degree, shape of the respective regional
areas. Traditionally, area cartograms strictly aim to preserve the
neighbourhood relationships between different regions. Colour is
sometimes used to further represent the same quantitative value or to
associate the region with a categorical classification. Area cartograms
require the reader to be relatively familiar with the original size and
shape of regions in order to be able to establish the degree of relative
change in their proportions. Without this it is almost impossible to
assess the degree of distortion and indeed to identify the regions
themselves.
Figure 6.54 The Carbon Map
HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Establish the quantitative value scales or categorical
classifications associated with the colour scale, usually found via a
legend. Glance across the entire chart to locate the big-, small- and
medium-sized shapes according to their apparent distortion. Identify
any noticeable exceptions and/or outliers. Gradually zoom in your focus
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to perform increasingly local comparisons between neighbouring
regional areas to identify any noticeable consistencies or inconsistencies
between their values. Estimate (or read, if labels are present) the
absolute values of specific regions of interest.
PRESENTATION TIPS
INTERACTIVITY: Animated sequences enabled through interactive
controls can help to better identify instances and degrees of change but
usually only over a small set of regions and only if the change is
relatively smooth and sustained. Manual animation will help provide
more control over the experience.
ANNOTATION: Directly labelling the regional areas with
geographical details and the value they hold is likely to lead to too
much clutter. You might include only a limited number of regional
labels to provide spatial context and orientation.
COLOUR: Legends explaining any colour scales should ideally be
placed as close to the map display as possible. The border colour and
stroke width for each spatial area should be distinguishable to define the
shape but not so prominent as to dominate attention, usually a subtle
grey- or white-coloured thin stroke will be fine.
COMPOSITION: To aid the readability of the size of the distortions, it
can be useful to present a thumbnail view of the undistorted original
geographical layout to help the readers orient themselves with the
changes.
VARIATIONS & ALTERNATIVES
Unlike contiguous cartograms, non-contiguous cartograms tend to
preserve the shape of the individual polygons but modify the size and
the neighbouring connectivity to other adjacent regional polygon areas.
The best alternative ways of showing similar data would be to consider
using the ‘choropleth map’ or ‘Dorling cartogram’.
Charts Distortions
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Dorling cartogram
ALSO KNOWN AS Demers cartogram
REPRESENTATION DESCRIPTION
A Dorling cartogram displays the quantitative values associated with
distinct, definable spatial regions on a map. Each geographic region is
represented by a circle which is proportionally sized to represent a
quantitative value. The placement of each circle generally resembles the
region’s geographic location with general preservation of
neighbourhood relationships between adjacent shapes. Colour is used to
associate the region with a categorical classification.
EXAMPLE Mapping the predicted electoral voting results for each
state in the 2012 Presidential Election.
Figure 6.55 Election Dashboard
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HOW TO READ IT & WHAT TO LOOK FOR
Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Establish the quantitative value scales or categorical
classifications associated with the colour scale, usually found via a
legend. Glance across the entire chart to locate the big-, small- and
medium-sized shapes. Identify any noticeable exceptions and/or
outliers. Gradually zoom in your focus to perform increasingly local
comparisons between neighbouring regional areas to identify any
noticeable consistencies or inconsistencies between their values.
Estimate (or read, if labels are present) the absolute values of specific
regions of interest.
PRESENTATION TIPS
INTERACTIVITY: Interactive features that enable annotation for
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category and value labelling can be useful to overcome the difficulties
associated with the geographic distortion.
ANNOTATION: Directly labelling the shapes with geographical
details and the value they hold is common, though you might restrict
this to the circles that have sufficient size to hold such annotation.
Otherwise you will need to decide how to handle the labelling of small
values.
COLOUR: Legends explaining the size scales and colour associations
should ideally be placed as close to the map display as possible. If
colours are being used to distinguish the different categories, ensure
these are as visibly different as possible.
COMPOSITION: Remember that preserving the adjacency with
neighbouring regions is important. Dorling cartograms tend not to allow
circles to overlap or occlude, so some accommodation of large values
might result in location distortion.
VARIATIONS & ALTERNATIVES
A variation on the approach, called the ‘Demers cartogram’, involves
the use of squares or rectangles instead of circles, which offers an
alternative way of connecting adjacent shapes. Other approaches would
be through the ‘area cartogram’ and the ‘choropleth map’.
Charts Distortions
Grid map
ALSO KNOWN AS Cartogram, bin map, equal-area cartogram,
hexagon bin map
REPRESENTATION DESCRIPTION
A grid map displays the quantitative values associated with distinct
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definable spatial regions on a map. Each geographic region (or a
statistically consistent interval of space, known as a ‘bin’) is
represented by a fixed-size uniform shape, sometimes termed a ‘tile’.
The shapes used tend to be squares or hexagons, though any tessellating
shape would work in theory in order to help arrange all the regional
tiles into a collective shape that roughly fits the real-world geographical
adjacency. Colours are applied to each regional tile either to represent a
quantitative value or to associate the region with a categorical
classification. Note that the mark used for this chart type is a point
rather than an area mark as its size attributes are constant.
EXAMPLE Showing the percentage of household waste recycled in
each council region across London between April 2013 to March 2014.
Figure 6.56 London is Rubbish at Recycling and Many Boroughs are
Getting Worse
HOW TO READ IT & WHAT TO LOOK FOR
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Acquaint yourself with the geographic region you are presented with
and carefully consider the quantitative measure that is being
represented. Identify the general layout of the constituent tiles to
determine how good a fit they are with their adjacent regions in
absolute and relative geographical terms. Establish the categorical or
quantitative classifications associated with the colour scale, usually
found via a legend. Glance across the entire chart to locate the big,
small and medium shaded tiles (if quantitative) or the main patterns
formed by the categorical colouring. Identify any noticeable exceptions
and/or outliers. Gradually zoom in your focus to perform increasingly
local comparisons between neighbouring regional areas to identify any
noticeable consistencies or inconsistencies between their values.
Estimate (or read, if labels are present) the absolute values of specific
regions of interest.
PRESENTATION TIPS
INTERACTIVITY: Interactive features that enable annotation for
category and value labelling can be useful to overcome the difficulties
associated with the geographic distortion.
ANNOTATION: Directly labelling the shapes with geographical
details is usually too hard. Some versions of the ‘grid map’ will include
abbreviated labels, maybe two digits, to indicate the region they
represent and to aid orientation. Otherwise it may require interactivity
to facilitate such annotations. Legends explaining the colour
associations should ideally be placed as close to the map display as
possible.
COLOUR: If colour is being used to distinguish the different
categories, ensure they are as visibly different as possible.
COMPOSITION: The main challenge is to find the most appropriate
and representative tile–region relationship (what is the right amount and
geographical level for each constituent tile?) and to optimise the best-fit
collective layout that preserves as many of the legitimate neighbouring
regions as possible.
VARIATIONS & ALTERNATIVES
‘Hexagon bin maps’ are specific deployments of the grid map that offer
a layout formed by a high resolution of smaller hexagons to preserve
localised details. Beyond geographical space, the grid map approach is
applicable to any spatial analysis such as in sports.
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6.3 Influencing Factors and Considerations
Having covered the fundamentals of visual encoding and profiled many
chart type options that deploy different encoding combinations you now
need to consider the general factors that will influence your specific
choices for which chart or charts to use for your data representation.
Choosing which chart type(s) to use is, inevitably, not a single-factor
decision. Rather, as ever with data visualisation, it is an imperfect recipe
made up of many ingredients. A pragmatic balance has to be found
somewhere between taking on board the range of influencing factors that
shape selections and not becoming frozen with indecision caused by the
burden of having to consider so many different issues.
Firstly, you need to reflect on the relevant factors that emerge from the
first three ‘preparatory’ stages of the design process and then supplement
this by addressing the guidance offered by the three visualisation design
principles introduced in Chapter 1. It must be emphasised that there are no
direct answers provided for you here, simply guidance. How you might
resolve the unique challenges posed by your project has to be something
you arrive at yourself.
Formulating Your Brief
Skills and resources, frequency: What charts can you actually make
and how efficiently can you create them? This is the big question.
Having the ability to create a broad repertoire of different chart types
is the vocabulary of this discipline, judging when to use them is the
literacy. What will have a great influence on the ambitions of the type
of charts you might employ is the ‘expressiveness’ of your abilities
and that of the technology (applications, programs, tools) you have
access to. Expressiveness is a term I first heard used in this context by
Arvind Satyanarayan, a Computer Science PhD candidate at Stanford
University. It describes the amount of variety and extent of control
you are provided with by a given technology in the construction of
your visualisation solution, so long as you also possess the necessary
skills to exploit such features, of course:
In a data representation context, maximum expressiveness means
you can create any combination of mark and attribute encoding
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to display your data – that is, you can create many different
charts. Programming libraries like D3.js and open source tools
like R offer broad libraries of different chart options and
customisations. The drawing-by-hand nature of Adobe Illustrator
would similarly enable you to create a wide range of solutions
(though unquestionably more manual in effort and less
replicable).
Restricted expressiveness means you have much more limited
scope to adapt different mark and attribute encodings. Indeed
you might be faced with assigning data to the fixed encoding
options afforded by a modest menu of chart types. A tool like
Excel has a relatively limited range of (useful) chart types in its
menu. While there are ways of enhancing the options through
plugins and different ‘workaround’ techniques that broaden its
scope, it is a relatively limited tool. It may, however, suffice for
most people’s visualisation ambitions. Elsewhere, there are
many web-based visualisation creation tools which are of value
for those who want quick and simple charting, though they
certainly reduce the range of options and the capability to
customise their appearance.
‘The capability to cope with the technological dimension is a key
attribute of successful students: coding – more as a logic and a mindset
than a technical task – is becoming a very important asset for designers
who want to work in Data Visualization. It doesn’t necessarily mean
that you need to be able to code to find a job, but it helps a lot in the
design process. The profile in the (near) future will be a hybrid one,
mixing competences, skills and approaches currently separated into
disciplinary silos.’ Paolo Ciuccarelli, discussing students on his
Communication Design Master Programme at Politecnico di
Milano
As you reflect on the gallery of charts, my advice would be to perform an
assessment of the charts you can make using a scoring system as follows:
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For any of the charts that fail to score 3 points, here are some
strategies to dealing with this:
Tools are continually being enhanced. The applications you use
now that cannot create, for example, a Sankey diagram, may
well offer that in the next release. So wait it out!
For those charts that currently score 1 or 0 points, look around
the web for examples of workaround approaches that will help
you achieve them. For example, you might use conditional
formatting in an Excel worksheet to create a rudimentary heat
map. This is not a chart type offered as standard within the tool
but represents an innovative solution through appropriating
existing features intended to serve other purposes. Any such
solutions, though, have to be framed by the frequency of your
work – will this work realistically need to be replicable and
repeatable (for example, every month) and does my solution
make that achievable?
Invest time in developing skills in the other tools to broaden
your repertoire. Tools like R have a large community of users
sharing code, tutorials and examples, resources that would
greatly help to facilitate your learning.
Lower your ambitions. Sometimes the most significant discipline
to demonstrate is acknowledging what you cannot do and
accepting that (at least, for now) you might need to sacrifice the
ideal choices you would make for more pragmatic ones.
Purpose: Should you even seek to represent you data in chart form? Will
it add any value, enabling new insights or greater perceptual efficiency
compared with its non-visualised form? Will portraying your data via an
elegantly presented table, offering the viewer the ability to look up and
reference values, actually offer a more suitable solution? Do not rule out
the value of a table. Additionally, perhaps you are trying to represent
something in chart form that would actually be better displayed through
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information-based (rather than data-based) explanations using imagery,
textual anecdotes, video and photos? Most of the time the charting of data
will be fit for purpose, but just keep reminding yourself that you do not
have to chart everything – just make sure you are doing it to add value.
‘I was in the middle of this huge project, juggling as fast and as focused
as I could, and I had this idea of a set of charts stuck in my head that
kept resurfacing. And then, as we were heading close to deadline, I
realized I couldn’t do it. I failed. I couldn’t make it work. Because we
had pictures of the children, and that was enough … I had to let it go.’
Sarah Slobin, Visual Journalist, discussing a project profiling a
group of families with children who have a fatal disease
Purpose map: In defining the ‘tone’ of the project, your were determining
what the optimum perceptibility of your data would be for your audience.
Your definitions were based on whether you were aiming to facilitate the
reading of the data or more a general feeling of the data? Were you
concerned with enabling precise and accurate perceptions of values or is it
more about the sense-making of the big, medium and small judgments –
getting the ‘gist’ of values more than reading back the values? Were there
emotional qualities that you wanted to emphasise perhaps at the
compromise of perceptual efficiency? Maybe there was a balance between
the two?
How these tonal definitions apply specifically to data representation
requires our appreciation of some fundamental theory about data
visualisation. In his book Semiology Graphique, published in 1967,
Jacques Bertin was the first, most notable author to propose the idea that
different ways of encoding data might offer varying degrees of
effectiveness in perception. In 1984 William Cleveland and Robert McGill
published a seminal paper, ‘Graphical Perception: Theory,
Experimentation, and Application to the Development of Graphical
Methods’, that offered more empirical evidence of Bertin’s thoughts. They
produced a general ranking that explained which attributes used to encode
quantitative values would facilitate the highest degree of perceptual
accuracy. In 1986, Jock Mackinlay’s paper, ‘Automating the Design of
Graphical Presentations of Relational Information’, further extended this
to include proposed rankings for encoding categorical nominal and
categorical ordinal data types as well as quantitative ones. The table shown
in Figure 6.57, adapted from Mackinlay’s paper, presents the ‘Ranking of
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Perceptual Tasks’.
In a nutshell, this ancestry of studies reveals that certain attributes used to
encode data may make it easier, and others may make it harder, to judge
accurately the values being portrayed. Let’s illustrate this with a couple of
examples. Looking at Figure 6.58, ask yourself: if A is 10, how big is B in
the respective bar and circular displays?
In both cases the answer is B = 5, but while the B ‘bar’ being 5 feels about
right, the idea that the B ‘circle’ is 5 does not feel quite right. That is
because our ability to perform relative judgements for the length of bars is
far more precise and accurate than the relative judgements for the area of
circles. This is explained by the fact that when judging the variation in size
of a line (bar) you are detecting change in a linear dimension, whereas the
variation in size of a geometric area (circle) occurs across a quadratic
dimension. If you look at the rankings in Figure 6.57 in the ‘Quantitative’
column, you will see the encoding attribute of Length is ranked higher than
the attribute of Area.
Figure 6.57 The Ranking of Perceptual Tasks
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Figure 6.58 Comparison of Judging Line Size vs Area Size
Now let’s consider an example (Figure 6.59) that shows the relative
accuracy of using different dimensions of colour variation to represent
categorical nominal values. In the next pair of charts you can see different
attributes being used to represent the categorical groupings of the points in
the respective scatter plots. On the left you can see variation in the
attribute of colour hue (blue, orange and green) to separate the categories
visually; on the right you will see the attribute of shape (diamond, circle
and square) applied to the same category groupings. What you should be
experiencing is a far more immediate, effortless and accurate sense of the
groupings of the coloured category markers compared with the shaped
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category markers. It is simply easier to spot the associations through
variation in colour than variation in shape. This explains why colour hue is
much higher in the proposed rankings for nominal data than shape.
Figure 6.59 Comparison of judging related items using variation in colour
(hue) vs variation in shape
So you can see from these simple demonstrations that there are clearly
ways of encoding data that will make it easier to read values accurately
and efficiently. However, as Cleveland and McGill stress in their paper,
this ranking should be taken as only one ingredient of guidance: ‘The
ordering does not result in a precise prescription for displaying data but
rather is a framework within which to work’.
This is important to note because you have to take into account other
factors. You have to decide whether precise perceiving is actually what
you need to facilitate for your readers. If you do, then the likes of the bar
chart – through the variation in length of a bar – will evidently offer a very
precise approach. As stated in Chapter 3, that is why they are such an
important part of your visual artillery.
However, sometimes getting a ‘gist’ of the data is sufficient. A few pages
ago I presented an image of a bubble chart on my website’s home page,
showing the popularity of my blog posts over the previous 100-day period.
The purpose of this display was purely to give visitors a sense of the
general order of magnitude from the most popular to the relative least
popular posts. I do not need visitors to form a precise understanding of
absolute values or exact rankings. I just want them to get a sense of the
ranking hierarchy. I can therefore justify moving down the quantitative
attribute rankings proposed and deploy a series of circles that encode the
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visitor totals through the size of their area (colour is used to represent
different article categories). The level of perceptibility (accuracy and
efficiency) that I need to facilitate is adequately achieved by the resulting
‘frogspawn’-like display. Furthermore, it offers an appealing and varied
display that suits the purpose of this front-page navigation device.
In practice, what all this shows is that chart types vary in the relative
efficiency and accuracy of perception offered to a viewer. Moreover, many
of the charts shown in the gallery can therefore only ever facilitate a gist of
the values of data due to the complexity of their mark and attribute
combinations and the amount of data values they might typically contain
(e.g. the treemap often has many parts of a whole in a single display). It is
up to you to judge what the right threshold is for your purpose.
Working With Data
Data examination: Inevitably, the physical characteristics of your
data are especially influential. What types of data you are trying to
display will have a significant impact on how you are able to show
them. Only certain types of data can fit into certain chart types; only
certain chart types can accommodate certain types of data. That is
why it is often most useful practically to think of this task in terms of
chart types and particularly in terms of these as templates, able to
accommodate specific types of data.
For example, representing data through a bar chart requires one
categorical variable (e.g. department) and one quantitative variable
(e.g. maximum age). If you want to show a further categorical
variable (let’s say, to break down departments by gender) you are
going to need to switch ‘template’ and use something like a clustered
bar chart which can accommodate this extra dimension.
I explained earlier how the shape of data influenced the viability of
the flower metaphor used in the ‘Better Life Index’. The range of
categorical and quantitative values will certainly influence the most
appropriate chart type choice. For example, suppose you want to
show some part-to-whole analysis and you have only three parts
(three sub-categories belonging to the major category or whole) then
a treemap really does not make a great deal of sense – they are better
at representing many parts to a whole. The unloved pie chart would
probably suffice if the percentage values were quite diverse otherwise
the bar chart would be best.
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Beyond the size and shape of your data you also might be influenced
by its inherent meaning. Sometimes, you will have scope in your
encoding choices to incorporate a certain amount of visual immediacy
in accordance with your topic. The flowers of the Better Life Index
feel consistent in metaphor with the idea of better life: the more in
bloom the flowers, the more colourful and proud each petal appears
and the better the quality of life in that country. There is a congruence
between subject matter and visual form. Think about the billionaires’
project from earlier in the chapter, with rankings displayed by
industry. Each point marking each billionaire was a small caricature
face. This is not necessary – a small circular mark for each person
would have been fine – but by using a face for the mark it creates a
more immediate recognition that the subject matter is about people.
Data exploration: One consistently useful pointer towards how you
might visually communicate your data to others is to consider which
techniques helped you to unearth key insights when you were visually
exploring the data. What chart types have you already tried out and
maybe found to reveal interesting patterns? Exploratory data analysis
is, in many ways, a bridge to visual communication: the charts you
use to inform yourself often represent prototype thinking on how you
might communicate with others. The design execution may end up
being different once you introduce the influence of audience
characteristics into your thinking, naturally, but if a method is already
working, why not utilise the same approach again?
‘Effective graphics conform to the Congruence Principle according to
which the content and format of the graphic should correspond to the
content and format of the concepts to be conveyed.’ Barbara Tversky
and Julie Bauer Morrison, taken from Animation: Can it Facilitate?
Establishing Your Editorial Thinking
Angle: When articulating the angles of analysis you intend to portray
to your viewers, you are effectively dictating which chart types might
be most relevant. If you intend to show how quantities have changed
over time, for example, there will be certain charts best placed to
portray that and many others that will not. By expressing your desired
editorial angles of analysis in language terms, this will be extremely
helpful in identifying the primary families of charts across the
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CHRTS taxonomy that will provide the best option.
It is vital to treat every representation challenge on its own merits –
do not fall into the trap of going through the motions. Just because
you have spatial data does not mean that the most useful portrayal of
that data will be via a map. If the interesting insights are not
regionally and spatially significant, then the map may not provide the
most relevant window on that data. The composition of a map – the
shape, size and positioning of the world’s regions – is so diverse,
inconsistent and truly non-uniform that it may hinder your analysis
rather than illuminate it. So always make sure you have carefully
considered the relevance of your chosen angle through your editorial
thinking.
Trustworthy Design
Avoiding deception: In the discussion about tone I explained how
variations in the potential precision of perception may be appropriate
for the purpose and context of your work. Precision in perception is
one thing, but precision in design is another. Being truthful and
avoiding deception in how you portray data visually are fundamental
obligations.
There are many ways in which viewers can be deceived through
incorrect and inappropriate encoding choices. The main issues around
deception tend to concern encoding the size of quantities. For
beginners, these mistakes can be entirely innocent and unintended but
need to be eradicated immediately.
Geometric calculations – When using the area of shapes to
represent different quantitative values, the underlying geometry
needs to be calculated accurately. One of the common mistakes
when using circles, for example, is simply to modify the
diameters: if a quantitative value increases from 10 to 20, just
double the diameter, right? Wrong. That geometric approach
would be a mistake because, as viewers, when perceiving the
size of a circle, it is the area, not the width, of the circle upon
which we base our estimates of the quantitative value being
represented.
Figure 6.60 Illustrating the correct and incorrect circle size
encoding
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The illustration in Figure 6.60 shows the incorrect and correct
ways of encoding two quantitative values through circle size,
where the value of A is twice the size of B. The orange circle for
B has half the diameter of A, the green circle for B has half the
area of A. The green circle area calculations are the correct way
to encode these two values, whereas the orange circle
calculations disproportionately shrink circle B by halving the
diameter rather than halving the area. This makes it appear much
smaller than its true value.
3D decoration – In the vast majority of circumstances the use of
3D charts is at best unnecessary and at worst hugely distorting in
the display of data. I have some empathy for those who might
volunteer that they have made and/or like the look of 3D charts.
In the past I did too. Sometimes we don’t know not to do
something until we are told. So this is me, here and now, telling
you.
The presence of 3D in visualisation tends to be motivated by a
desire to demonstrate technical competence with the features of
a tool in terms of ‘look how many things I know how to do with
this tool!’ (users of Excel, I am pointing an accusatory finger at
you right now). It is also driven by the appetite of rather
unsophisticated viewers who are still attracted by the apparent
novelty of 3D skeuomorphic form. (Middle and senior
management of the corporate world, with your ‘make me a fancy
chart’ commands, my finger of doom is now pointing in your
direction.)
Using psuedo-3D effects in your charts when you have only two
dimensions of data means you are simply decorating data. And
when I say ‘decorating’, I mean this with the same sneer that
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would greet memories of avocado green bathrooms in 1970s
Britain. A 3D visualisation of 2D data is gratuitous and distorts
the viewer’s ability to read values within any degree of
acceptable accuracy. As illustrated in Figure 6.61, in perceiving
the value estimates of the angles and segments in the respective
pie charts, the 3D version makes it much harder to form accurate
judgements. The tilting of the isometric plane amplifies the front
part of the chart and diminishes the back. It also introduces a
raised ‘step’ which is purely decorative, thus embellishing the
judgement of the segment sizes.
Figure 6.61 Illustrating the Distortions Created by 3D
Decoration
Furthermore, for charts based on three dimensions of data, 3D
effects should only be considered if – and only if – the viewer is
provided with means to move around the chart object to establish
different 2D viewing angles and the collective representation of
all the 3D of data makes sense in showing a whole ‘system’.
Truncated axis scales – When quantitative values are encoded
through the height or length components of size (e.g. for bar
charts and area charts), truncating the value axis (not starting the
range of quantitative values from the true origin of zero) distorts
the size judgements. I will look at this in more detail in the
chapter on composition because it is ultimately more about the
size considerations of scales and deployment of chart apparatus
than necessarily just the representation choices.
Accessible Design
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The bullet chart is a derivative of the bar chart – the older, more
sophisticated brother of the idiot gauge chart – but I didn’t think it was
necessary to profile as a separate chart type.
Encoded overlays: Beyond the immediate combinations of marks
and attributes that comprise a given chart type, you may find value in
incorporating additional detail to help viewers with the perceiving
and interpretation task. Encoded overlays are useful to help explain
further the context of values and amplify the interpretation of the
good and the bad, the normal and the exceptional. In some ways these
features might be considered forms of annotation, but as they
represent data values (and therefore require encoding choices) it
makes sense to locate these options within this chapter. There are
many different types of visual overlays that may be useful to include:
Figure 6.62 Example of a Bullet Chart Using Banding Overlays
Figure 6.63 Excerpt from ‘What’s Really Warming the World?’
Bandings – These are typically shaded areas that provide some
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sense of contrast between the main data value marks and
contextual judgements of historic or expected values. In a bullet
chart (Figure 6.62) there are various shaded bands that might
help to indicate whether the bar’s value should be considered
bad, average or good. In the line chart (Figure 6.63) here you can
see the observed rise in global temperatures. To facilitate
comparison with potentially influencing factors, in the
background there is a contextual overlay showing the change in
greenhouse gases with banding to indicate the 95% confidence
interval.
Markers – Adding points to a display might be useful to show
comparison against a target, forecast, a previous value, or to
highlight actual vs budget. Figure 6.64 shows a chart that
facilitates comparisons against a maximum value marker.
Figure 6.64 Example of Using Markers Overlays
Figure 6.65 Why Is Her Paycheck Smaller?
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Reference lines – These are useful in any display that uses
position or size along an axis as an attribute for a quantitative
value. Line charts or scatter plots (Figure 6.65) are particularly
enhanced by the inclusion of reference lines, helping to direct
the eye towards calculated trends, constants or averages and,
with scatter plots specifically, the lines of best fit or correlation.
Elegant Design
Visual appeal: This fits again with the thinking about ‘tone’ and may
also be informed by some of the mental visualisations that might have
formed in the initial stages of the process. Although you should not
allow yourself to be consumed by ideas over the influence of the data,
sometimes there is scope to squeeze out an extra sense of stylistic
association between the visual and the content. For example, the
‘pizza’ pie chart in Figure 6.66 presents analysis about the political
contributions made by companies in the pizza industry. The decision
to use pizza slices as the basis of a pie chart makes a lot of sense. The
graphic in Figure 6.67 displays the growth in online sales of razors.
Like the pizzas, the notion of creating bar charts by scraping away
lengths of shaving foam offers a clever, congruent and charming
solution.
Figure 6.66 Inside the Powerful Lobby Fighting for Your Right to Eat
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Pizza
Figure 6.67 Excerpt from ‘Razor Sales Move Online, Away From
Gillette’
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Summary: Data Representation
Visual Encoding All charts are based on combinations of marks and
attributes:
Marks: represent records (or aggregation of records) and can be
points, lines, areas or forms.
Attributes: represent variable values held for each record and can
include visual properties like position, size, colour, connection.
Chart Types If visual encoding is the fundamental theoretical
understanding of data representation, chart types are the practical
application. There are five families of chart types (CHRTS mnemonic):
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Influencing Factors and Considerations
Formulating the brief: skills and resources – what charts can you
make and how efficiently? From the definitions across the ‘purpose
map’ what ‘tone’ did you determine this project might demonstrate?
Working with data: what is the shape of the data and how might that
impact on your chart design? Have you already used a chart type to
explore your data that might prove to be the best way to communicate
it to others?
Establishing your editorial thinking: what is the specific angle of the
enquiry that you want to portray visually? Is it relevant and
representative of the most interesting analysis of your data?
Trustworthy design: avoid deception through mistaken geometric
calculations, 3D decoration, truncated axis scales, corrupt charts.
Accessible design: the use of encoded overlays, such as bandings,
markers, reference lines, can aid readability and interpretation.
Elegant design: consider the scope of certain design flourishes that
might enhance the visual appeal through the form of your charts
whilst also preserving their function.
Tips and Tactics
Data is your raw material, not your ideas, so do not arrive at this stage
desperate and precious about wanting to use a certain data
representation approach.
Be led by the preparatory work (stages 1 to 3) but do use the chart
type gallery for inspiration if you need to unblock!
Be especially careful in how you think about representing instances of
zero, null (no available data) and nothing (no observation).
Do not be too proud to acknowledge when you have made a bad call
or gone down a dead end.
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7 Interactivity
The advancement of technology has entirely altered the nature of how we
consume information. Whereas only a generation ago most visualisations
would have been created exclusively for printed consumption,
developments in device capability, Internet access and bandwidth
performance have created an incredibly rich environment for digital
visualisation to become the dominant output. The potential now exists for
creative and capable developers to produce powerful interactive and
engaging multimedia experiences for cross-platform consumption.
Unquestionably there is still an fundamental role for static (i.e. not
interactive) and print-only work: the scope offered by digital simply
enables you to extend your reach and broaden the possibilities. In the right
circumstances, incorporating features of interactivity into your
visualisation work offers many advantages:
It expands the physical limits of what you can show in a given space.
It increases the quantity and broadens the variety of angles of analysis
to serve different curiosities.
It facilitates manipulations of the data displayed to handle varied
interrogations.
It increases the overall control and potential customisation of the
experience.
It amplifies your creative licence and the scope for exploring different
techniques for engaging users.
The careful judgements that distinguish this visualisation design process
must be especially discerning when handling this layer of the anatomy.
Well-considered interactivity supports, in particular, the principle of
‘accessible’ design, ensuring that you are adding value to the experience,
not obstructing the facilitation of understanding. Your main concern in
considering potential interactivity is to ensure the features you deploy are
useful. This is an easy thing to say about any context but just because you
can does not mean to say you should. For some who possess a natural
technical flair, there is often too great a temptation to create interactivity
where it is neither required nor helpful.
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Having said that, beyond the functional aspects of interactive design
thinking, depending on the nature of the project there can be value
attached to the sheer pleasure created by thoughtfully conceived
interactive features. Even if these contribute only ornamental benefit there
can be merit in creating a sense of fun and playability so long as such
features do not obstruct access to understanding.
There is a lot on your menu when it comes to considering potential
interaction design features. As before, ahead of your decision making
about what you should do, you will first consider what you could do. To
help organise your thinking, your options are divided into two main groups
of features:
Data adjustments: Affecting what data is displayed.
Presentation adjustments: Affecting how the data is displayed.
There is an ever-increasing range of interfaces to enable interaction
events beyond the mouse/touch through gesture interfaces like the
Kinect device, oculus rift, wands, control pads. These are beyond the
scope of this book but it is worth watching out for developments in the
future, especially with respect to the growing interest in exploring the
immersive potential of virtual reality (VR).
When considering potential interactive features you first need to recognise
the difference between an event, the control and the function. The event is
the input interaction (such as a click), applied to a control (maybe a button)
or element on your display, with the function being the resulting operation
that is performed (filter the data).
Where once we were limited to the mouse or the trackpad as the common
peripheral, over the past few years the emergence of touch-screens in the
shape of smartphones and tablets has introduced a whole new event
vocabulary. For the purposes of this chapter we focus on the language of
the mouse or trackpad, but here is a quick translation of the equivalent
touch events. Note that arguably the biggest difference in assigning events
to interactive data visualisations exists in the inability to register a
mouseover (or ‘hover’) action with touch-screens.
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7.1 Features of Interactivity: Data
Adjustments
This first group of interactive features covers the various ways in which
you can enable your users to adjust and manipulate your data. Specifically,
they influence what data is displayed at a given moment.
I will temporarily switch nomenclature to ‘user’ in this chapter because
a more active role is needed than ‘viewer’.
Framing: There is only so much one can show in a single
visualisation display and thus giving users the ability to modify
criteria to customise what data is visible at any given point is a strong
advantage. Going back to the discussion on editorial thinking, in
Chapter 5, this set of adjustments would specifically concern the
‘framing’ of what data to isolate, include or exclude from view.
For those of you familiar with databases, think of this group of features
as similar in scope to modifying the criteria when querying data in a
database.
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In ‘Gun Deaths’ (Figure 7.1), you can use the filters in the pop-up
check-box lists at the bottom to adjust the display of selected
categorical data parameters. The filtered data is then shown in
isolation above the line from all non-selected groups, which are
shown below the line. The ‘Remove filters’ link can be used to reset
the display to the original settings.
Figure 7.1 US Gun Deaths
In the bubble map view of the ‘FinViz’ stock market analysis site,
you can change the values of the handles along the axes to modify the
maximum and minimum axis range, which allows you effectively to
zoom in on the records that match this criterion. You can also select
the dropdown menus to change the variables plotted on each axis.
Notice the subtle transparency of the filter menu (in Figure 7.1) so that
it doesn’t entirely occlude the data displayed beneath.
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Figure 7.2 FinViz: Standard and Poor’s 500 Index
Navigating: There are dynamic features that enable users to expand
or explore greater levels of detail in the displayed data. This includes
lateral movement and vertical drill-down capabilities.
You will see that many of these interactive projects include links to
share the project (or view of the project) with others via social media or
through offering code to embed work into other websites. This helps to
mobilise distribution and open up wider access to your work.
Figure 7.3 The Racial Dot Map
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The dot map in Figure 7.3, showing the 2010 Census data, displays
population density across the USA. As a user you can use a scrollable
zoom or scaled zoom to zoom in and out of different map view levels.
The map can also be navigated laterally to explore different regions at
the same resolution.
This act of zooming to increase the magnification of the view is
known as a geometric zoom. This is considered a data adjustment
because through zooming you are effectively re-framing the window
of the included and excluded data at each level of view.
In the ‘Obesity Around the World’ visualisation (Figure 7.4),
selecting a continent connector expands the sub-category display to
show the marks for all constituent countries. Clicking on the same
connector collapses the countries to revert back to the main continent-
level view.
The ‘Social Progress Imperative’ project (Figure 7.5) provides an
example of features that enable users to view the tabulated form of
the data – the highest level of detail – by selecting the ‘Data Table’
tab. The data adjustment taking place here is through providing
access to the data in a non-visual form. Users can also export the data
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by clicking on the relevant button to conduct further local analysis.
Figure 7.4 Obesity Around the World
Animating: Data with a temporal component often lends itself to
being portrayed via animated sequences. The data adjustment taking
place here involves the shifting nature of the timeframe in view at any
given point. Operations used to create these sequences may be
automatic and/or manual in nature.
Figure 7.5 Excerpt from ‘Social Progress Index 2015’
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This next project (Figure 7.6) plots NFL players’ height and weight
over time using an animated heat map. When you land on the web
page the animation automatically triggers. Once completed, you can
also select the play button to recommence the animation as well as
moving the handle along the slider to manually control the sequence.
The gradual growth in the physical characteristics of players is clearly
apparent through the resulting effect.
Sequencing: In contrast to animated sequences of the same
phenomena changing over time, there are other ways in which a more
discrete sequenced experience can suit your needs. This commonly
exists by letting users navigate through predetermined, different
angles of analysis about a subject. As you navigate through the
sequence a narrative is constructed. This is a quintessential example
of storytelling with data exploring the metaphor of the anecdote: ‘this
happened’ and then ‘this happened’…
Figure 7.6 NFL Players: Height & Weight Over Time
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The project ‘How Americans Die’ (Figure 7.7) offers a journey
through many different angles of analysis. Clicking on the series of
‘pagination’ dots and/or the navigation buttons will take you through
a pre-prepared sequence of displays to build a narrative about this
subject.
Figure 7.7 Excerpt from ‘How Americans Die’
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Sometimes data exists in only two states: a before and after view.
Using normal animated sequences would be ineffective – too sudden
and too jumpy – so one popular technique, usually involving two
images, employs the altering of the position of a handle along a slider
to reveal/fade the respective views. This offers a more graduated
sequence between the two states and facilitates comparisons far more
effectively as exhibited by the project shown in Figure 7.8.
A different example of sequencing – and an increasingly popular
trend – is the vertical sequence. This article from the Washington Post
(Figure 7.9) profiles the beauty of baseball player Bryce Harper’s
swing and uses a very slick series of illustrations to break down four
key stages of his swing action. As you scroll down the page it acts
like a lenticular print or flip-book animation. Notice also how well
judged the styles of the illustrations are.
Figure 7.8 Model Projections of Maximum Air Temperatures Near
the Ocean and Land Surface
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Figure 7.9 Excerpt from ‘A Swing of Beauty’
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Contributing: So far the features covered modify the criteria of what
data is included/excluded, that then help you dive deeper into the
data, and move through sequenced views of that data. The final
component of ‘data adjustment’ concerns contributing data.
Sometimes there are projects that require user input, either for
collecting further records to append and save to an original dataset or
just for temporary (i.e. not held beyond the moment of usage)
participation. Additionally, there may be scope to invite users to
modify certain data in order to inform calculations or customise a
display. In each case, the events and controls associated with this kind
of interaction are designed to achieve one function: input data.
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The first example ‘How well do you know your area?’ (Figure 7.10)
by ONS Digital, employs simple game/quiz dynamics to challenge
your knowledge of your local area in the UK. Using the handle to
modify the position along the slider you input a quantitative response
to the questions posed. Based on your response it then provides
feedback revealing the level of accuracy of your estimation.
Figure 7.10 How Well Do You Know Your Area?
In the next project (Figure 7.11), by entering personal details such as
your birth date, country and gender into the respective input boxes
you learn about your place in the world’s population with some rather
sobering details about your past, present and future on this planet.
Figure 7.11 Excerpt from ‘Who Old Are You?’
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Figure 7.12 shows an excerpt from ‘512 Paths to the White House’. In
this project the toggle buttons are used to switch between three
categorical data states (unselected, Democratic and Republican) to
build up a simulated election outcome based on the user’s predictions
for the winners in each of the key swing states. As each winner is
selected, only the remaining possible pathways to victory for either
candidate are shown.
Inevitably data privacy and intended usage are key issues of concern for
any project that involves personal details being contributed, so be
careful to handle this with integrity and transparency.
Adjusting the position of the handle along the slider in the Better Life
Index project (Figure 7.13) modifies the quantitative data value
representing the weighting of importance you would attach to each
quality of life topic. In turn, this modifies the vertical positioning of
the country flowers based on the recalculated average quality of life.
Figure 7.12 512 Paths to the White House
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Figure 7.13 OECD Better Life Index
7.2 Features of Interactivity: Presentation
Adjustments
In contrast to the features of ‘data adjustment’, this second group of
interactive features does not manipulate the data but rather lets you
configure the presentation of your data in ways that facilitate assistance
and enhance the overall experience.
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Focusing: Whereas the ‘framing’ features outlined previously
modified what data would be included and excluded, ‘focus’ features
control what data is visually emphasised and, sometimes, how it is
emphasised. Applying such filters helps users select the values they
wish to bring to the forefront of their attention. This may be through
modifying the effect of depth through colour (foreground, mid-
ground and background) or a sorting arrangement. The main
difference with the framing features is that no data is eliminated from
the display but simply relegated in its contrasting prominence or
position.
Figure 7.14 Nobel Laureates
The example in Figure 7.14 provides a snapshot of a project which
demonstrates the use of a focus filter. It enables users to select a radio
button from the list of options to emphasise different cohorts of all
Nobel Laureates (as of 2015). As you can see the selections include
filters for women, shared winners and those who were still living at
the time. The selected Laureates are not coloured differently, rather
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the unselected values are significantly lightened to create the contrast.
Figure 7.15 Geography of a Recession
The project shown in Figure 7.15 titled ‘Geography of a Recession’
allows users to select a link from the list of filters provided on the left
to emphasise different cohorts of counties across the USA. Once
again, the selected counties are not coloured differently here, the
unselected regions are de-emphasised by washing-out their original
shades.
Figure 7.16 How Big Will the UK Population be in 25 Years’ Time?
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‘Brushing’ data is another technique used to apply focus filters. In
this next example (Figure 7.16), looking at the UK Census estimates
for 2011, you use the cursor to select a range of marks from within
the ‘violin plot’ display in order to view calculated statistics of those
chosen values below the chart.
The next example (Figure 7.17), portraying the increase or cuts in
Workers’ Compensation benefits by US state, demonstrates a
technique known as ‘linking’, whereby hovering over a mark in one
chart display will then highlight an associated mark in another chart
to draw attention to the relationship. In this case, hovering over a state
circle in any of the presented ‘grid maps’ highlights the same state in
the other two maps to draw your eye to their respective statuses. You
might also see this technique combined with a brushing event to
choose multiple data marks and then highlight all associations
between charts, as also demonstrated in the population ‘violin plot’ in
Figure 7.16.
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Figure 7.17 Excerpt from ‘Workers’ Compensation Reforms by
State’
Sorting is another way of emphasising the presentation of data. In
Figure 7.18, featuring work by the Thomson Reuters graphics team,
‘ECB bank test results’, you see a tabular display with sorting
features that allow you to reorder columns of data by clicking on the
column headers. For categorical data this will sort values
alphabetically; for quantitative data, by value order. You can also
hand-pick individual records from the table to promote them to the
top of the display to facilitate easier comparisons through closer
proximity.
Linking and brushing are particularly popular approaches used for
exploratory data analysis where you might have several chart panels and
wish to see how a single record shows up within each display.
Annotating: As you saw in the previous chapter on data
representation, certain combinations of marks and attributes may only
provide viewers with a sense of the order of magnitude of the values
presented. This might be entirely consistent with the intended tone of
the project. However, with interactivity, you can at least enable
viewers to interact with marks to view more details momentarily.
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This temporary display is especially useful because most data
representations are already so busy that permanently including certain
annotated apparatus (like value labels, gridlines, map layers) would
overly clutter the display.
Figure 7.18 Excerpt from ‘ECB Bank Test Results’
The example in Figure 7.19, profiles the use of language throughout
the history of US Presidents’ State of the Union addresses, using
circle sizes to encode the frequency of different word mentions,
giving a gist of the overall quantities and how patterns have formed
over time. By hovering over each circle you get access to a tooltip
dialogue box which reveals annotations such as the exact word-use
quantities and extra contextual commentary.
One issue to be aware of when creating pop-up tooltips is to ensure
the place they appear does not risk obstructing the view of important
data in the chart beneath. This can be especially intricate to handle
when you have a lot of annotated detail to share. One tactic is to
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utilise otherwise-empty space on your page display, occupying it with
temporary annotated captions only when triggered by a select or
hover event from within a chart.
Orientating: A different type of interactive annotation comes in the
form of orientation devices, helping you to make better sense of your
location within a display – where you are or what values you are
looking at. Some of these functions naturally supplement features
listed in the previous section about ‘data adjustment’ specifically for
navigation support.
Figure 7.19 Excerpt from ‘History Through the President’s Words’
This snapshot, again from the ‘How Americans Die’ project (Figure
7.20), dynamically reveals the values of every mark (both x and y
values) in this line chart depending on the hover position of the
cursor. This effect is reinforced by visual guides extending out to the
axes from the current position.
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Figure 7.20 Excerpt from ‘How Americans Die’
Figure 7.21 Twitter NYC: A Multilingual Social City
Figure 7.21 displays the language of tweets posted over a period of
time from the New York City area. Given the density and number of
data points, displaying the details of the mapping layer would be quite
cluttered, yet this detail would provide useful assistance for judging
the location of the data patterns. The effective solution employed lets
you access both views by providing an adjustable slider that allows
you to modify the transparency of the network of roads to reveal the
apparatus of the mapping layer.
Figure 7.22 Killing the Colorado: Explore the Robot River
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Finally, as mentioned in the previous section, navigating through
digital visualisation projects increasingly uses a vertical landscape to
unfold a story (some term this ‘scrollytelling’). Navigation is often
seamlessly achieved by using the scroll wheel to move up and down
through the display. To assist with orientation, especially when you
have a limited field of view of a spatial display, a thumbnail image
might be used to show your current location within the overall
journey to give a sense of progress. The project featured in Figure
7.22 is a great example of the value of this kind of interface,
providing a deep exploration of some of the issues impacting on the
Colorado River.
7.3 Influencing Factors and Considerations
You now have a good sense of the possibilities for incorporating
interactive features into your work, so let’s turn to consider the factors that
will have most influence on which of these techniques you might need to
or choose to apply.
Formulating Your Brief
Skills and resources: Interactivity is unquestionably something that
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many people aspire to create in their visualisation work, but it is
something greatly influenced by the skills possessed, the technology
you have access to and what they offer. These will be the factors that
ultimately shape your ambitions. Remember, even in common
desktop tools like Excel and Powerpoint, which may appear more
limited on this front, there are ways to incorporate interactive controls
(e.g. using VBA in Excel) to offer various adjustment features (e.g.
links within Powerpoint slides to create sequences and navigate to
other parts of a document).
Timescales: It goes without saying that if you have a limited
timeframe in which to complete your work, even with extensive
technical skills you are going to be rather pushed in undertaking any
particularly ambitious interactive solutions. Just because you want to
does not mean that you will be able to.
Setting: Does the setting in which the visualisation solution will be
consumed lend itself to the inclusion of an interactive element to the
experience? Will your audience have the time and know-how to take
full advantage of multi-interactive features or is it better to look to
provide a relatively simpler, singular and more immediate static
solution?
Format: What will be the intended output format that this project
needs to be created for? What device specifications will it need to
work across? How adaptable will it need to be?
The range and varied characteristics of modern devices present
visualisers (or perhaps more appropriately, at this stage, developers)
with real challenges. Getting a visualisation to work consistently,
flexibly and portably across device types, browsers and screen
dimensions (smartphone, tablet, desktop) can be something of a
nightmare. Responsive design is concerned with integrating automatic
or manually triggered modifications to the arrangement of contents
within the display and also the type and extent of interactive features
that are on offer. Your aim is to preserve as much continuity in the
core experience as possible but also ensure that the same process and
outcome of understanding can be offered to your viewers.
While the general trend across web design practice is heading towards
a mobile-first approach, for web-based data visualisation
developments there is still a strong focus on maximising the
capabilities of the desktop experience and then maybe compromising,
in some way, the richness of the mobile experience.
For ProPublica’s work on ‘Losing Ground’ (Figure 7.23), the
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approach to cross-platform compatibility was based around the rule of
thumb ‘smallify or simplify’. Features that worked on ProPublica’s
primary platform of the desktop would have to be either simplified to
function practically on the smartphone or simply reduced in size. You
will see in the pair of contrasting images how the map display is both
shrunk and cropped, and the introductory text is stripped back to only
include the most essential information.
Figure 7.23 Losing Ground
Other format considerations include whether your solution will be
primarily intended for the Web, but will it also need to work in print?
The proverb ‘horses for courses’ comes to mind here: solutions need
to be created as fit for the format it will be consumed in. The design
features that make up an effective interactive project will unlikely
translate directly as a static, print version. You might need to pursue
two parallel solutions to suit the respective characteristics of each
output format.
Another illustration of good practice from the ‘History through the
Presidents’ words’ (Figure 7.24) includes a novel ‘Download graphic’
function which, when selected, opens up an entirely different static
graphic designed to suit a printable, pdf format.
Figure 7.24 Excerpt from ‘History Through the President’s Words’
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Purpose map: Interactivity does not only come into your thinking
when you are seeking to create ‘Exploratory’ experiences. You may
also employ interactive features for creating ‘Explanatory’
visualisations, such as portraying analysis across discrete sequenced
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views or interactively enabling focus filters to emphasise certain
characteristics of the data. The general position defined on the
purpose map will not singularly define the need for interactivity,
rather it will inform the type of interactivity you may seek to
incorporate to create the experience you desire.
There will also often be scope for an integrated approach whereby
you might lead with an explanatory experience based around showing
headline insights and then transitions into a more exploratory
experience through offering a set of functions to let users interrogate
data in more detail.
Working With Data
Data examination: As profiled with the functions to facilitate drill-
down navigation, one of the keen benefits of interactivity is when you
have data that is too big and too broad to show in one view. To
repeat, you can only show so much in a single-screen display. Often
you will need to slice up views across and within the various
hierarchies of your data.
One particular way the physical properties of the data will inform
your interaction design choices is with animation. To justify an
animated display over time, you will need to consider the nature of
the change that exists in your data. If your data is not changing much,
an animated sequence may simply not prove to be of value.
Conversely, if values are rapidly changing in all dimensions, an
animated experience will prove chaotic and a form of change
blindness will occur. It may be that the intention is indeed to exhibit
this chaos, but the value of animated sequences is primarily to help
reveal progressive or systematic change rather than random variation.
The speed of an animation is also a delicate matter to judge as you
seek to avoid the phenomenon of change blindness. Rapid sequences
will cause the stimulus of change to be missed; a tedious pace will
dampen the stimulus of change and key observations may be lost. The
overall duration will, of course, be informed by the range of values in
your temporal data variable. There is no right or wrong here, it is
something that you will get the best sense of by prototyping and
trialling different speeds.
Establishing Your Editorial Thinking
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Angle, framing and focus: If you have multiple different angles of
analysis you wish to portray then these will have to be accommodated
within the space allocated. Alternatively, using interactivity, you
could provide access to them via sequenced views or menus enabling
their selection. The value of incorporating the potential features to
achieve this – and the specific range of different options you do wish
to facilitate – will be informed by the scope of the decisions you
made in the editorial thinking stages.
Thinking again about animations, you must consider whether an
animated sequence will ultimately convey the clearest answer to an
angle of interest about how something has changed over time. This
really depends on what it is you want to show: the dynamics of a
‘system’ that changes over time or a comparison between different
states over time?
The animated project in Figure 7.25 shows the progressive clearing of
snow across the streets of New York City during the blizzard of
February 2014. The steady and connected fluidity of progress of the
snow-clearing is ideally illustrated through the intervals of change
across the 24 hours shown.
Figure 7.25 Plow: Streets Cleared of Snow in New York City
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Sometimes, you might wish to compare one moment directly against
another. With animated sequences, there is a reliance on memory to
conduct this comparison of change. However, our ability to recall is
fleeting at best and weakens the further apart (in time) the basis of the
comparison has occurred. Therefore, to facilitate such a comparison
you ideally need to juxtapose individual frames within the same view.
The most common technique used to achieve this is through small
multiples, where you repeat the same representation for each moment
in time of interest and present them collectively in the same view,
often through a grid layout. This enables far more incisive
comparisons, as you can see through ‘The Horse in Motion’ work by
Eadward Muyrbidge, which was used to learn about the galloping
form of a horse by seeing each stage of the motion through
individually framed moments.
‘Generations of masterpieces portray the legs of galloping horses
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incorrectly. Before stop-gap photography, the complex interaction of
horses’ legs simply happened too fast to be accurately apprehended …
but in order to see the complex interaction of moving parts, you need
the motion.’ [Paraphrasing] Barbara Tversky and Julie Bauer
Morrison, taken from Animation: Can it Facilitate?
Figure 7.26 The Horse in Motion
Data Representation
Chart type choice: Some charts are inherently visually complex and
ideally need interactivity to make them more accessible and readable
for the viewer. The bump chart, chord diagram, and Sankey diagram
are just a few of the charts that are far more readable and, by
extension, usable if they can offer users the means to filter or focus on
certain selected components of the display through interactivity.
Trustworthy Design
Functional performance: Faith in the reliability, consistency and
general performance of a visualisation is something that impacts on
the perception of a project as ‘trustworthy’. Does it do what it
promises and can I trust the functions that it performs? Projects that
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involve the collection of user-inputted data will carry extra risk
around trust: how will the data be used and stored? You need to
alleviate any such concerns upfront.
‘Confusing widgets, complex dialog boxes, hidden operations,
incomprehensible displays, or slow response times … may curtail
thorough deliberation and introduce errors.’ Jeff Heer and Ben
Schneiderman, taken from Interactive Dynamics for Visual Analysis
Accessible Design
Useful: Does it add value? Resort to interactivity only when you have
exhausted the possibility of an appropriate and effective static
solution. Do not underestimate how effective a well-conceived and
executed static presentation of data can be. This is not about holding a
draconian view about any greater merits offered by static or print
work, but instead recognising that the brilliance of interactivity is
when it introduces new means of engaging with data that simply
could not be achieved in any other way.
Unobtrusive: As with all decisions, an interactive project needs to
strive for the optimum ease of usability: minimise the friction
between the act of engaging with interactive features and the
understanding they facilitate. Do not create unnecessary obstacles that
stifle sparks of curiosity and the scent of intrigue that stirs within the
user. The main watchword here is affordance, making interactive
features seamless and either intuitive or at least efficiently
understandable.
Visual accessibility: To heighten the accessibility levels of your
work you may offer different presentations of it. For people with
visual impairments you might offer options to magnify the view of
your data and all accompanying text. For those with colour
deficiencies, as you will learn about shortly, you could offer options
to apply alternative, colour-blind friendly palettes. A further example
of this is seen with satellite navigation devices whereby the displayed
colour combinations change to better suit the surrounding lightness or
darkness at a given time of day.
Elegant Design
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Feature creep: The discipline required to avoid feature creep is
indisputable. The gratuitous interactive operation of today is the
equivalent of the flashy, overbearing web design trends of the late
1990s and early 2000s. People were so quick and so keen to show
how competent and expressive they could be through this (relatively)
new technology that they forgot to judge if it added value.
If your audience is quite broad you may be (appropriately) inclined to
cover more combinations of features than are necessary in the hope of
responding to as many of the anticipated enquiries as well as possible
and serving the different types of viewer. Judging the degree of
flexibility is something of a balancing act within a single project: you
do not want to overwhelm the user with more adjustments than they
need, nor do you want to narrow the scope of their likely
interrogations. For a one-off project you have to form your own best
judgement; for repeatedly used projects you might have scope to
accommodate feedback and iteration.
Minimise the clicks: With visualisation you are aiming to make the
invisible (insights) visible. Conversely, to achieve elegance in design
you should be seeking to make visible design features as seamlessly
inconspicuous as possible. As Edward Tufte stated, ‘the best design is
invisible; the viewer should not see your design. They should only see
your content’.
Fun: A final alternative influence is to allow yourself room for at
least a little bit of fun. So long as the choices do not gratuitously
interrupt the primary objective of facilitating understanding, one
should not downplay the heightened pleasure that can be generated by
interactive features that might incorporate an essence of playability.
Summary: Interactivity
Data adjustments affect what data is displayed and may include the
following features:
Framing: isolate, include or exclude data.
Navigating: expand or explore greater levels of detail in the displayed
data.
Animating: portray temporal data via animated sequences.
Sequencing: navigate through discrete sequences of different angles
of analysis.
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Contributing: customising experiences through user-inputted data.
Presentation adjustments affect how the data is displayed and may
include the following features:
Focusing: control what data is visually emphasised.
Annotating: interact with marks to bring up more detail.
Orientating: make better sense of your location within a display.
Influencing Factors and Considerations
Formulating the brief: skills and resources, timescales, setting, and
format will all influence the scope of interactivity. What experience
are you facilitating and how might interactive options help achieve
this?
Working with data: what range of data do you wish to include? Large
datasets with diverse values may need interactive features to help
users filter views and interrogate the contents.
Establishing your editorial thinking: choices made about your chosen
angle, as well as definitions for framing and focus will all influence
interactive choices, especially if users must navigate to view multiple
angles of analysis or representations portrayed through animated
sequences.
Data representation: certain chart choices may require interactivity to
enable readability.
Trustworthy design: functional performance and reliability will
substantiate the perception of trust from your users.
Accessible design: any interactive feature should prove to be useful
and unobtrusive. Interactivity can also assist with challenges around
visual accessibility.
Elegant design: beware of feature creep, minimise the clicks, but
embrace the pleasure of playability.
Tips and Tactics
Initial sketching of concepts will be worth doing first before investing
too much time jumping into prototype mode.
Project management is critical when considering the impact of
development of an interactive solution.
Backups, contingencies, version control.
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Do not be precious about – nor overly impressed with – ‘cool’-
sounding interaction features that will disproportionately divert
precious resources (time, effort, people).
Beware of feature creep: keep focusing on what is important and
relevant. A technical achievement is great for you, but is it great for
the project?
Version control and file management will be important here.
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8 Annotation
Annotation is the third layer of the visualisation design anatomy and is
concerned with the simple need to explain things: what is the right amount
and type of help your viewers will need when consuming the
visualisation?
Annotation is unquestionably the most often neglected layer of the
visualisation anatomy. Maybe this is because it involves the least amount
of pure design thinking relative to the other matters requiring attention,
like interactivity and colour. More likely, it is because effective annotation
requires visualisers truly to understand their intended audience. This can
be a hard frame of mind to adopt, especially when your potential viewers
are likely to have a diverse knowledge, range of interests and capability.
In contrast to the greater theoretical and technical concerns around data
representation, colour and interactivity, I find thinking about annotation
relatively refreshing. It is not only uncomplicated and based on a huge
dose of common sense, but also hugely influential, especially in directly
facilitating understanding.
Annotation choices often conform to the Goldilocks principle: too much
and the display becomes cluttered, overwhelming, and potentially
unnecessarily patronising; too little and the viewers may be
inappropriately faced with the prospect of having to find their own way
around a visualisation and form their own understanding about what it is
showing.
Later in this chapter we will look at the factors that will influence your
decision making but to begin with here is a profile of some of the key
features of annotated design that exist across two main groups:
Project annotations: helping viewers understand what the project is
about and how to use it.
Chart annotations: helping viewers perceive the charts and optimise
their potential interpretations.
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8.1 Features of Annotation: Project
Annotation
This collection of annotation options is related to decisions about how
much and what type of help you might need to offer your audiences in
their understanding of the background, function and purpose of your
project.
Headings: The titles and subtitles occupy such prime real estate
within your project’s layout, yet more often than not visualisers fail to
exploit these to best effect. There are no universal practices for what a
heading should do or say; this will vary considerably between subject
areas and industries, but should prove fundamentally useful.
Figure 8.1 A Sample of Project Titles
The primary aim of a title (and often subtitle combination) is to
inform viewers about the immediate topic or display, giving them a
fair idea about what they are about to see. You might choose to
articulate the essence of the curiosity that has driven the project by
framing it around a question or maybe a key finding you unearthed
following the work.
Subheadings, section headings and chart titles will tend to be more
functional in their role, making clear to the viewer the contents or
focus of attention associated with each component of the display.
Your judgement surrounds the level of detail and the type of language
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you use in each case to fit cohesively with the overall tone of the
work.
Introductions: Essentially working in conjunction with titles,
introductions typically exist as short paragraphs that explain, more
explicitly than a title can, what the project is about. The content of
this introduction might usefully explain in clear language terms some
of the components you considered during the editorial thinking
activity, such as:
details of the reason for the project (source curiosity);
an explanation of the relevance of this analysis;
a description of the analysis (angle, framing) that is presented;
expression of the main message or finding that the work is about
to reveal (possibly focus).
Some introductions will extend beyond a basic description of the project to
include thorough details of where the data comes from and how it has been
prepared and treated in advance of its analysis (including any assumptions,
modifications or potential shortcomings). There may also be further links
to ‘read more’ detail or related articles about the subject.
Figure 8.2 Excerpt from ‘The Color of Debt’
Introductions may be presented as fixed text located near the top (or
start) of a project (usually underneath a title) as in Figure 8.2 or,
through interactivity, may be hidden from view and brought up in a
separate window or pop-up to provide the details upon request.
User guides: As you have seen, some projects can incorporate many
different features of interactivity. While they may not necessarily be
overly technical – and therefore not that hard to learn how to use
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them – the full repertoire of features may be worth walking through,
as in Figure 8.3. This is important to consider so that, as a visualiser,
you can be sure your users are acquainted with the entire array of
options they have to explore, interrogate and control their experience.
You should want people to fully utilise all the different features you
have carefully curated and created, so it is in everyone’s interest to
think about including these types of user guides.
Figure 8.3 Excerpt from ‘Kindred Britain’
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Multimedia: There is increasing potential and usage of broader
media assets in visualisation design work beyond charts, such as
video and imagery. In visualisation this is perhaps a relatively
contemporary trend (infographics have incorporated such media but
visualisations generally have done so far less) and, in some ways,
reflects the ongoing blurring of boundaries between this and other
related fields. Incorporating good-quality and sympathetically styled
assets like illustrations or photo-imagery can be a valuable
complementary device alongside your data representation elements.
In the ‘Color of Debt’ (Figure 8.4) project, different neighbourhoods
of Chicago that have been hardest hit by debt are profiled using
accompanying imagery to show more graphic context of the
communities affected, including a detailed reference map of the area
and an animated panel displaying a sequence of street view images.
Imagery, in particular, will be an interesting option to consider when
it adds value to help exhibit the subject matter in tangible form,
offering an appealing visual hook to draw people in or simply to aid
immediate recognition of the topic. In Bloomberg’s billionaires
project (Figure 8.5), each billionaire is represented by a pen-and-ink
caricature. This is elegant in choice and also dodges the likely flaws
of having to compose the work around individual headshot
photographs that would have been hard to frame and colour
consistently.
Figure 8.4 Excerpt from ‘The Color of Debt’
It was worth Bloomberg investing in the time/cost involved in
commissioning these illustrations, given that the project was not a
one-off but something that would be an ongoing, updated daily
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resource.
Problems with the integration of such media within a visualisation
project will occur when unsuitable attempts are made to combine
imagery within the framework of a chart. Often the lack of cohesion
creates a significant hindrance whereby the data representations are
obscured or generally made harder to read, as the inherent form and
colour clashes undermine the functional harmony.
Researching, curating, capturing or creating assets of imagery
requires skill and a professional approach, otherwise the resulting
effect will look amateurish. Incorporating these media into a data
visualisation is not about quickly conducting some Google Image
fishing exercise. Determining what imagery you will be able to use
involves careful considerations around image suitability, quality and,
critically, usage rights. Beware the client or colleague who thinks
otherwise.
‘Although all our projects are very much data driven, visualisation is
only part of the products and solutions we create. This day and age
provides us with amazing opportunities to combine video, animation,
visualisation, sound and interactivity. Why not make full use of this? …
Judging whether to include something or not is all about editing: asking
“is it really necessary?”. There is always an aspect of “gut feel” or
“instinct” mixed with continuous doubt that drives me in these cases.’
Thomas Clever, Co-founder CLEVER°FRANKE, a data driven
experiences studio
A frequent simple example of incorporated imagery is when you have to
include logos according to the needs of the organisation for whom your
work is being created. Remember to consider this early so you at least
know in advance that you will have to assign some space to accommodate
this component elegantly.
Footnotes: Often the final visible feature of your display, footnotes
provide a convenient place to share important details that further
substantiate the explanation of your work. Sometimes this
information might be stored within the introduction component
(especially if that is interactively hidden/revealed to allow it more
room to accommodate detail):
Data sources should be provided, ideally in close proximity to
the relevant charts.
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Credits will list the authors and main contributors of the work,
often including the provision of contact details.
Figure 8.5 Excerpt from ‘Bloomberg Billionaires’
Attribution is also important if you wish to recognise the
influence of other people’s work in shaping your ideas or to
acknowledge the benefits of using an open source application or
free typeface, for example.
Usage information might explain the circumstances in which the
work can be viewed or reused, whether there are any
confidentialities or copyrights involved.
Time/date stamps are often forgotten but they will give an
indication to viewers of the moment of production and from that
they might be able to ascertain the work’s current accuracy and
contextualise their interpretations accordingly.
Figure 8.6 Excerpt from ‘Gender Pay Gap US’
8.2 Features of Annotation: Chart Annotation
This second group of annotated features concerns the ways you provide
viewers with specific assistance for perceiving and interpreting the charts.
Think of these as being the features that refer directly to your charts or
exist directly within or in immediate proximity to each chart.
Reading guides: These are written or visual instructions that provide
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viewers with a guide for how to read the chart or graphic and offer
greater detailed assistance than a legend (see later). The idea of
learnability in visualisation is an important consideration. It is a two-
way commitment requiring will and effort from the viewer and
sufficient assistance from the visualiser. This is something to be
discussed in Chapter 11 under ‘Visualisation literacy’.
Recognising that their readership may not necessarily understand
connected scatter plots, Bloomberg’s visual data team offer a ‘How to
Read this Graphic’ guide immediately as you land on the project
shown in Figure 8.7. This can be closed but a permanent ‘How to’
button remains for those who may need to refer to it again. The
connected scatter plot was the right choice for this angle of analysis,
so rather than use a different ‘safer’ representation approach (and
therefore alter what analysis was shown) it is to their credit that they
respected the capacity of their viewers to be willing to learn how to
read this unfamiliar graphical form.
Figure 8.7 Excerpt from ‘Holdouts Find Cheapest Super Bowl
Tickets Late in the Game’
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Figure 8.8 Excerpt from ‘The Life Cycle of Ideas’
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The second example shown (Figure 8.8) is from the ‘How to Read’
guide taken from the ‘Life Cycle of Ideas’ graphic created by
Accurate, a studio renowned for innovative and expressive
visualisation work. Given the relative complexity of the encodings
used in this piece, it is necessary to equip the viewer with as much
guidance as possible to ensure its potential is fully realised.
Chart apparatus: Options for chart apparatus relate to the structural
components found in different chart types. Every visualisation
displayed in this book has different elements of chart apparatus
(Figure 8.9), specifically visible axis lines, tick marks or gridlines to
help viewers orient their judgements of size and position. There is no
right or wrong for including or excluding these features, it tends to be
informed by your tonal definitions based on how much precision in
the perceiving of values you wish to facilitate. I will discuss the range
of different structures underlying each chart type (such as Cartesian,
Radial or Spatial) in Chapter 10 on composition, as these have more
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to do with issues of shape and dimension.
‘Labelling is the black magic of data visualization.’ Gregor Aisch,
Graphics Editor, The New York Times
Labels: There are three main labelling devices you will need to think
about using within your chart: axis titles, axis labels and value labels:
Axis titles describe what values are being referenced by each
axis. This might be a single word or a short sentence depending
on what best fits the needs of your viewers. Often the role of an
axis is already explained (or implied) by project annotations
elsewhere, such as titles or sub-headings, but do not always
assume this will be instantly clear to your viewers.
Axis labels provide value references along each axis to help
identify the categorical value or the date/quantitative value
associated with that scale position. For categorical axes (as seen
in bar charts and heat maps, for example) one of the main
judgments relates to the orientation of the label: you will need to
find sufficient room to fit the label but also preserve its
readability. For non-categorical data the main judgement will be
what scale intervals to use. This has to be a combination of what
is most useful for referencing values by your viewer, what is the
most relevant interval based on the nature of the data (e.g.
maybe a year-level label is more relevant than marking each
month), and also what feels like it achieves the best-looking
visual rhythm along the chart’s edge. This will be another matter
that is discussed more in the composition chapter.
Figure 8.9 Mizzou’s Racial Gap Is Typical On College
Campuses
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Value labels will appear in proximity to specific mark encodings
inside the chart. Typically, these labels will be used to reveal a
quantity, such as showing the percentage sizes of the sectors in a
pie chart or the height of bars. Judging whether to include such
annotations will refer back to your definition of the appropriate
tone: will viewers need to read off exact values or will their
perceived estimates of size and/or relationship be sufficient? The
need to include categorical labels will be a concern for maps
(whether to label locations?) or charts like the scatter plot seen in
Figure 8.9, where you may wish to draw focus to a select sample
of the categories plotted across the display.
As you have seen, one way of providing detailed value labels is
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through interaction, maybe offering a pop-up/tooltip annotation
that is triggered by a hover or click event on different mark
encodings. Having the option for interactivity here is especially
useful as it enables you to reduce clutter from your display that
can develop as more annotated detail is added.
Redundancy in labelling occurs when you include value labelling of
quantities for all marks whilst also including axis-scale labelling. You
are effectively unnecessarily doubling the assistance being offered and
so, ideally, you should choose to include one or the other.
Legend: A legend is an annotated feature within or alongside your
chart that presents one or several keys to help viewers understand the
categorical or quantitative meaning of different attributes.
Figure 8.10 Excerpt from ‘The Infographic History of the World’
For quantitative data the main role for a legend will be if the attribute
of area size has been used to encode values, as found on the bubble
plot chart type. The keys displayed there will provide a reference for
the different size scales. Which selection of sizes to show needs
careful thought: what is the most useful guide to help your viewers
make their perceptual judgements from a chart? This might not entail
showing only even interval sizes (50, 100, 150 etc.); rather, you might
offer viewers a indicative spread of sizes to best represent the
distribution of your data values. The example in Figure 8.10 shows
logical interval sizes to reflect the range of values in the data and also
helpfully includes reference to the maximum value size to explain
that no shape will be any larger than this. For categorical data you
also see a key showing the meaning of different colours and shapes
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and their associated values.
Figure 8.11 Twitter NYC: A Multilingual Social City
A nice approach to getting more out of your legends is shown in
Figure 8.11. Here you will see a key explaining the colour
associations combined with a bar chart to display the distribution of
quantities for each language grouping from this analysis of tweets
posted around New York City.
Captions: These exist typically as small passages of written analysis
that bring to the surface some of the main insights and conclusions
from the work. These might be presented close to related values
inside the chart or in separate panels to provide commentary outside
the chart.
In ‘Gun Deaths’ (Figure 8.12), there is a nice solution that combines
annotated captions with interactive data adjustments. Below the main
chart there is a ‘What This Data Reveals’ section which some of the
main findings from the analysis of the gun death data. The captions
double up as clickable shortcuts so that you can quickly apply the
relevant framing filters and update the main display to see what the
captions are referring to.
Figure 8.12 Excerpt from ‘US Gun Deaths’
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As creative tools become more ubiquitous the possibility for
incorporating non-visual data in you work increases. As an alternative
to the written caption there is greater scope to consider using audio as
a means of verbally narrating a subject and explaining key messages.
Over the past few years one of the standout projects using this feature
was the video profiling ‘Wealth Inequality in America’ (Figure 8.13),
as introduced in Chapter 3, where the voiceover provides a very
compelling and cohesive narrative against the backdrop of the
animated visuals that present the data being described.
Figure 8.13 Image taken from ‘Wealth Inequality in America’
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8.3 Typography
As you have seen, many features of annotation utilise text. This means
your choices will be concerned not just with what text to include, but also
with how it will look. This naturally merits a brief discussion about how
typography will have a significant role in the presentation of your work.
Firstly, some clarity about language. A typeface is a designed collection of
glyphs representing individual letters, numbers and other symbols of
language based on a cohesive style. A font is the variation across several
physical dimensions of the typeface, such as weight, size, condensation
and italicisation. A typeface can have one or many different fonts in its
family. Type effectively represents the collective appearance formed by the
choice of typeface and the font.
Tahoma and Century Gothic are different typefaces. This font and this font
both belong to the Georgia typeface family but display variations in size,
weight and italicisation.
I discussed earlier the distinction between definitions of data
visualisation and other related fields. I mentioned how the person
creating their design is not necessarily conscious or concerned about
what label is attached to their work, they are simply doing their work
regardless. The same could be applied to people’s interchangeable use
of and meaning of the terms typeface and font, the clarity of which has
been irreparably confused by Microsoft’s desktop tools in particular.
Serif typefaces add an extra little flourish in the form of a small line at the
end of the stroke in a letter or symbol. Garamond is an example of a serif
font. Serif typefaces are generally considered to be easier to read for long
sequences of text (such as the full body text) and are especially used in
print displays.
Sans-serif typefaces have no extra line extending the stroke for each
character. Verdana is an example of a sans-serif typeface. These typefaces
are commonly used for shorter sections of text, such as axis or value labels
or titles, and for screen displays.
In making choices about which type to use, there are echoes with the
thinking you are about to face on using colour. As you will see, colour
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decisions concern legibility and meaning first, decoration last. With
typeface choices you are not dressing up your text, you are optimising its
readability and meaning across your display. The desired style of typeface
only comes into your thinking after legibility and meaning.
In terms of legibility, you need to choose a typeface and font combination
that will be suitable for the role of each element of text you are using.
Viewers need to be able to read the words and numbers on display without
difficulty. Quite obvious, really. Some typefaces (and specifically fonts)
are more easily read than others. Some work better to make numbers as
clearly readable as possible, others work better for words. There are plenty
of typefaces that might look cool and contemporary but if they make text
indecipherable then that is plain wrong.
Typeface decisions will often be taken out of your hands by the visual
identity guidelines of organisations and publications, as well as by
technical reasons relating to browser type, software compatibility and
availability.
Just as variation in colour implies meaning, so does variation in typeface
and font. If you make some text capitalised, large and bold-weight this will
suggest it carries greater significance and portrays a higher prominence
across the object hierarchy than any text presented in lower case, with a
smaller size and thinner weight. So you should seek to limit the variation
in font where possible.
Text-based annotations should be considered part of the supporting cast
and the way you consider typeface and font choices should reflect this
role. Typography in visualisation should be seen but really not heard.
Deciding on the most suitable type is something that can ultimately come
down to experience and influence through exposure to other work. Every
individual has their own relied-upon preferences. In practice, I find there is
a good chunk of trial and error as well as viewer testing that goes into
resolving the final selection. Across the spectrum of data visualisation
work being produced there are no significant trends to be informed by
largely because judging the most suitable typography choices will be
unique to the circumstances influencing each project.
Typography is just another of the many individual ingredients relevant to
data visualisation that exists as a significant subject in its own right. It is
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somewhat inadequate to allocate barely two pages of this book to
discussing its role in visualisation, but these will at least offer you a bite-
sized window into the topic.
‘Never choose Times New Roman or Arial, as those fonts are favored
only by the apathetic and sloppy. Not by typographers. Not by you.’
Matthew Butterick, Typographer, Lawyer and Writer
8.4 Influencing Factors and Considerations
Having become familiar with the principal options for annotating, you now
have to decide which features to incorporate into your work and how you
might deploy these.
Formulating Your Brief
‘Think of the reader – a specific reader, like a friend who’s curious but a
novice to the subject and to data-viz – when designing the graphic. That
helps. And I rely pretty heavily on that introductory text that runs with
each graphic – about 100 words, usually, that should give the new-to-
the-subject reader enough background to understand why this graphic is
worth engaging with, and sets them up to understand and contextualize
the takeaway. And annotate the graphic itself. If there’s a particular
point you want the reader to understand, make it! Explicitly! I often run
a few captions typeset right on the viz, with lines that connect them to
key elements in the design.’ Katie Peek, Data Visualisation Designer
and Science Journalist, on making complex and/or complicated
subject matter accessible and interesting to her audience
Audience: Given that most annotations serve the purpose of viewer
assistance, your approach will inevitably be influenced by the
characteristics of your intended audience. Having an appreciation of
and empathy towards the knowledge and capabilities of the different
cohorts of viewers is especially important with this layer of design.
How much help will they need to understand the project and also the
data being portrayed? You will need to consider the following:
Subject: how well acquainted will they be with this subject
matter? Will they understand the terminology, acronyms,
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abbreviations? Will they recognise the relevance of this
particular angle of analysis about this subject?
Interactive functions: how sophisticated are they likely to be in
terms of being able to understand and utilise the different
features of interactivity made possible through your design?
Perceiving: how well equipped are they to work with this
visualisation? Is it likely that the chart type(s) will be familiar or
unfamiliar; if the latter, will they need support to guide them
through the process of perceiving?
Interpreting: will they have the knowledge required to form
legitimate interpretations of this work? Will they know how to
understand what is good or bad, what big and small mean, what
is important, or not? Alternatively, will you need to provide
some level of assistance to address this potential gap?
Purpose map: The defined intentions for the tone and experience of
your work will influence the type and extent of annotation features
required.
If you are working towards a solution that leans more towards the
‘reading’ tone you are placing an emphasis on the perceptibility of the
data values. It therefore makes sense that you should aim to provide
as much assistance as possible (especially through extensive chart
annotations) to maximise the efficiency and precision of this process.
If it is more about a ‘feeling’ tone then you may be able to justify the
absence of the same annotations. Your intent may be to provide more
of a general sense – a ‘gist’ – of the order of magnitude of values.
If you are seeking to provide an ‘explanatory’ experience it would be
logical to employ as many devices as possible that will help inform
your viewers about how to read the charts (assisting with the
‘perceiving’ stage of understanding) and also bring some of the key
insights to the surface, making clear the meaning of the quantities and
relationships displayed (thus assisting with the stage of
‘interpreting’). The use of captions and visual overlays will be
particularly helpful in achieving this, as will the potential for audio
accompaniments if you are seeking to push the explanatory
experience a step further.
‘Exploratory’ experiences are less likely to include layers of insight
assistance, instead the focus will be more towards project-level
annotation, ensuring that viewers (and particularly here, users) have
as much understanding as possible about how to use the project for
their exploratory benefit. You might find, however, that devices like
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‘How to read this graphic’ are still relevant irrespective of the
definition of your intended experience.
Characteristically, ‘exhibitory’ work demonstrates far less annotated
assistance because, by intention, it is more about providing a visual
display of the data rather than offering an explanatory presentation or
the means for exploratory interrogation. The assumptions here are
that audiences will have sufficient domain and project knowledge not
to require extensive additional assistance. Common chart annotations
like value labels and legends, and project annotations like titles and
introductions, are still likely to be necessary, but these might reflect
the extent required.
Establishing Your Editorial Thinking
Focus: During your editorial thinking you considered focus and its
particular role in supporting explanatory thinking. Are there specific
value labels that you wish to display over others? Rather than
labelling all values, for example, have you determined that only
certain marks and attributes will merit labelling? As you saw earlier
in the example scatter plot about the under-representation of black
students in US colleges, only certain points were labelled, not all.
These would have been judged to have been the most relevant and
interesting elements to emphasise through annotation.
Trustworthy Design
Transparency: Annotation is one of the most important aids to
ensure that you secure and sustain trust from your viewers by
demonstrating integrity and openness:
Explain what the project is and is not going to show.
Detail where the data came from and what framing criteria were
used during the process of acquisition, and also make what has
been ultimately included in the chart(s).
Outline any data transformation treatments, assumptions and
calculations. Are there any limitations that viewers need to be
aware of?
Highlight and contextualise any findings to ensure accuracy in
interpretation.
With digital projects in particular, provide access to coding
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repositories to lay open all routines and programmatic solutions.
Accessible Design
Understandable: If you recall, in the section profiling circumstances
you considered what the characteristics were of the setting or
situation in which your audience might consume your visualisation.
Well-judged project and chart annotations are entirely concerned with
providing a sufficient level of assistance to achieve understanding.
The key word there is ‘sufficient’ because there is a balance: too
much assistance makes the annotations included feel overburdening;
too little and there is far more room for wrong assumptions and
misconceptions to prosper. A setting that is consistent with the need
to deliver immediate insights will need suitable annotations to fulfil
this. There will be no time or patience for long introductions or
explanations in that setting. Conversely, a visualisation about a
subject matter that is inherently complex may warrant such
assistance.
Elegant Design
Minimise the clutter: A key concern about annotations is judging the
merits of including structural or textual assistance against the
potential disruption and obstruction caused by these to the view of the
data. Any annotation device added to your display has a spatial and
visual consequence that needs to be accommodated. Of course, as
mentioned, with the benefit of interactivity it is possible to show and
hide layers of detail. Overall, you will have to find the most elegant
solution for presenting your annotations to ensure you do not
inadvertently undermine the help you are trying to provide.
Summary: Annotation
Project annotations help viewers understand what the project is about and
how to use it, and may include the following features:
Headings: titles, sub-titles and section headings.
Introductions: providing background and aims of the project.
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User guides: advice or instruction for how to use any interactive
features.
Multimedia: the potential to enhance your project using appropriate
imagery, videos or illustrations.
Footnotes: potentially includes data sources, credits, usage
information, and time/date stamps.
Chart annotations help viewers perceive the charts and optimise their
potential interpretations and may include the following features:
Chart apparatus: axis lines, gridlines, tick marks.
Labels: axis titles, axis labels, value labels.
Legend: providing detailed keys for colour or size associations.
Reading guides: detailed instructions advising readers how to
perceive and interpret the chart.
Captions: drawing out key findings and commentaries.
Typography Most of the annotation features you include are based on text
and so you will need to consider carefully the legibility of the typeface you
choose and the logic behind the font-size hierarchy you display.
Influencing Factors and Considerations
Formulating the brief: consider the characteristics and needs of the
audience. Certain chart choices and subjects may require more
explanation. From the ‘purpose map’ what type of tone and
experience are you trying to create and what role might annotation
play?
Establishing your editorial thinking: what things do you want to
emphasise or direct the eye towards (focus)?
Trustworthy design: maximise the information viewers have to ensure
all your data work is transparent and clearly explained.
Accessible design: what is the right amount and type of annotation
suitable to the setting and complexity of your subject?
Elegant design: minimise the clutter.
Tips and Tactics
Attention to detail is imperative: all instructions, project information,
captions and value labels need to be accurate. Always spell-check
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digitally and manually, and ask others to proofread if you are too
‘close’ to see.
Do not forget to check on permission to use any annotated asset, such
as imagery, photos, videos, quotations, etc.
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9 Colour
Having established which charts you will use, the potential interactive
functions that might be required and the annotation features that will be
especially useful, you have effectively determined all the visible elements
that will be included in your project. The final two layers of design
concern not what elements will be included or excluded, but how they will
appear. After this chapter you will look at issues on composition, but
before that the rather weighty matter of colour.
As one of the most powerful sensory cues, colour is a highly influential
visual property. It is arguably the design decision that has the most
immediate impact on the eye of the viewer. All the design features of your
visualisation display hold some attribute of colour, otherwise they are
invisible:
Every mark and item of apparatus in your charts will be coloured;
indeed colour in itself may be an attribute that represents your data
values.
Interactive features do not always have an associated visible property
(some are indeed invisible and left as intuitively discoverable).
However, those features that involve buttons, menus, navigation tabs
and value sliders will always have a colour.
Annotation properties such as titles, captions and value labels will all
be coloured.
Composition design mainly concerns the arrangement of all the above
features, though you might use colour to help achieve a certain design
layout. As you will see, emptiness is a useful organising device –
leaving something blank is a colour choice.
Thankfully, there is a route through all of this potential complexity relying
on just a little bit of science mixed in with lots of common sense. By
replacing any arbitrary judgements that might have been previously based
on taste, and through increasing the sensitivity of your choices, colour
becomes one of the layers of visualisation design that can be most quickly
and significantly improved.
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‘Colors are perhaps the visual property that people most often misuse in
visualization without being aware of it.’ Robert Kosara, Senior
Research Scientist at Tableau Software
The key factor in thinking about colour is to ensure you establish meaning
first and decoration last. That is not to rule out the value of certain
decorative benefits of colour, but to advise that these should be your last
concern. Besides, in dealing with meaningful applications of colour you
will already have gone a long way towards establishing the ‘decorative’
qualities of your project’s aesthetic appearance.
This chapter begins with a look at some of the key components of colour
science, offering a foundation for your understanding about this topic.
After that you will learn about the ways and places in which colour could
be used. Finally, you will consider the main factors that influence colour
decisions.
COLOUR thinking begins from inside the chart(s), working outwards
across the rest of the visualisation anatomy:
Data legibility.
Editorial salience.
Functional harmony.
9.1 Overview of Colour Theory
COLOUR in visualisation is something of a minefield. As with many of
these design layer chapters, an introduction to colour involves judging the
right amount of science and the right amount of practical application. What
does justice to the essence of the subject and gives you the most relevant
content to work with is a delicate balance.
When you lift the lid on the science behind colour you open up a world of
brain-ache. When this chapter is finalised I will have spent a great deal of
time agonising over how to explain this subject and what to leave in or
leave out because there is so much going on with colour. And it is tricky.
Why? Because you almost come face to face with philosophical questions
like ‘what is white?’ and the sort of mathematical formulae that you really
rather hoped had been left behind at school. You learn how the colours you
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specify in your designs as X might be perceived by some people as Y and
others as Z. You discover that you are not just selecting colours from a
neat linear palette but rather from a multi-dimensioned colour space
occupying a cubic, cylindrical or spherical conceptual shape, depending on
different definitions.
The basis of this topic is the science of optics – the branch of physics
concerned with the behaviour and properties of light – as well as
colorimetry – the science and technology used to quantify and describe
human colour perception. Two sciences, lots of maths, loads of variables,
endless potential for optical illusions and impairment: that is why colour is
tricky and why you need to begin this stage of thinking with an
appreciation of some colour theory.
The most relevant starting point is to recognise that when dealing with
issues of colour in data visualisation you will almost always be creating
work on some kind of computer. Unless you are creating something by
hand using paints or colouring pencils, you will be using software viewed
through an electronic display.
This is important because a discussion about colour theory needs to be
framed around the RGB (Red, Blue, Green) colour model. This is used to
define the combination of light that forms the colours you see on a screen,
conceptually laid out in a cubic space based on variations across these
three attributes.
The output format of your work will vary between screen display and print
display. If you are creating something for print you will have to shift your
colour output settings to CMYK (Cyan, Magenta, Yellow and Black). This
is the model used to define the proportions of inks that make up a printed
colour. This is known as a subtractive model, which means that combining
all four inks produces black, whereas RGB is additive as the three screen
colours combine to produce white.
When you are creating work to be consumed on the Web through screen
displays, you will often program using HEX (Hexadecimal) codes to
specify the mix of red, green and blue light (in the form #RRGGBB
using codes 00 to FF).
While CMYK communicates from your software to a printer, telling it
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what colours to print as an output, it does not really offer a logical model
to think about the input decisions you will make about colour. Neither, for
that matter, does RGB: it just is not realistic to think in those terms when
considering what choices are needed in a visualisation design. There are
different levers to adjust and different effects being sought that require an
alternative model of thinking.
Figure 9.1 HSL Colour Cylinder
Figure 9.2 Colour Hue Spectrum
I share the belief with many in the field that the most accessible colour
model – in terms of considering the application of colour in data
visualisation – is HSL (Hue, Saturation, Lightness), devised by Albert
Munsell in the 1980s. These three dimensions combine to make up what is
known as a cylindrical-coordinate colour representation of the RGB colour
model (I did warn you about the cylinders).
Hue is considered the true colour. With hue there are no shades
(adding black), tints (adding whites) or tones (adding grey) – a
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consideration of these attributes follows next. When you are
describing or labelling colours you are most commonly referring to
their hue: think of the colours of the rainbow ranging through various
mixtures of red, orange, yellow, green, blue, indigo and violet. Hue is
considered a qualitative colour attribute because it is defined by
difference and not by scale.
Saturation defines the purity or colourfulness of a hue. This does
have a scale from intense pure colour (high saturation) through
increasing tones (adding grey) to the no-colour state of grey (low
saturation). In language terms think vivid through to muted.
Figure 9.3 Colour Saturation Spectrum
Lightness defines the contrast of a single hue from dark to light. It is
not a measure of brightness – there are other models that define that –
rather a scale of light tints (adding white) through to dark shades
(adding black). In language terms I actually think of lightness more as
degrees of darkness, but that is just a personal mindset.
Figure 9.4 Colour Lightness Spectrum
Technically speaking, black, white and grey are not considered colours.
I have deliberately described these dimensions separately because, as you
will see when looking at the applications of colour in visualisation, your
decisions will often be defined by how you might employ these distinct
dimensions of colour to form your visual display. The main choices tend to
fall between employing difference in hue and variation in lightness, with
the different levels of saturation often being a by-product of the definitions
made for the other two dimensions.
Alternative models exist offering variations on a similar theme, such as
HSV (Hue, Saturation, Value), HSI (Hue, Saturation, Intensity), HSB
(Hue, Saturation, Brightness) and HCL (Hue, Chroma, Luminance).
These are all primarily representations of the RGB model space but
involve differences in the mathematical translation into/from RGB and
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offer subtle differences in the meaning of the same terms (local definitions
of hue and saturation vary). The biggest difference relates to their
emphasis as a means of specifying either a colour quality (in an input,
created sense) or a colour perception (in how a colour is ultimately
experienced).
Pantone is another colour space that you might recognise. It offers a
proprietary colour-matching, identifying and communicating service for
print, essentially giving ‘names’ to colours based on the CMYK
process.
The argument against using the HSL model for defining colour is that,
while it is fine for colour setting (i.e. an intuitive way to think about and
specify the colours you want to set in your visualisation work), the
resulting colours will not be uniformly perceived the same, from one
device to the next. This is because there are many variables at play in the
projection of light to display colour and the light conditions present in the
moment of perception. That means the same perceptual experience will not
be guaranteed. It is argued that more rigorous models (such as CIELAB)
offer an absolute (as opposed to a relative) definition of colour for both
input and output. My view is that they are just a little bit too hard to easily
translate into visualisation design thinking. Furthermore, trying to control
for all the subtleties of variation in consumption conditions is an extra
burden you should ideally avoid.
At this stage, it is important to be pragmatic about colour as much as
possible. The vast majority of your colour manipulating and perceptual
needs should be nicely covered by the HSL model. As and when you
develop a deeper, purist interest in colour you should then seek to learn
more about the nuances in the differences between the definitions of these
models and their application.
9.2 Features of Colour: Data Legibility
Data legibility concerns the use of the attribute of colour to encode data
values in charts. The objective here is to make the data being represented
by differences in colour as clearly readable and as meaningful as possible.
While you have probably already decided by now the chart or charts you
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intend to use, you still need to take think carefully – and separately – about
how you will specifically employ colour. To do this we first need to revisit
the classification of data types and consider how best to use colour for
representing each different type.
Nominal (Qualitative)
With nominal data colour is used to classify different categorical values.
The primary motive for the choice of colour is to create a visible
distinction between each unique categorical association, helping the eye to
discern the different categories as efficiently and accurately as possible.
Creating contrast is the main aim of representing nominal data. What you
are not seeking to show or even imply is any sense of an order of
magnitude. You want to help differentiate one category from the next –
and make it easily identifiable – but to do so in a way that preserves the
sense of equity among the colours deployed.
Figure 9.5 Excerpt from ‘Executive Pay by the Numbers’
Variation in hue is typically the colour dimension to consider using for
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differentiating categories. Additionally, you might explore different tones
(variations in saturation across the hues). You should not, though, consider
using variations in the lightness dimension. That is because the result is
insufficiently discernible. As you can see demonstrated in Figure 9.5, the
lightness variation of a blue tone makes it quite hard to connect the colour
scale presented in the key at the top with the colours displayed in the
stacked bars underneath. With the shading in the column header and the
2011 grey bar also contributing similar tones to the overall aesthetic of the
table our visual processing system has to work much harder to determine
the associations than it should need to do.
Often the categories you will be differentiating with colour will be
relatively few in number, maybe two or three, such as in the separation
between political parties or plotting different values for gender, as seen in
Figure 9.6.
Figure 9.6 How Nations Fare in PhDs by Sex
Figure 9.7 How Long Will We Live – And How Well?
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Beyond these small numbers, you still typically might only need to
contend with assigning colours to around four to six categories, perhaps in
analysis that needs to visually distinguish values for different continents of
the world, as seen in the scatter plot in Figure 9.7.
As the range of different categories grows, the ability to preserve clear
differentiation becomes harder. In expanding your required palette, the
colours used become decreasingly unique. The general rule of thumb is
that once you have more than 12 categories it will not be possible to find a
sufficiently different colour to assign to categories from 13 upwards.
Additionally, you are really increasing the demands of learning and
recognition for viewers. This then becomes quite a cognitive burden and
delays the process of understanding.
Figure 9.8 Charting the Beatles: Song Structure
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Two approaches for dealing with this. Firstly, consider offering interactive
filters to modify what categories are displayed in a visualisation – thus
potentially reducing the impact of so many being available. Secondly,
think about transforming your data by excluding or combining categories
in to a reduced number of aggregate groupings.
Depending on the subject of your data, sometimes you can look to
supplement the use of colour with texture or pattern to create further
visible distinctions. In Figure 9.8 you can see two patterns being used
occasionally as additive properties to show the structure of tracks on The
Beatles’ album.
Ordinal (Qualitative)
With ordinal data you are still dealing with categories but now they have a
natural hierarchy or ordering that can be exploited. The primary motive for
using colour in this case is not only to create a visible distinction between
each unique category association but also to imply some sense of an order
of magnitude through the colour variation. The colour dimensions used to
achieve this tend to employ variations of either the saturation or the
lightness (or a combination of both). You might also introduce different
hues when dealing with diverging (dual-direction) scales rather than
simply converging (single-direction) ones.
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Figure 9.9 displays a simple example of colour used to display a
converging ordinal variable. This is the teacup that I use in my office. On
the inside you can see it has a colour guide to help ascertain how much
milk you might need to add: going through Milky, Classic British,
Builder’s Brew, and finally Just Tea (zero milk).
Figure 9.9 Photograph of MyCuppa Mug
A typical example of a diverging ordinal scale might be seen in the stacked
bar chart showing the results of a survey question (Figure 9.10). The
answers are based on the strength of feeling: strongly agree, agree, neutral,
disagree, strongly disagree. By colouring the agreement in red (‘hot’
sometimes used to represent ‘good’) and the disagreement in blue (‘cold’
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to mean ‘bad’) means a viewer can quickly perceive the general balance of
feelings being expressed.
Figure 9.10 Example of a Stacked Bar Chart Based on Ordinal Data
Another example of ordinal data might be to represent the notion of
recency. In Figure 9.11 you see a display plotting the 2013 Yosemite
National Park fire. Colour is used to display the recorded day-by-day
progress of the fire’s spread. The colour scale is based on a recency scale
with darker = recent, lighter = furthest away (think faded memory).
Figure 9.11 The Extent of Fire in the Sierra Nevada Range and Yosemite
National Park, 2013
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Interval and Ratio (Quantitative)
With quantitative data (ratio and interval) your motive, as it is with ordinal
data, is to demonstrate the difference between and of a set of values. In the
choropleth map in Figure 9.12, showing the variation in electricity prices
across Switzerland, the darker shades of blue indicate the higher values,
the lighter tints the lower prices. This approach makes the viewer’s
perception of the map’s values immediate – it is quite intuitive to
recognise the implication of the general patterns of light and dark shades.
Figure 9.12 What are the Current Electricity Prices in Switzerland
[Translated]
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Typically, using colour to represent quantitative data will involve breaking
up your data values into discrete classifications or ‘bins’. This makes the
task of reading value ranges from their associated colour shade or tone a
little easier than when using a continuous gradient scale. While our
capacity to judge exact variations in colour is relatively low (even with a
colour key for reference), we are very capable of detecting local variations
of colour through differences in tint, shade or tone. Assessing the relative
contrast between two colours is generally how we construct a quantitative
hierarchy.
Look at the fascinating local patterns that emerge in the next map (Figure
9.13), comparing increases in the percentage of people gaining health
insurance in the USA (during 2013–14). The data is broken down to
county level detail with a colour scale showing a darker red for the higher
percentage increases.
Some of the most relevant colour practices for data visualisation come
from the field of cartography (as do many of the most passionate colour
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purists). Just consider the amount of quantitative and categorical detail
shown in a reference map that relies on colour to differentiate types of
land, indicate the depth of water or the altitude of high ground, present
route features of road and rail networks, etc. The best maps pack an
incredible amount of detail into a single display and yet somehow they
never feel disproportionately overwhelming.
Figure 9.13 Excerpt from ‘Obama’s Health Law: Who Was Helped Most’
Aside from the big-picture observations of the darker shades in the west
and the noticeably lighter tints to the east and parts of the mid-west, take a
closer look at some of the interesting differences at a more local level. For
example, notice the stark contrast across state lines between the dark
regions of southern Kentucky (to the left of the annotated caption) and the
light regions in the neighbouring counties of northern Tennessee. Despite
their spatial proximity there are clearly strong differences in enrolment on
the programme amongst residents of these regions.
Both of these previous examples use a convergent colour scale, moving
through discrete variations in colour lightness to represent an increasing
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scale of quantitative values, from zero or small through to large. As
illustrated with the stacked bar chart example shown earlier, portraying the
range of feelings from an ordinal dataset, sometimes you may need to
employ a divergent colour scale. This is when you want to show how
values are changing in two directions either side of a set breakpoint.
Figure 9.14 Daily Indego Bike Share Station Usage
Figure 9.14 shows a cropped view of a larger graphic comparing the
relative peaks and troughs of usage across all bike share stations in
Philadelphia over a 24-hour period. The divergent colour scale uses two
hues and variations in lightness to show the increasingly busy and
increasingly slow periods of station activity either side of a breakpoint,
represented by a very light grey to indicate the average point. The darkest
red means the station is full, the darkest blue means the station is empty.
Regardless of whether you are plotting a converging or diverging scale,
judging how you might divide up your colour scales into discrete value
bins needs careful thought. The most effective colour scales help viewers
perceive not just the relative order of magnitude – higher or lower – but
also a sense of the absolute magnitude – how different a value might be
compared to another value.
There is no universal rule about the number of value bins. Indeed, it is not
uncommon to see entirely continuous colour scales. However, a general
rule of thumb I use is that somewhere between between four and nine
meaningful – and readable – value intervals should suffice. There are two
key factors to consider when judging your scales:
Are you plotting observed data or observable data? You might only
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have collected data for a narrow range of quantities (e.g. 15 to 35) so
will your colour classifications be based on this observed range or on
the potentially observable data range i.e. the values you know
would/could exist with a wider sample size or on a different
collection occasion (e.g. 0 to 50)?
What are the range and distribution of your data? Does it make sense
to create equal intervals in your colour classifications or are there
more meaningful intervals that better reflect the shape of your data
and the nature of your subject? Sometimes, you will have legitimate
outliers that, if included, will stretch your colour scales far beyond the
meaningful concentration of most of your data values.
Figure 9.15 Battling Infectious Diseases in the 20th Century: The Impact
of Vaccines
You can see this effect in Figure 9.15, showing the incidence of Hepatitis
A per 100,000 population. There are only three values that exceed 100
(you can see them on the top line for Alaska in the late 1970s). To
accommodate these outliers the colour scale becomes somewhat stretched-
out, with a wide range of potential values being represented by a dark
yellow to red colour. With 99.9% of the values being under 100 there is
little discernibly in the blue/green shades used for the lower values. If
outliers are your focus, it makes sense to include these and colour
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accordingly to emphasise their exceptional quality. Otherwise if they risk
compromising the discrete detail of the lower values you might look to
create a broad classification that uses a single colour for any value beyond
a threshold of maybe 75, with even value intervals of maybe 15 below that
help to show the patterns of smaller values.
For diverging scales, the respective quantitative shades either side of a
breakpoint need to imply parity in both directions. For example, a shade of
colour that means +10% one side of the breakpoint should have an equal
shade intensity in a different hue on the other side to indicate the same
interval, i.e. −10%. Additionally, the darkest shades of hues at the extreme
ends of a diverging scale must still be discernible. Sometimes the darkest
shades will be so close to black that you will no longer be able to
distinguish the differences in their underlying hues when plotted in a chart
or map.
As well as considering the most appropriate discrete bins for your values,
for diverging scales one must also pay careful attention to the role of the
breakpoint. This is commonly set to separate values visually above or
below zero or those either side of a meaningful threshold, such as target,
average or median.
One of the most common mistakes in using colour to represent quantitative
data comes with use of the much-derided rainbow scale. Look at Figure
9.16, showing the highest temperatures across Australia during the first
couple of weeks in 2013. Consider the colour key to the right of the map
and ask yourself if this feels like a sufficiently intuitive scale. If the key
was not provided, would you be able to perceive the order of magnitude
relationship between the colours on the map? If you saw a purple colour
next to a blue colour, which would you expect to mean hotter and which
colder?
Figure 9.16 Highest Max Temperatures in Australia
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While the general implication of blue = ‘colder’ through to red = ‘hotter’ is
included within sections of this temperature colour scale, it is the presence
of many other hues that obstructs the accessibility and creates
inconsistency in logic. For instance, do the colours used to show 24°C
(light blue) jumping to 26°C (dark green) make sense as a means for
showing an increasing temperature? How about 18°C (grey) to 20°C (dark
blue), or the choice of the mid-brown used for 46°C which interrupts the
increasingly dark red sequence? If you saw on the map a region with the
pink tone as used for 16°C would you be confident that you could easily
distinguish this from the lighter pink used to represent 38°C? Unless there
are meaningful thresholds within your quantitative data – justifiable
breakpoints – you should only vary your colour scales through the
lightness dimension, not the hue dimension.
One of the interesting recurring challenges faced by visualisers is how to
represent nothing. For example, if a zero quantity or no category is a
meaningful state to show, you still need to represent this visually
somehow, even though it might possess no size, no position and no area.
How do you distinguish between no data and a zero value?
Figure 9.17 State of the Polar Bear
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Typically, using colour is one of the best ways to portray this. Figure 9.17
shows one solution to making ‘no data’ a visible value. This map displays
the population trends of the polar bear. Notice those significant areas of
grey representing ‘data deficient’. A subtle but quite effective political
point is being made here by including this status indicator. As I mentioned
before, sometimes the absence of data can be the message itself.
Figure 9.18 Excerpt from ‘Geography of a Recession’
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When considering colour choices for quantitative classifications, you will
need to think especially carefully about the lowest value grouping: is it to
be representative of zero, an interval starting from zero up to a low value,
or an interval starting only from the minimum value and never including
zero? In this choropleth map (Figure 9.18) looking at the unemployment
rate across the counties of the USA, no value is as low as zero. There
might be value that are close, but nowhere is the unemployment rate at
0%. As you can see, the lowest tint used in this colour key is not white,
rather a light shade of orange, so as not to imply zero. Whilst not relevant
to this example, if you wanted to create a further distinction between the
lowest value interval and the ‘null’ or ‘no data’ state you could achieve
this by using a pure white/blank.
9.3 Features of Colour: Editorial Salience
Having considered options for the application of colour in facilitating data
legibility, the next concern is colour used for editorial salience. Whereas
data legibility was concerned with helping to represent data, using colour
for editorial salience is about drawing the viewer’s attention to the
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significant or meaningful features of your display. Colour offers such a
potent visual stimulus and an influential means for drawing out key
aspects of your data and project that you might feel are sufficiently
relevant to make prominent.
Consider again the idea of photography and the effect of taking a
photograph of a landscape. You will find the foreground objects are darker
and more prominent than the faded view of the background in the distance
as light and colour diminish. Using colour to achieve editorial salience
involves creating a similar effect of depth across your visualisation’s
contents: if everything is shouting, nothing is heard.
The goal of using colour to facilitate editorial salience is a suitable
contrast. For things to stand out, you are in turn determining which other
things will not.
The degree of contrast you might seek to create will vary. Often you will
be seeking to draw a significant contrast, maximising the emphasis of a
value or subset of values so the viewer can quickly home in on what you
have elevated for their attention relative to everything else.
For this reason, grey will prove to be one of your strongest allies in data
visualisation. When contrasted with reasonably saturated hues, grey helps
to create depth. Elements coloured in greyscale will sit quietly at the back
of the view, helping to provide a deliberately subdued context that enables
the more emphasised coloured properties to stand proudly in the
foreground.
In Figure 9.19, the angle of analysis shows a summary of the most
prevalent men’s names featuring among the CEOs of the S&P 1500
companies. As you can see there are more guys named ‘John’ or ‘David’
than the percentage of all the women CEOs combined. With the emphasis
of the analysis on this startling statement of inequality the bar for ‘All
women’ is emphasised in a burgundy colour, contrasting with the grey bars
of all the men’s names. Notice also that the respective axis and bar value
labels are both presented using a bold font, which further accentuates this
emphasis. It is also editorially consistent with the overriding enquiry of the
article. As discussed in Chapter 3, bringing to the surface key insights
from data displays in this way contributes towards facilitating an
‘explanatory’ experience.
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Figure 9.19 Fewer Women Run Big Companies Than Men Named John
Figure 9.20 NYPD, Council Spar Over More Officers
Sometimes, only noticeable contrast – not shouting, just being slightly
more distinguishable – may be appropriate. Compared with the previous
bar chart example, Figure 9.20 creates a more subtle distinction between
the slightly darker shade of green (and emboldened text) emphasising the
New York figures compared to the other listed departments in a slightly
lighter green. As with the CEOs’ example, the object of our attention is the
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subject of focus in the analysis, in this case regarding a drive for more
NYPD officers. This does not need to be any more contrasting; it is just as
sufficiently noticeable as the visualiser wishes it to be.
Sometime you will seek to create several levels of visual ‘urgency’ in the
relative contrast of your display. The colour choices in Figure 9.21 gives
foreground prominence to the yellow coloured markers and values (the
dots are also larger) and then mid-ground/secondary prominence to the
slightly muted red markers. In perceiving the values of the yellow markers,
the viewer is encouraged to concentrate on primarily comparing these with
the red markers. The subtle grey markers are far less visible – closer in
shade to the background than the foreground – and deliberately relegated
to a tertiary level so they do not clutter up the display and cause
unwarranted attention. They provide further context for the distribution of
the values but do not need to be any more prominent in their relationship
with the foreground and mid-ground colours.
Figure 9.21 Excerpt from a Football Player Dashboard
I touched on the use of encoded overlays earlier where coloured areas or
bandings can be used to help separate different regions of a display in
order to facilitate faster interpretation of the meaning of values. In the
bubble plot in Figure 9.22, you can see the circle markers are colour coded
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to help viewers quickly ascertain the significance of each location on the
chart according to the quadrants in which they fall. Notice how in the
background the diagonal shading further emphasises the distinction
between above the line ‘improvement’ and below the line ‘worsening’, a
very effective approach.
Figure 9.22 Elections Performance Index
9.4 Features of Colour: Functional Harmony
After achieving data legibility and editorial salience through astute colour
choices, functional harmony is concerned with ensuring that any remaining
colour choices will aid, and not hinder, the functional effectiveness and
elegance of the overall visualisation.
‘When something is not harmonious, it’s either boring or chaotic. At
one extreme is a visual experience that is so bland that the viewer is not
engaged. The human brain will reject under-stimulating information. At
the other extreme is a visual experience that is so overdone, so chaotic,
that the viewer can’t stand to look at it. The human brain rejects what it
can not organise, what it cannot understand.’ Jill Morton, Colour
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Expert and Researcher
You must judge the overall balance of and suitability of your collective
colour choices and not just see these as isolated selections. This is again
primarily a judgement about contrast – what needs to be prominent and
what needs to be less so. Such an apparent calming quality about a well-
judged and cohesive colour palette is demonstrated by Stefanie Posavec’s
choices in visualising the structure of Walter Benjamin’s essay ‘Art in the
age of mechanical reproduction’ (Figure 9.23). There is effortless harmony
here between the colour choices extending across the entire anatomy of
design: the petals, branches, labels, titles, legend, and background.
A reminder that any and every design feature you incorporate into your
display will have a property of colour otherwise they will be invisible. In
looking at data legibility and editorial salience you have considered your
colour choices for representing data. A desire to achieve functional
harmony means considering further colour decisions that will help
establish visual relationships across and between the rest of your
visualisation’s anatomy: its interactive features, annotations and
composition.
Figure 9.23 Art in the Age of Mechanical Reproduction: Walter Benjamin
Interactive features: Visible interactive features will include
controls such as dropdown menus, navigation buttons, time sliders
and parameter selectors. The colour of every control used will need to
be harmonious with the rest of the project but also, critically, must be
functionally clear. How you use colour to help the user discern what
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is selected and what is not will need to be carefully judged.
To illustrate this, Figure 9.24 shows an interactive project that
examines the connected stories of the casualties and fatalities from
the Iraqi and Afghan conflicts. Here you can see that there are several
interactive features, all of which are astutely coloured in a way that
feels both consistent with the overall tone of the project but also
makes it functionality evident what each control’s selected status or
defined setting is. This is achieved through very subtle but effective
combinations of dark and light greys that help create intuitive clarity
about which values the user has selected or highlighted. When a
button has a toggle setting (on/off, something/something else), such
as the ‘Afghanistan’ or ‘Iraq’ tabs at the top, the selected tab is
highlighted in bright grey and the unselected tab in a more subdued
grey. Filters can either frame (include/exclude) or focus
(highlight/relegate) the data. The same approach to using brighter
greys for the selected parameter values makes it very clear what you
have chosen, but also what you have excluded (while making evident
the other currently-unselected values from which you can potentially
choose).
Figure 9.24 Casualties
Annotations: Chart annotations such as gridlines, axis lines and
value labels all need colouring in a way that will be sympathetic to
the colour choices already made for the data representation and,
possibly, editorial contrasting. As mentioned in the last chapter, many
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annotation devices exist in the form of text and so the relative font
colour choices will need to be carefully considered. For any
annotation device the key guiding decision is to find the level at
which these are suitably prominent. Not loud, not hidden, just at the
right level. This will generally take a fair amount of trial and error to
get right but once again, depending on your context, your first
thought should be to consider the merits offered by different shades
of grey.
You might be starting to suspect I’m a lobbyist for the colour grey.
Nobody wants to live in a world of only grey. The point is more about how
its presence enables other colours to come alive. The great Bill Shankly
once said ‘Football is like a piano, you need 8 men to carry it and 3 who
can play the damn thing’. In data visualisation, grey does the heavy lifting
so the more vibrant colours can bring the energy and vibrancy to your
design.
Figure 9.25 First Fatal Accident in Spain on a High-speed Line
[Translated]
Another example of the role of greyscale is demonstrated by Figure 9.25,
illustrating key aspects of the tragic rail crash in Spain in 2013. The sense
of foreground and background is clearly achieved by the prominence of
the scarlet-coloured annotations and visual cues offset against the
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backdrop of an otherwise greyscale palette.
Figure 9.26 Lunge Feeding
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There are other features of annotation that will have an impact on
functional harmony through their colouring. Multimedia assets like
photos, embedded videos, images and illustrations need to be
consistent in tone according to their relative role on the display. If
they are to dominate the page then unleash the vibrancy of their
colours to achieve this; if they are playing more of a secondary or
supporting role then relegate their constituent colours to allow other
primary features due prominence.
Figure 9.26 includes small illustrations of a whale, showing how it
goes through the stages of lunge feeding. The elegance of the colours
used in these illustrations is entirely harmonious with the look and
feel of the overall piece. They are entirely at one with the rest of the
graphic.
Composition: The clarity in layout of a project will often be achieved
by the use of background colour to create logical organisation. In the
‘Lunge Feeding’ graphic the shading of the blue sea getting darker as
it moves down is not attempting to offer a precise representation of
the sea, but it gives a sense of depth and draws maximum attention to
that panel. It is also naturally congruent with the subject matter.
Figure 9.27 Examples of Common Background Colour Tones
In general, there are no fixed rules on the benefits of any particular
colour for background shading. Your choices will depend mostly on
the circumstances and conditions in which your viewers are
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consuming the work. Usually, when there is no associated congruence
for a certain background colour, your options will tend to come from
one of the selection of neutral and/or non-colours (Figure 9.27). This
is because they particularly help to aid accentuation in combination
with foreground colours.
Typically, though, a white background (at least for your chart area)
gives viewers the best chance of being able to accurately perceive the
different colour attributes used in your data representation and the
contrasting nature of your editorial contrast.
White – or more specifically emptiness – is one of your most
important options for creating functional meaning for nothingness,
something I touched on earlier. The emptiness of uncoloured space
can be used very effectively to direct the eye’s attention. It organises
the relationship between space on a page without the need for visible
apparatus, as seen in the left hand column of the lunge feeding
graphic. It can also be used to represent or emphasise values that
might have the state of ‘null’ or ‘zero’ to maximise contrast.
‘The single most overlooked element in visual design is emptiness.
Space must look deliberately used.’ Alex White, Author, The
Elements of Graphic Design
9.5 Influencing Factors and Considerations
Having mapped out the ways and places where colour could be used, you
will now need to consider the factors that will influence your decisions
about how colour should be used.
Formulating Your Brief
Format: This is a simple concern but always worth pointing out: if
you are producing something for screen display you will need to set
your colour output to RGB; if it is for print you will need CMYK.
Additionally, when you are preparing work for print, running off
plenty of proofs before finalising a design is imperative. What you are
preparing digitally is a step away from the form of its intended
output. What looks like a perfect colour palette on screen may not
ultimately look the same when printed.
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Print quality and consistency is also a factor. Graphics editors who
create work for print newspapers or magazines will often consider
using colours as close in tone as possible to pure CMYK, especially if
their work is quite intricate in detail. This is because the colour plates
used in printing presses will not always be 100% aligned and thus
mixtures of colours may be slightly compromised.
As black and white printing is still commonplace, you need to be
aware of how your work might look if printed without colour. If you
are creating a visualisation that might possibly be printed by certain
users in black and white, the only colour property that you can
feasibly utilise will be the lightness dimension. Sometimes, as a
designer or author, you will be unaware of this intent and the
colourful design that you worked carefully towards will end up not
being remotely readable.
We all refer to black and white printing, but technically printers do not
actually print using white ink, it is just less black or no black.
Furthermore, there is an important difference in how colours appear when
published in colour and how they appear when published in black and
white. Hues inherently possess different levels of brightness: the purest
blue is darker than the purest yellow. If these were printed in black and
white, blue would therefore appear a darker, more prominent shade of
grey. If your printed work will need to be compatible for both colour and
black and white output, before finalising your decisions check that the
legibility and intended meaning of your colour choices are being
maintained across both forms.
Setting: For digital displays, the conditions in which the work will be
consumed will have some influence over the choice between light and
dark backgrounds. The main factor is the relative contrast and the
stresses this can place on the eye to adjust against the surroundings. If
your work is intended for consumption in a light environment, lighter
backgrounds tend to be more fitting; likewise darker backgrounds
will work best for consuming in darker settings. For
tablets/smartphones, the bordering colour of the devices can also
influence the most suitable choice of background tone to most
sympathetically contrast with the surroundings.
Colour rules and identities: In some organisations there are style
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guidelines or branding identities that require the strict use of only
certain colour options. Similar guidelines may exist if you are
creating work for publication in a journal, magazine, or on certain
websites. Guidelines like these are well intended, driven by a desire
to create conformity and consistency in style and appearance.
However, in my experience, the basis of such colour guides rarely
incorporates consideration for the subtleties of data visualisation. This
means that the resulting palettes are often a bad fit for ideal
visualisation colour needs, providing limited scope for the variation
and salience you might seek to portray.
Your first task should always be to find out if there is any
compromise – any chance of not having these colour restrictions
imposed. If there is no flexibility, then you will just have to accept
this and begin acquainting yourself with the colours you do have to
work with. Taking a more positive view, achieving consistency in the
use of colour for visualisation within an organisation does have merits
if the defined palettes offer suitably rich variety. Developing a
recognisable ‘brand’ and not having to think from scratch about what
colours to use every time you face a new project is something that can
be very helpful, especially across a team.
Purpose map: Does it need to be utilitarian or decorative? Should it
be functional or appealingly seductive? Does it lend itself to being
vivid and varied in colour or more muted and distinguished? Colour
is the first thing we notice as viewers when looking at a visualisation,
so your choices will play a huge part in setting the visible tone of
voice. How you define your thinking across the vertical dimension of
your purpose map will therefore have an influence on your colour
thinking.
Along the horizontal dimension, the main influencing consideration
will be a desire to offer an ‘explanatory’ experience. As mentioned,
some of the tactics for incorporating editorial salience will be of
specific value if you are seeking to emphasise immediately apparent,
curated insights.
Ideas and inspiration: In the process of sketching out your ideas and
capturing thoughts about possible sources of influence, maybe there
were already certain colours you had identified as being consistent
with your thinking about this subject? Additionally, you might have
already identified some colours you wish to avoid using.
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Working With Data
Data examination: The characteristics of your data will naturally
have a huge impact, on the decisions you make around data legibility.
Firstly, the type of data you are displaying (primarily nominal vs all
other types) will require a different colour treatment, as explained.
Secondly, the range of categorical colour associations (limits on
discernible hues) and the range and distribution of quantitative values
(numbers of divisions and definition of the intervals across your
classification scale) will be directly shaped by the work you did in the
examination stage.
In Figure 9.28 you can see a census of the prevalence and species of
trees found around the boroughs of New York City. This initial big-
picture view creates a beautiful tapestry made up of tree populations
across the region (notice the big void where JFK Airport is located).
Figure 9.28 Excerpt from ‘NYC Street Trees by Species’
To observe patterns for individual tree types is harder: with 52
different tree species there are simply too many classifications to be
able to allocate sufficiently unique colours to each. To overcome this,
the project features a useful pop-up filter list which then allows you to
adjust the data on view to reveal the species you wish to explore.
It is often the case when thinking about colour classifications that you
may need to revisit the data transformation actions to find new ways
of grouping your data to create better-fit quantitative value
classifications or to look at ways of grouping your categories. For the
latter, actions such as combining less important categories in an
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‘other’ bin to reduce the variability or eliminating certain values from
your analysis may be necessary.
‘If using colour to identify certain data, be careful to not accidentally
apply the same identity to a nearby part of the graphic. Don’t allow
colour to confuse just for the sake of aesthetics. I also like to use colour
to highlight. A single colour highlight on a palette of muted colours can
be a strong way to draw attention to key information.’ Simon Scarr,
Deputy Head of Graphics, ThomsonReuters
Establishing Your Editorial Thinking
Focus: When considering the perspective of ‘focus’ in the editorial
thinking stage, you were defining which, if any, elements of content
would merit being emphasised. Are there features of your analysis
that you might wish to accentuate? How might colour be used to
accentuate key insights in the foreground and push other (less
important) features into the background? What are the characteristics
of your data that you might want to emphasise through changes in
colour? For example, are there certain threshold values that will need
to be visually amplified if exceeded? Your decisions here will directly
influence your thinking about using colour to facilitate editorial
salience.
Data Representation
Chart type choice: Specifically in relation to data legibility,
depending on which chart type you selected to portray your data, this
may have attributes requiring decisions about colour. The heat map
and choropleth map are just two examples that use variation in colour
to encode quantitative value. Almost every chart has the potential to
use colour for categorical differentiation.
Trustworthy Design
Data classification: The decisions you make about how to encode
data through colour have a great bearing on the legibility and
accuracy of your design, especially with quantitative data. You will
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need to ensure the classifications present a true reflection of the shape
and characteristics of your data and do not suppress any significant
interpretations.
‘Start with black and white, and only introduce color when it has
relevant meaning. In general, use color very sparingly.’ Nigel Holmes,
Explanation Graphic Designer
Meaningful: Eliminating arbitrary decisions is not just about
increasing the sophistication of your design thinking, it is also an
essential part of delivering a trustworthy design. If something looks
visually significant in its data or editorial colouring it will be read as
such, so make sure it is significant, otherwise remove it. You
especially want to avoid any connotation of significant meaning
across your functional or decorative colour choices. This will be
confusing at best, or will appear deceptive at worst.
Do not try to make something look more interesting than it
fundamentally is. Colour should not be used to decorate data. You
might temporarily boost the apparent appeal of your work in the eye
of the viewer but this will be short-lived and artificial.
Illusions: The relationship between a foreground colour and a
background one can create distorting illusions that modify the
perceived judgement of a colour. You saw an effect of this earlier
with the inverted area chart showing ‘Gun deaths in Florida’,
whereby the rising white mountain was seen by some as the
foreground data, when in fact it was the background emptiness
framed by the red area of data and the axis line. Illusions can affect
all dimensions of colour perception. There are simply too many to
mention here and they are hard to legislate for entirely; it is really
more about mentioning that you need to be aware of these as a
consequence of your colour choices.
Accessible Design
Consistency: Consistency in the use of colours helps to avoid visual
chaos and confusion and minimises cognitive effort. When you
establish association through colour you need to maintain that
meaning for as long as possible. Once a viewer has allocated time and
effort to learn what colours represent, that association becomes
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locked down in the eye and the mind. However, if you then allocate
the same colour(s) to mean something different (within the same
graphic or on a different page/screen view) this creates an additional
cognitive burden. The viewer has almost to disregard the previous
association and learn the new one. This demands effort that
undermines the accessibility of your design.
Sometimes this can prove difficult, especially if you have a restricted
colour palette. The main advice here is to try to maximise the ‘space’
between occasions of the same colour meaning different things. This space
may be physical (different pages, interactive views), time (the simple
duration of reading between the associations being changed) or editorial
(new subject matter, new angle of analysis). Such space effectively helps
to clean the palate (pun intended). Of course, at the point of any new
assignment in your colour usage, clear explanations are mandatory.
Visual accessibility: Approximately 5% of the population have visual
impairments that compromise their ability to discern particular colours and
colour combinations. Deuteranopia is the most common form, often
known as red–green colour blindness, and is a particular genetic issue
associated with men. The traffic light scheme of green = ‘good’, red =
‘bad’ is a widespread approach for using colour as an indicator. It is a
convenient and common metaphor and the reasons for its use are entirely
understandable. However, as demonstrated in the pair of graphics in Figure
9.29, looking at some word-usage sentiment analysis, the reds and greens
that most of us would easily discern (from the left graphic) are often not at
all distinguishable for those with colour blindness (simulated on the right).
Figure 9.29 Demonstrating the Impact of Red-green Colour Blindness
(deuteranopia)
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Of course, if you have a particularly known, finite and fixed audience then
you can easily discover if any colour-blindness issues do in fact exist.
However, if your audience is much larger and you are going to
alienate potentially 1 person in every 14, in which case the use of the
default red–green colour combination is not acceptable. Be more sensitive
to your viewers by considering other options:
Figure 9.30 Colour-blind Friendly Alternatives to Green and Red
If you are working on an interactive solution, you may consider
having a toggle option to switch between different colour modes. For
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print outputs you might normally have reduced flexibility, but in
certain circumstances the option of creating dual versions (second
output for colour-impaired viewers) may be legitimate.
Connotations and congruence: Whether it is in politics, sport,
brands or in nature, there are many subjects that already have
established colour associations you can possibly look to exploit. This
association may sit directly with the data, such as the normal colour
associations for political party categories, or more through the
meaning of the data, such as perhaps through the use of green to
present analysis about ecological topics.
In support of accessible design, exploiting pre-existing colour
associations in your work can create more immediacy in subject
recognition. You might also benefit from the colour learning
experiences your viewers may already have gone through. This
provides a shortcut to understanding through familiarity.
However, while some colour connotations can be a good thing, in
some cases they can be a bad thing and possibly should be avoided.
You need to be considerate of and sensitive to any colour usage to
ensure that you do not employ connotations that may have a negative
implication and may evoke strong emotions and reactions from
people.
Sometimes a colour is simply incongruent with a subject. You would
not use bright, happy colours if you were portraying data about death
or disease. Earlier, in the ‘Vision: Ideas’ section, I described a project
context where I knew I wanted to avoid the use of blue colours in a
particular project about psychotherapy treatment in the Arctic,
because it would carry an unwelcome clichéd association given the
subject matter. The use of ‘typical’ skin colours to represent ethnic
groups in a visualisation is something that would be immediately
clumsy (at best) and offensive (at worst).
Cultural sensitivities and inconsistencies are also important to
consider. In China, for example, red is a lucky colour and so the use
of red in their stock market displays, for example, indicates the rising
values. A sea of red on the FTSE or Dow Jones implies the opposite.
In Western society red is often the signal for a warning or danger.
Occasionally established colour associations are out of sync with
contemporary culture or society. For example, when you think about
colour and the matter of gender, because it has been so endlessly
utilised down the years, it is almost impossible not to think
instinctively about the use of blue (boys) and pink (girls). My
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personal preference is to avoid this association entirely. I agree with
so many commentators out there that the association of pink to
signify the female gender, in particular, is clichéd, outdated and no
longer fit for purpose. It is not too much to expect viewers to learn the
association of – at most – two new colours for representing gender.
Elegant Design
Unity: As I alluded to in the discussion about using colours for
editorial salience, colour choices are always about contrast. The effect
of using one colour is not isolated to just that instance of colour:
choosing one colour will automatically create a relationship with
another. There is always a minimum of two colours in any
visualisation – a foreground and background colour – but generally
there are many more.
We notice the impact of colour decisions more when they are done
badly. Inconsistent and poorly integrated colour combinations create
jarring and discordant results. If we do not consciously notice colour
decisions this probably means they have been seamlessly blended into
the fabric of the overall communication.
Neutral colouring: Even if there is no relevance in the use of colour
for quantitative or categorical classifications, you still have to give
your chart some colour, otherwise it will be invisible. The decision
you make will depend again on the relative harmony with other
colour features but should also avoid unnecessarily ‘using up’ a
useful colour. Suppose you colour your bars in blue but then
elsewhere across your visualisation project blue would have been a
useful colour to show something meaningful; you then have
unnecessarily taken blue out of the reckoning. My default choice is to
go with grey to begin with (Figure 9.31) and only use a colour if there
is a suitable and available colour not used elsewhere or if it needs to
be left as a back- or mid-ground artefact to preserve prominence
elsewhere in the display.
Figure 9.31 Excerpt from ‘Pyschotherapy in The Arctic’
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Justified: Achieving elegant design is about eliminating the arbitrary.
In thinking about colour usage I often get quite tough with myself. If I
want to show any feature on my visualisation display I have to seek
permission from myself to unlock access to the more vibrant colours
by justifying why I should be allowed to use and apply that colour (I
know what you’re thinking, ‘what a fun existence this guy leads’).
Elegance in visualisation design is often about using only the colours
you need to use and avoiding the temptation to inject unnecessary
decoration. The Wind Map project (Figure 9.32) demonstrates
unquestionable elegance and yet uses only a monochromatic palette.
There is no colouring of the sea, no topographic detail, no
emphasising of any extreme wind speed thresholds being reached.
The resulting elegance is quite evident: the map has artistic and
functional beauty.
To emphasise again, I am not advocating a need to pursue
minimalism: while you can create incredibly elegant and detailed
works from a limited palette of colours, justifying the use of colours
is not the same as unnecessarily restricting the use of colour.
Feels right: The last component of influence is yourself. Sometimes
you will just find colours that feel right and look good when you
apply them to your work. There is maybe no underlying science
behind such choices, and as such you will simply need to back your
own instinctive judgement as an astute visualiser and know when
something looks good. Creating the right type of visual appeal,
something that is pleasing to the eye and equally fit for purpose in all
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the functional ways I have outlined, is a hard balance to achieve, but
you will find that weighing up all these different components of
influence alongside your own flair for design judgement will give you
the best chance of getting there.
Figure 9.32 Wind Map
Summary: Colour
Data legibility involves using colours to represent different types of data.
The most appropriate colour association or scale decisions will depend on
the data type: nominal (qualitative), ordinal (qualitative), interval and ratio
(quantitative).
Editorial salience is about using colour to direct the eye. For which
features and to what degree of emphasis do you want to create contrast?
Functional harmony concerns deciding about every other colour property
as applied to all interactive features, annotations and aspects of your
composition thinking.
Influencing Factors and Considerations
Formulating the brief: format, setting, colour rules and imposed
guidelines all have a significant impact. Your definitions about both
tone and and experience, on the purpose map, will lead to specific
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choices being more suitable than others. What initial ideas did you
form? Have any sources of inspiration already implanted ideas inside
your head about which colours you could use?
Working with data: what type of data and what range of
values/number of classifications have you got?
Establishing your editorial thinking: what things do you want to
emphasise or direct the eye towards (focus)?
Data representation: certain chart type choices will already include
colour as an encoded attribute.
Trustworthy design: ensure that your colour choices are faithful to the
shape of your data and the integrity of your insights. If something
looks meaningful it should be, otherwise it will confuse or deceive.
Accessible design: once you’ve committed colour to mean something
preserve the consistency of association for as long as possible. Be
aware of the sensitivities around visual accessibility and
positive/negative colour connotations.
Elegant design: the perception of colours is relative so the unity of
your choices needs to be upheld. Ensure that you can justify every dot
of colour used and, ultimately, rely on your own judgment to
determine when your final palette feels right.
Tips and Tactics
Use the squint test: shrink things down and/or half close your eyes to
see what coloured properties are most prominent and visible – are
these the right ones?
Experimentation: trial and error is still often required in colour,
despite the common sense and foundation of science attached to it.
Developing a personal style guide for colour usage saves you the pain
of having to think from scratch every time and will help your work
become more immediately identifiable (which may or may not be an
important factor).
Make life easier by ensuring your preferred (or imposed) colour
palettes are loaded up into any tool you are using, even if it is just the
tool you are using for analysis rather than for the final presentation of
your work.
If you are creating for print, make sure you do test print runs of the
draft work to see how your colours are looking – do not wait for the
first print when you (think you) have finished your process.
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10 Composition
Composition concerns making careful decisions about the physical
attributes of, and relationships between, every visual property to ensure the
optimum readability and meaning of the overall, cohesive project.
Composition is the final layer of your design anatomy, but this should not
imply that it is the least important part of your design workflow. Far from
it. It is simply that now is the most logical time to think about this, because
only at this point will you have established clarity about what content to
include in your work. As I explained, this final layer of design thinking,
along with colour, is no longer about what elements will be included but
how they will appear. Composition is a critical component of any design
discipline. The care and attention afforded in the precision of your
composition thinking will continue until the final dot or pixel has been
considered.
Visual assets such as your chart(s), interactive controls and annotations all
occupy space. In this chapter you will be judging what is the best way to
use space in terms of the position, size and shape of every visible property.
In many respects these individual dimensions of thought are inseparable
and so, similar to the discussion about annotation, the division in thinking
is separated between project- and chart-level composition options:
Project composition: defining the layout and hierarchy of the entire
visualisation project.
Chart composition: defining the shape, size and layout choices for all
components within your charts.
10.1 Features of Composition: Project
Composition
This first aspect of composition design concerns how you might lay out
and size all the visual content in your project to establish a meaningful
hierarchy and sequence. Content, in this case, means all of your charts,
interactive operations and elements of annotation.
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Where will you put all of this, what size will it be and why? How will the
hierarchy (across views) and sequencing (within a view) best fit the space
you have to work in? How will you convey the relative importance and
provide a connected narrative where necessary?
I will shortly run through all the key factors that will influence your
decisions, but it is worth emphasising that so much about composition
thinking is rooted in common sense and involves a process of iteration
towards what feels like an optimum layout. Of course, there are certain
established conventions, such as the positioning of titles first or at the top
(usually left or centrally aligned). Introductions are inevitably useful to
offer early, whereas footnotes detailing data sources and credits might be
of least importance, relatively speaking. You might choose to show the
main features first, exploiting the initial attention afforded by your
audience, or you may wish to build up to this, starting off with contextual
content before the big ‘reveal’.
Figure 10.1 City of Anarchy
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The hierarchy of content is not just a function of relative position through
layout design, it can also be achieved through the relative variation in size
of the contents. Just as variation in colour implies significance, so too does
variation in size: a chart that is larger than another chart will imply that the
analysis it is displaying carries greater importance.
The ‘City of anarchy’ infographic demonstrates a clear visual hierarchy
across its design. There is a primary focal point of the main subject
‘cutaway’ illustration in the centre with a small thumbnail image above it
for orientation. At the bottom there are small supplementary illustrations to
provide further information. It is clear through their relative placement at
the bottom of the page and their more diminutive stature that they are of
somewhat incidental import compared with the main detail in the centre.
There are generally two approaches for shaping your ideas about this
project-level composition activity, depending on your entry-point
perspective: wireframing and storyboarding. I profiled these at the start
of this part of the book, but it is worth reinforcing their role now you are
focusing on this section of design thinking.
Wireframing involves sketching the potential layout and size of all the
major contents of your design thinking across a single-page view.
This might be the approach you take when working on an infographic
or any digital project where all the interactive functions are contained
within a single-screen view rather than navigating users elsewhere.
Any interactive controls included would have a description within the
wireframe sketch to explain the functions they would trigger.
Figure 10.2 is an early wireframe drawn by Giorgia Lupi when
shaping up her early thoughts about the potential layout of a graphic
exploring various characteristics of Nobel prizes and laureates
between 1901 and 2012.
Figure 10.2 Wireframe Sketch
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Storyboarding is something you would undertake with wireframing if
you have a project that will entail multiple pages or many different
views and you want to establish a high-level feel for the overall
architecture of content, its navigation and sequencing. This would be
an approach relevant for linear outputs like discrete sequences in
reports, presentation slides or video graphics, or for non-linear
navigation around different pages of a multi-faceted interactive. The
individual page views included as cells in this big-picture hierarchy
will each merit more detailed wireframing versions to determine how
their within-page content will be sized and arranged, and how the
navigation between views would operate.
With both wireframing and storyboarding activities all you are
working towards, at this stage, are low-fidelity sketched concepts.
Whether this sketching is on paper or using a quick layout tool does
not matter; it just needs to capture with moderate precision the
essence of your early thinking about the spatial consequence of
bringing all your design choices together. Gradually, through further
iteration, the precision and finality of your solution will emerge.
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10.2 Features of Composition: Chart
Composition
After establishing your thoughts about the overall layout, you will now
need to go deeper in your composition thinking and contemplate the
detailed spatial matters local to each chart, to optimise its legibility and
meaning. There are many different components to consider.
Chart size: Do not be afraid to shrink your charts. The eye can still
detect at quite small resolution and with great efficiency chart
attributes such as variation in size, position, colour, shape and pattern.
This supports the potential value of the small-multiples technique, an
approach that tends to be universally loved in data visualisation. As I
explained earlier, this technique offers an ideal solution for when you
are trying to display the same analysis for multiple categories or
multiple points in time. Providing all the information in a
simultaneous view means that viewers can efficiently observe overall
patterns as well as perform a more detailed inspection. Figure 10.3
provides a single view of a rugby team’s match patterns across the
first 12 matches of a season. Each line chart panel portrays the
cumulative scoring for the competing teams across the 80 minutes of
a match. The 12 match panels are arranged in chronological order,
from top left to bottom right, based on the date of the match.
Figure 10.3 Example of the Small Multiples Technique
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The main obstacle to shrinking chart displays is the impact on text.
The eye will not cope too well with small fonts for value or category
labels, so there has to be a trade-off, as always, between the amount
of detail you show and the size you show it.
Chart scales: When considering your chart-scales try to think about
how you might use these to tell the viewer something meaningful.
This can be achieved through astute choices around the maximum
value ranges and also in the choice of suitable intervals for labelling
and gridline guides.
The maximum values that you assign to your chart scales, informed
by decisions around editorial framing, can be quite impactful in
surfacing key insights. You may recall the chart from earlier that
looked at the disproportionality of women CEO’s amongst the S&P
1500 companies. Figure 10.4 is another graphic on a similar subject,
which contextualises the relative progress in the rise of women CEOs
amongst the Fortune 500 companies. By setting the maximum y-axis
value range to reflect the level at which equality would exist, the
resulting empty space emphasises the significant gap that still
persists.
Figure 10.4 Reworking of ‘The Glass Ceiling Persists’
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Figure 10.5 Fast-food Purchasers Report More Demands on Their
Time
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Figure 10.5 shows how the lack of careful thought about your scales
can undermine the ease of readability. This chart shows how
American adults spend their time on different activities. The analysis
is broken down into minutes and so the maximum is set at 1440
minutes in a day. For some reason, the y-axis labels and the
associated horizontal gridlines are displayed at intervals of 160
minutes. This is an entirely meaningless quantity of time so why
divide the day up into nine intervals? To help viewers perceive the
significance and size of the different stacked activities it would have
been far more logical to use 60-minute time intervals as that is how
we tend to think when dividing our daily schedule.
Chart orientation: Decisions about the orientation of your chart and
its contents can sometimes help squeeze out an extra degree of
readability and meaning from your display.
Figure 10.6 Illustrating the Effect of Chartorientation Decisions
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The primary concern about chart orientation is towards the legibility
of labels along the axis. A vertical bar chart, with multiple categories
along the x-axis, will present a challenge of making the labels legible
and avoiding them overlapping. Ideally you would want to preserve
label reading in line with the eye, but you might need to adjust their
orientation to either 45° or 90°. My preference for handling this with
bar charts is to switch the orientation of the chart and to then have
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much more compatible horizontal space to accommodate the labels.
The meaning of your subject’s data may also influence your choice.
While there may have been constraints on the dimension of space in
its native setting, Figure 10.6, portraying the split of political parties
in Germany, feels like a missed opportunity to display a political axis
of the Left and the Right through using a landscape rather than
portrait layout.
As you saw earlier, the graphic about ‘Iraq’s bloody toll’ (Figure
1.11) uses an inverted bar chart to create a potent display of data that
effectively conveys the subject matter, but importantly does so
without introducing any unnecessary obstacles in readability.
In the previous section I presented a wireframe sketch of a graphic
about Nobel prize winners. Figure 10.7 shows the final design. Notice
how the original concept of the novel diagonal orientation was
accomplished in the final composition, exploiting the greater room
that this dimension of space offers within the page. It feels quite
audacious to do this in a newspaper setting.
Figure 10.7 Nobels no Degrees
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Figure 10.8 Kasich Could Be The GOP’s Moderate Backstop
Figure 10.8, from FiveThirtyEight, rotates the scatter plot by 45° and
then overlays a 2 × 2 grid which helps to guide the viewer’s
interpretation by making it easier to observe which values are located
in each quadrant. It is also used to emphasise the distinction between
location in the top and bottom halves of the chart along the axis of
popularity, essentially the primary focus of the analysis.
Although the LATCH and CHRTS acronyms share some similarities,
the application of each concerns entirely different aspects of your design
thinking. They are independent of one another. A bar chart, which
belongs to the categorical (C) family of charts, could have its data
potentially sorted by location, alphabet, time, category or hierarchy.
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Chart value sorting: Sorting content within a chart is important for
helping viewers to find and compare quickly the most relevant
content. One of the best ways to consider the options for value sorting
comes from using the LATCH acronym, devised by Richard Saul
Wurman, which stands for the five ways of organising displays of
data: Location, Alphabet, Time, Category or Hierarchy.
Location sorting involves sequencing content according to the order
of a spatial dimension. This does not refer to sorting data on a map
locations are fixed, rather it could be sorting data by geographical
spatial relationships (such as presenting data for all the stops along a
subway route) or a non-geographical spatial relationship (like a
sequence based on the position of major parts of the body from head
to toe). You should order by location only when you believe it offers
the most logical sequence for the readability of the display or if there
is likely to be interest or significance in the comparison of
neighbouring values. An example of location sorting is displayed in
‘On Broadway’ (Figure 10.9) on the following page, an interactive
installation that stitches together a sequenced compilation of data and
media related to 30 metre intervals of life along the 13 miles (21 km)
of Broadway that stretches across the length of Manhattan. This
continuous narrative offers compelling views of the fluctuating
characteristics as you transport yourself down the spine of the city.
Figure 10.9 On Broadway
Alphabetical sorting is a cataloguing approach that facilitates efficient
lookup and reference. Only on rare occasions, when you are
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especially keen to offer convenient ordering for looking up
categorical values, will you find that alphabetical sorting alone offers
the best sequence. In Figure 10.10, investigating different measures of
waiting times in emergency rooms across the United States, the bar
charts are presented based on the alphabetical sorting of each state.
This is the default setting but users can also choose to reorder the
table hierarchically based on the increasing/decreasing values across
the four columns.
Data representation techniques that display overlapping connections,
like Sankey diagrams, slope graphs and chord diagrams, also introduce
the need to contemplate value sorting in the z-dimension: that is, which
of these connections will be above and which will be below, and why.
Alphabetical sorting might be seen as a suitably diplomatic option
should you not wish to imply any ranking significance that would be
displayed when sorting by any other dimension. Additionally, there is
a lot of sense in employing alphabetical ordering for values listed in
dropdown menus as this offers the most immediate way for viewers
to quickly find the options they are interested in selecting.
Figure 10.10 ER Wait Watcher: Which Emergency Room Will See
You the Fastest?
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Time-based sorting is used when the data has a relevant chronological
sequence and you wish to display and compare how changes have
progressed over time. In Figure 10.11, you can see a snapshot of a
graphic that portrays the rain patterns in Hong Kong since 1990. Each
row of data represents a full year of 365/366 daily readings running
from left to right. The subject matter and likely interest in the
seasonality of patterns make chronological ordering a common-sense
choice.
Figure 10.11 Rain Patterns
Categorical sorting can be usefully applied to a sequence of
categories that have a logical hierarchy implied by their values or
unique to the subject matter. For example, if you were presenting
analysis about football players you might organise a chart based on
the general order of their typical positions in a team (goalkeeper >
defenders > midfielders > forwards) or use seniority levels as a way
to present analysis about staff numbers. Alternatively, if you have
ordinal data you can logically sort the values according to their
inherent hierarchy. In Figure 10.12, that you saw earlier in the profile
of ordinal colours, the columns are sequenced left to right in order
from ‘major deterioration’ to ‘major improvement’, to help reveal the
balance of treatment outcomes from a sample of psychotherapy
clients.
Figure 10.12 Excerpt from ‘Pyschotherapy in The Arctic’
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Hierarchical sorting organises data by increasing or decreasing
quantities so a viewer can efficiently perceive the size, distribution
and underlying ranking of values. In Figure 10.13, showing the
highest typical salaries for women in the US, based on analysis of
data from the US Bureau of Labour Statistics, the sorting arrangement
presents the values by descending quantity to reveal the highest
rankings values.
Figure 10.13 Excerpt from ‘Gender Pay Gap US’
In Figure 10.12 the bubbles in each column do not need to be coloured
as their position already provides a visual association with the
‘deterioration’ through to ‘improvement’ ordinal categories. The
attribute of colour, specifically, can therefore be considered redundant
encoding. However, you might still choose to include this redundancy if
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you believed it aided the immediacy of association and distinction. In
this case, the chart was part of a larger graphic that employed the same
colour associations across several different charts and therefore it made
sense to preserve this association.
10.3 Influencing Factors and Considerations
You are now familiar with the array of various aspects of composition
thinking. At this point you will need to weigh up your decisions on how
you might employ these in your own work. Here are some of the specific
factors to bear in mind.
Formulating Your Brief
Format: Naturally, as composition is about spatial arrangement, the
nature and dimensions of the canvas you have to work with will have
a fundamental bearing on the decisions you make. There are two
concerns here: what will be the shape and size of the primary format
and how transferable will your solution be across the different
platforms on which it might be used or consumed?
Another factor surrounding format concerns the mobility of viewing
the work. If the form of your output enables viewers to easily move a
display or move around a display in a circular plane (such as looking
at a printout or work on a tablet) this means that issues such as label
orientation can be largely cast aside. If your output is going to be
consumed in a relatively fixed setting (desktop/laptop or via a
presentation) the flexibility of viewing positions will be restricted.
Working With Data
Data examination: Not surprisingly, the shape and size of your data
will directly influence your chart composition decisions. When
discussing physical properties in Chapter 4, I described the influence
of quantitative values with legitimate outliers distorting ideal scale
choices. One solution for dealing with this is to use a non-linear
logarithmic (often just known as a ‘log’) scale. Essentially, each
major interval along a log scale increases the value at that marked
position by a factor of 10 (or by one order of magnitude) rather than
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by equal increments. In Figure 10.14, looking at ratings for thousands
of different board games, the x-axis is presented on a log scale in
order to accommodate the wide range of values for the ‘Number of
ratings’ measure and to help fit the analysis into a square-chart layout.
Had the x-axis remained as a linear scale, to preserve a square layout
would have meant squashing values below 1000 into such a tightly
packed space that you would hardly see the patterns. Alternatively, a
wide rectangular chart would have been necessary but impractical
given the limitations of the space this chart would occupy.
I have great sympathy for the challenges faced by designers like
Zimbabwe-based Graham van de Ruit, when working on typesetting a
book titled Millions, Billions, Trillions: Letters from Zimbabwe,
2005−2009 in 2014. The book was all text, apart from one or two
tables. One of the tables of data supplied to Graham showed
Zimbabwe’s historical monthly inflation rates, which, as you can see
(Figure 10.15), included some incredibly diverse values.
I love the subtle audacity of Graham’s solution. Even though it is
presented in tabular form there is a strong visual impact created by
allowing the sheer spatial consequence of the exceptional mid-2008
numbers to cause the awkward widening of the final column. I think
this makes the point much more effectively than a chart might, in this
case.
Figure 10.14 The Worst Board Games Ever Invented
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Figure 10.15 From Millions, Billions, Trillions: Letters from Zimbabwe,
2005−2009
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‘I thought that a graph might be more effective, but I quickly realised
that the scale would be a big challenge… The whole point of graphing
would have been to show the huge leap in 2008, something that I felt
the log scale would detract from and was impractical with the space
constraints. I also felt that a log scale might not be intuitive to the target
audience.’ Graham van de Ruit, Editorial and Information Designer
Establishing Your Editorial Thinking
Angles: The greater the number of different angles of analysis you
wish to cover in your work, the greater the challenge will be to
seamlessly accommodate the resulting chart displays in one view. The
more content you include increases the need to contemplate
reductions in the size of charts or a non-simultaneous arrangement,
perhaps through multi-page sequences with interactive navigation.
In defining your editorial perspectives, you will have likely
established some sense of hierarchy that might inform which angles
should be more prominent (regarding layout position and size) and
which less so. There might also be some inherent narrative binding
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each slice of analysis that lends itself to being presented in a
deliberate sequence.
Data Representation
Chart type choice: Different charts have different spatial
consequences. A treemap generally occupies far more space than a
pie chart simply because there are many more ‘parts’ being shown. A
polar chart is circular in shape, whereas a waffle chart is squared.
With each chart you include you will have a uniquely shaped piece
that will form part of the overall jigsaw puzzle. Inevitably there will
be some shuffling of content to find the right size and placement
balance.
The table in Figure 10.16 summarises the main chart structures and
the typical shapes they occupy. This list is based only on the charts
included in the Chapter 6 gallery but still offers a reasonable
compilation of the main structures. These are ordered in descending
frequency as per the distribution of the different structures of charts in
the gallery.
Figure 10.16 List of chart structures
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Trustworthy Design
Chart-scale optimisation: Decisions about chart scales concern the
maximum, minimum and interval choices that ensure integrity
through the representation as well as optimise readability.
Firstly, let’s look at decisions around minimum values used on the
quantitative value axis, known as the origin, and the reasons why it is
not OK for you to truncate the axis in methods like the bar chart. Any
data representation where the attribute of size is used to encode a
quantitative value needs to show the full, true size, nothing more and
nothing less. The origin needs to be zero. When you truncate a bar
chart’s quantitative value axis you distort the perceived length or
height of the bar. Visualisers are often tempted to crop axis scales
when values are large and the differences between categories are
small. However, as you can see in Figure 10.17, the consequence is
that it creates the impression of highly noticeable relative difference
between values when the absolute values do not support this.
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Figure 10.17 Illustrating the Effect of Truncated Bar Axis Scales
The single instance in which it is remotely reasonable to truncate an
axis would be if you had a main graphic which effectively offered a
thumbnail view of the whole chart for orientation positioned
alongside a separate associated chart (similar to that on the right).
This separate chart might have a truncated axis that would provide a
magnified view of the main chart, showing just the tips of the bar, to
help viewers see the differences close up.
In contrast to the bar chart, a line chart does not necessarily need
always to have a zero origin for the value axis (normally the y-axis).
A line chart’s encoding involves a series of connected lines (marks)
joining up continuous values based on their absolute position along a
scale (attribute). It therefore does not encode quantitative values
through size, like the bar chart does, so the truncation of a value axis
will not unduly impact on perceiving the relative values against the
scale and the general trajectory. For some data contexts the notion of
a zero quantity might be impossible to achieve. In Figure 10.18,
showing 100m sprint record times, no human is ever going to be able
to run 100m in anywhere near zero seconds. Times have improved, of
course, but there is a physical limit to what can be achieved. To show
this analysis with the y-axis starting from zero would be unnecessary
and even more so if you plotted similar analysis for longer distance
races.
However, if you were to plot the 100m results and the 400m results
on the same chart, you would need to start from zero to enable
orientation of the scale of comparable values. This sense of
comparable scale is missing from the next chart, whereby including
the full quantitative value range down to zero would be necessary to
perceive the relative scale of attitudes towards same-sex marriage.
The chart’s y-axis appears to start from an origin of 20 but as we are
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looking at part-to-whole analysis, the y-axis should really be
displayed from an origin of zero. The maximum doesn’t need to go
up to 100%, the highest observed value is fine in this case, but it
could be interesting to set the maximum range to 100% in order to
create a similar sense of the gap to be bridged before 100% of
respondents are in agreement.
Figure 10.18 Excerpt from ‘Doping under the Microscope’
Figure 10.19 Record-high 60% of Americans Support Same-sex
Marriage
Aspect ratios: The aspect ratio of a line chart, as derived from the
height and width dimensions of the chart area, can have a large
impact on the perceived trends presented. If the chart is too narrow,
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the steepness of connections will be embellished and look more
significant; if the chart is stretched out too wide, the steepness of
slopes will be much more dampened and key trends may be
somewhat disguised. There is no absolutely right or wrong approach
here but clearly there is a need for sensitivity to avoid the possibility
of unintended deception. A general rule of thumb is to seek a chart
area that enables the average slope to be presented at 45°, though this
is not something that can be easily and practically applied, especially
as there are many other variables at play, such as the range of
quantitative and time values and the scales being used. My advice is
just to make a pragmatic judgement by eye to find the ratio that you
think is faithful to the significance of the trends in your data.
Mapping projections: One of the most contentious matters in the
visual representation of data relates to thematic mapping and
specifically to the choice of map projection used. The Earth is not flat
(hopefully no contention there, otherwise this discussion is rather
academic), yet the dominant form through which maps are presented
portrays the Earth as being just that. Features such as size, shape and
distance can be measured accurately on Earth but when projected on a
flat surface a compromise has to occur. Only some of these qualities
can be preserved and represented accurately.
I qualify this with ‘dominant’ because, increasingly, advances in
technology (such as WebGL) mean we can now interact with spherical
portrayals of the Earth within a 2D space.
There are lots of exceptionally complicated calculations attached to
the variety of spatial projections. The main things you need to know
about projection mapping are that:
every type of map projection has some sort of distortion;
the larger the area of the Earth portrayed as a flat map, the
greater the distortion;
there is no single right answer – it is often about choosing the
least-worst case.
Thematic mapping (as opposed to mapping spatially for navigation or
reference purposes) is generally best portrayed using mapping projections
based on ‘equal-area’ calculations (so the sacrifice is more on the shape,
not the size). This ensures that the phenomena per unit – the values you are
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typically plotting – are correctly represented by proportion of regional
area. For choosing the best specific projection, in the absence of perfect,
damage limitation is often the key: that is, which choice will distort the
spatial truth the least given the level of mapping required. There are so
many variables at play, however, based on the scope of view (world,
continent, or country/sub-region), the potential distance from the equator
of your region of focus and whether you are focusing on land, sea or sky
(atmosphere), to name but a few. As with many other topics in this field, a
discussion about mapping projections requires a dedicated text but let me
at least offer a brief outline of five different projections to begin your
acquaintance:
Many tools that offer rudimentary mapping options will tend to only
come with a default (non-adjustable) projection, often the Mercator (or
Web Mercator). The more advanced geospatial analysis tools will offer
pre-loaded or add-in options to broaden and customise the range of
projections. Hopefully, in time, an increasing range of the more
pragmatic desktop tools will enhance projection customisations.
Figure 10.20 A Selection of Commonly Deployed Mapping Projections
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Accessible Design
Good design is unobtrusive: One of the main obstructions to
facilitating understanding through a visualisation design is when
viewers are required to rely on their memory to perform comparisons
between non-simultaneous views.
When the composition layout requires viewers to flick between pages
or interactively generated views, they have to try store one view in
their mind and then mentally compare that against the live view that
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has arrived on the screen. This is too hard and too likely to fail given
the relatively weak performance of the brain’s working memory.
Content that warrants direct comparison should be enabled through
proximity to and alignment with related items. I mentioned in the
section on animation that if you want to compare different states over
time, rather than see the connected system of change, you will need to
have access to the ‘moment’ views simultaneously and without a
reliance on memory.
‘Using our eyes to switch between different views that are visible
simultaneously has much lower cognitive load than consulting our
memory to compare a current view with what was seen before.’
Tamara Munzner taken from Visualization Analysis and Design
Elegant Design
‘I’m obsessed with alignments. Sloppy label placement on final files
causes my confidence in the designer to flag. What other details haven’t
been given full attention? Has the data been handled sloppily as well?
… On the flip side, clean, layered and logically built final files are a
thing of beauty and my confidence in the designer, and their attention to
detail, soars.’ Jen Christiansen, Graphics Editor at Scientific
American
Unity: As I discussed with colour, composition decisions are always
relative: an object’s place and its space occupied within a display
immediately create a relationship with everything else in the display.
Unity in composition provides a similar sense of harmony and
balance between all objects on show as was sought with colour. The
flow of content should feel logical and meaningful.
The enduring idea that elegance in design is most appreciated when it
is absent is just as relevant with composition. Look around and open
your eyes to composition that works and does not work, and
recognise the solutions that felt effortless as you read them and those
that felt punctured and confusing. This is again quite an elusive
concept and one that only comes with a mixture of common-sense
judgement, experience and exposure to inspiration from elsewhere.
Thoroughness: Precision positioning is the demonstration of
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thoroughness and care that is so important in the pursuit of elegance.
You should aim to achieve pixel-perfect accuracy in the position and
size of every single property.
Think of the importance of absolute positioning in the context of
detailed architectural plans that outline the position of every fine
detail down to power sockets, door handles and the arc of a window’s
opening manoeuvre. A data visualiser has to commit to ultimate
precision and consistency because any shortcomings will be
immediately noticeable and will fundamentally impact on the
function of the work. If you do not feel a warm glow from every
emphatic snap-to-grid resize operation or upon seeing the results of a
mass alignment of page objects, you are not doing it right. (Honestly,
I am loads of fun to be around.
Summary: Composition
Project composition defines the layout and hierarchy of the entire
visualisation project and may include the following features:
Visual hierarchy – layout: how to arrange the position of elements?
Visual hierarchy – size: how to manage the hierarchy of element
sizes?
Absolute positioning: where specifically should certain elements be
placed?
Chart composition defines the shape, size and layout choices for all
components within your charts and may include the following features:
Chart size: don’t be afraid to shrink charts, so long as any labels are
still readable, and especially embrace the power of small multiple.
Chart scales: what are the most meaningful range of values given the
nature of the data?
Chart orientation: which way is best?
Chart value sorting: consider the most meaningful sorting
arrangement for your data and editorial focus, based on the LATCH
acronym.
Influencing Factors and Considerations
Formulating the brief: what space have you got to work within?
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Working with data: what is the shape and size of your data and how
might this affect your chart design architecture?
Establishing your editorial thinking: how many different angles
(charts) might you need to include? Is there any specific focus for
these angles that might influence a sequence or hierarchy between
them?
Data representation: any chart has a spatial consequence – different
charts have different structures that will create different dimensions
that will need to be accommodated.
Trustworthy design: the integrity and meaning of your chart scale,
chart dimensions, and (for mapping) your projection choices are
paramount.
Accessible design: remember that good design is unobtrusive – if you
want to facilitate comparisons between different chart displays these
ideally need to be presented within a simultaneous view.
Elegant design: unity of arrangement is another of the finger-tip sense
judgments but will be something achieved by careful thinking about
the relationships between all components of your work.
Tips and Tactics
You will find that as you reach the latter stages of your design
process, the task of nudging things by fractions of a pixel and
realigning features will dominate your attention. As energy and
attention start to diminish you will need to maintain a commitment to
thoroughness and a pride in precision right through to the end!
Empty space is like punctuation in visual language: use it to break up
content when it needs that momentary pause, just as how a comma or
full stop is needed in a sentence. Do not be afraid to use empty space
more extensively across larger regions as a device to create impact.
Like the notes not played in jazz, effective visualisation design can
also be about the relationship between something and nothing.
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Part D Developing Your Capabilities
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11 Visualisation Literacy
This final chapter explores some of the important ingredients and tactics
that will help you continue to develop and refine your data visualisation
literacy. By definition, literacy is the ability to read and write. Applied to
data visualisation, this means possessing the literacy both to create
visualisations (write) and consume them (read).
Data visualisation literacy is increasingly an essential capability regardless
of the domain in which we work and the nature of our technical skills. Just
as computer literacy is now a capability that is expected of everyone, one
can imagine a time in the not-too-distant future when having data
visualisation capabilities will be viewed as a similarly ‘assumed’ attribute
across many different roles.
In exploring the components of visualisation literacy across this chapter
we will look at two sides of the same coin: the competencies that make up
the all-round talents of a visualiser but, first, the tactics and considerations
required to be an effective and efficient viewer of data visualisation.
11.1 Viewing: Learning to See
Learning how to understand a data visualisation, as a viewer, is not a topic
that has been much discussed in the field until recently. For many the idea
that there are possible tactics and efficient ways to approach this activity is
rarely likely to have crossed their mind. We just look at charts and read
them, don’t we? What else is there to consider?
Many of the ideas for this section emerged from the Seeing Data
visualisation literacy research project (seeingdata.org) on which I
collaborated.
The fact is we are all viewers. Even if you never create a visualisation
again you will always be a viewer and you will be widely exposed to
different visual forms of data and information across your daily life. You
cannot escape them. Therefore, it seems logical that optimising
visualisation literacy as a consumer is a competency worth developing,
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Let’s put this into some sort of context. As children we develop the ability
to read numbers and words. These are only understandable because we are
taught how to recognise the association between numeric digits and their
representation as numbers and the connection between alphabetical
characters with letters and words. From there we begin to understand
sentences and eventually, as we build up a broader vocabulary, we acquire
the literacy of language. This is all a big effort. We are not born knowing a
language but we are born with the capacity to learn one.
Beyond written language, something as simple and singular as, for
example, the Wi-Fi symbol is now a universally recognised form of visual
language but one that only exists in contemporary culture. For millions of
people today, this symbol is a signal of relief and tangible celebration –
‘Thank God, Wi-Fi is available here!’ The context of the use of this
symbol would have meant nothing to people in the 1990s: it is a symbol of
its time and we have learnt to recognise its use and understand its meaning.
Across all aspects of our lives, there are things that once seemed
complicated and inaccessible but are now embedded within us as
automatic competencies: driving a car, using a keyboard, cooking a meal. I
often think back to growing up as a kid in the 1980s and my first
(functioning) computer, the mighty Commodore 64 (C64). One of the most
famous games in the UK from this period was Daley Thompson’s
Decathlon. Of particular nostalgic fame was the brutally simple operation
of maniacally waggling the single joystick arm left and right to control the
running events (if memory serves me correctly, the single button came into
use when there were hurdles to jump over).
Consider the universally and immediately understandable control
configuration of that game with the frankly ludicrous number of options
and combinations that exist on the modern football games, such as the
FIFA series on contemporary consoles like the Xbox or PS4. The control
combinations required to master the array of attacking moves alone require
an entire page of instruction and remarkable levels of finger dexterity. Yet
young kids today are almost immediate masters of this game. I should
know – I have been beaten by some awfully young opponents. It hurts. But
they have simply utilised their capacity to learn through reading and
repeated practice.
As discussed in Chapter 1 when looking at the principle of ‘accessible’
design, many data visualisations will be intended – and designed – for
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relatively quick consumption. These might be simple to understand and
offer immediately clear messages for viewers to easily comprehend. They
are the equivalent of the C64 joystick controls. However, there will be
occasions when you as a viewer are required to invest a bit more time and
effort to work through a visualisation that might be based on subject matter
or analysis of a more complex nature, perhaps involving many angles of
analysis or numerous rich features of interactivity. This is the equivalent
prospect of mastering the Xbox controls. Without having the confidence or
capability to extract as much understanding from the viewing experience
as possible and doing so as efficiently as possible, you are potentially
missing out.
‘Though I consider myself a savvy consumer of bar charts, line graphs,
and other traditional styles of data display, I’m totally at sea when
trying to grasp what’s going on in, say, arc diagrams, circular hierarchy
graphs, hyperbolic tree charts, or any of the seemingly outlandish
visualisations … I haven’t thought much about this flip side, except that
I do find I now view other people’s visualisations with a more critical
eye.’ Marcia Gray, Graphic Designer
As viewers, we therefore need to acknowledge that there might be a need
to learn and a reward from learning. We should not expect every type of
visualisation to signpost every pearl of insight that is relevant to us. We
might have to work for it. And we have to work for it because we are not
born with the ability or the right to understand everything that is presented
to us. Few of us will have ever been taught how to go about effectively
consuming charts and graphics. We might be given some guidance on how
to read charts and histograms, maybe even a scatter plot, if we study maths
or the sciences at school. Otherwise, we get by.
But ‘getting by’ is not really good enough, is it? Even if, through exposure
and repetition, we hope gradually to become more familiar with the most
common approaches to visualising data, this does not sufficiently equip us
with the breadth and range of literacy that will be required.
I mentioned earlier the concept, proposed by Daniel Kahneman, of System
1 and System 2 thinking. The distinctions of these modes of thought
manifest themselves again here. Remember how System 1 was intuitive
and rapid whereas System 2 was slow, deliberate and almost consciously
undertaken? For example, you are acutely aware of thinking when trying
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to run a mathematical calculation through your mind. That is System 2 at
work. In part, due to the almost hyperactive and instinctive characteristics
of System 1, when there is a need for System 2 thinking to kick into
action, we might try to avoid whatever that activity entails. We get lazy
and resort to shortcut solutions or decisions based on intuition. System 1
almost persuades System 2 to sit back and let it look after things. Anything
to avoid having to expend effort thinking deeply and rationally.
The demands of learning anything new or hard can trigger that kind of
response. It is understandable that somebody facing a complex or
unfamiliar visualisation that needs learning might demonstrate antipathy
towards the effort required to learn.
Of course, there are other factors involved in learning, such as having the
time, receiving assistance or tuition, and recognising the incentive. These
are all enablers and therefore their absence can create obstacles to learning.
Without assistance from the visualiser, viewers are left to fend for
themselves. The role of this book has primarily been to try to raise the
standard of the design choices that visualisers make when creating
visualisations. Visualisers do not want to obstruct viewers from being able
to read, interpret and comprehend. If work is riddled with design errors
and misjudgements then viewers are naturally going to be disadvantaged.
However, even with a technically perfect design, as I explained in the
definition section of the first chapter, we as visualisers can only do so
much to control this experience. There are things we can do to make our
work as accessible as possible, but there is also a partial expectation of the
viewer to be willing to make some effort (so long as it is ‘proportional’) to
get the most out of the experience. The key point, however, is that this
effort should be rewarded.
Many of the visualisations that you will have seen in this book,
particularly in Chapter 6, may have been unfamiliar and new to you. They
need learning. Your confidence in being able to read different types of
charts is something that will develop through practice and exposure. It will
be slow and deliberate at first, probably a little consciously painful, but
then, over time, as the familiarity increases and the experiential benefits
kick in, perceiving these different types of representations will become
quite effortless and automatic. System 2 thinking will then transform into a
reliably quick form of System 1 thinking.
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Over the next few pages I will present a breakdown of the components of
effectively working with a visualisation from the perspective of being a
viewer. This demonstration will provide you with a strategy for
approaching any visualisation with the best chance of understanding how
to read it and ensure you gain the benefit of understanding from being able
to read it.
To start with I will outline the instinctive thoughts and judgements you
will need to make before you begin working with a visualisation. I will
then separate the different features of a visualisation, first by considering
the common components that sit outside the chart and then some pointers
for how to go about perceiving what is presented inside the chart. This part
will also connect with the content included in the chart type gallery found
in Chapter 6 describing how to read each unique chart type. Finally, I will
touch on the attributes that will lead you, in the longer term, to becoming a
more sophisticated viewer.
It is important to note that not all data visualisation and infographic
designs will have all the design features and apparatus items that I describe
over the next few sections. There may be good reasons for this in each
case, depending on the context. However, if you find there are significant
gaps in the work you are consuming, or features of assistance have been
deployed without real care or quality, that would point to flawed design. In
these cases the viewer is not really being given all the assistance required:
the visualiser has failed to facilitate understanding.
Figure 11.1 The Pursuit of Faster
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To illustrate this process I will refer to a case-study project titled ‘The
Pursuit of Faster: Visualising the Evolution of Olympic Speed’. As the title
suggests, the focus of this work was to explore how results have changed
(improved or declined) over the years of the Olympics for those events
where speed (as measured by a finishing time) was the determinant of
success.
Before You Begin
Here are some of the instinctive, immediate thoughts that will cross your
mind as soon as you come face to face with a data visualisation. Once
again, these are consistent with the impulsive nature of the System 1
thoughts mentioned earlier.
Setting: Think about whether the setting you are in is conducive to
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consuming a visualisation at that moment in time. Are you under any
pressure of time? Are you on a bumpy train trying to read this on your
smartphone?
Visual appeal? In this early period of engaging with the work you
will be making a number of rapid judgements to determine whether
you are ‘on board’. One of the ingredients of this is to consider
whether the look and feel (the ‘form’) of the visualisation attract you
and motivate you to want to spend time with it.
Relevance? In addition to the visual appeal, the second powerful
instinct is to judge whether the subject matter interests you. You
might have decided you are on board with your instinctive reaction to
the visuals but the key hurdle is whether it is even interesting or
relevant to you. Ask yourself if this visualisation is going to deliver
some form of useful understanding that confirms, enlightens or thrills
you about the topic.
If you respond positively to both those considerations you will likely
be intent on continuing to work with the visualisation. Even if you are
just positive about one of these factors (form or subject) you will
most probably persevere despite the indifference towards the other. If
your thoughts are leaning towards a lack of interest in both the
relevance of this work and its visual appeal then, depending on
circumstances, your tolerance may not be high enough to continue
and it will be better to abandon the task there and then.
Initial scan? It is inevitable that your eyes will be instinctively drawn
to certain prominent features. This might be the title or even the chart
itself. You may be drawn to a strikingly large bar or a sudden upward
rise on a line chart. You might see a headline caption that captures
your attention or maybe some striking photo imagery. It is hard to
fight our natural instincts, so don’t. Allow yourself a brief glance at
the things you feel compelled to look at – these are likely the same
things the visualiser is probably hoping you are drawn to. Quickly
scanning the whole piece, just for an initial period of time, gives you
a sense of orientation about what is in store.
In ‘The Pursuit of Faster’ project you might find yourself only drawn
to this if you have a passing interest in the Olympics and/or the
history of athletic achievement. On the surface, the visuals might look
quite analytical in nature, which might turn some people off. The
initial scan probably focuses on elements like the Olympic rings and
the upward direction of the lines in the chart which might offer a
degree of intrigue, as might the apparent range of interactive controls.
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Outside the Chart
Before getting into the nuts and bolts of understanding the chart displays,
you will first need to seek assistance from the project at large to
understand in more detail what you are about to take on and how you
might need to go about working with it.
The Proposition
Considering the proposition offered by the visualisation is about
determining how big a task of consuming and possibly interacting you
have ahead of you. What is its shape, size and nature?
Format: Is it presented in a print, physical or digital format and what
does this make you feel about your potential appetite and the level of
your engagement? Is it static or interactive and what does this present
in terms of task?
If it is a static graphic, how large and varied is the content – is it
a dense display with lots of charts and text, or quite a small and
compact one? Does the sequence of content appear logical?
If it is interactive, how much potential interactivity does there
appear to be – are there many buttons, menus, options, etc.?
Where do the interactive events take you? Are there multiple
tabs, pages or layers beneath this initial page? Have a click
around.
Shape and size: Do you think you will probably to have to put in a
lot of work just to scan the surface insights? Is there a clear hierarchy
or sequence derived through the size and position of elements on the
page? Does it feel like there is too much or too little content-wise? If
the project layout exceeds the dimensions of your screen display, how
much more scrolling or how many different pages will you have to
look through to see the whole?
This initial thinking helps you establish how much work and effort you are
going to be faced with to explore the visualisation thoroughly. In ‘The
Pursuit of Faster’ project, it does not feel like there is too much content
and all the possible analysis seems to be located within the boundaries of
the immediate screen area. However, with a number of different selectable
tabs, interactive options and collapsible content areas lurking beneath the
surface, it could be more involving than it first appears.
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What’s this Project About?
Although you have already determined the potential relevance of this
subject matter (or otherwise) you will now look to gain a little more
insight into what the visualisation is specifically about.
Title: You will have probably already glanced at the title but now
have another look at it to see if you can learn more about the subject
matter, the specific angle of enquiry or perhaps a headline finding. In
the sample project (Figures 11.2 and 11.3), the presence of the
Olympic rings logo on the right provides an immediate visual cue
about the subject matter, as you might have observed in the initial
scan. The title, ‘The Pursuit of Faster’, is quite ambiguous, but as the
supporting subtitle reveals, ‘Visualising the evolution of Olympic
speed’ helps to explain what the visualisation is about.
Figure 11.2 Excerpt from ‘The Pursuit of Faster’
Source: If it is a web-based visualisation the URL is worth
considering. You might already know where you are on the Web, but
if not you can derive plenty from the site on which this project is
being hosted. An initial sense about trust in the data, the author and
the possible credibility of insights can be drawn from this single bit of
information. This particular project is hosted on my website,
visualisingdata.com, and so may not carry the same immediate
recognition that an established Olympics or sport-related site might
command. There is nothing provided in the main view of the
visualisation that informs the viewer who created the project.
Normally this might have been detailed towards the bottom of the
display or underneath the title, but in this case viewers have to click
on a ‘Read more…’ link to find this out. If there are no details
provided about the author/visualiser, as a viewer, this anonymity
might have any affect on your trust in the work’s motives and quality.
Introduction: While some visualisation projects will be relatively
self-explanatory, depending on the familiarity of the audience with
the subject matter, others will need to provide a little extra guidance.
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The inclusion of introductory text will often help ‘set the scene’,
providing some further background about the project. If, as the
viewer, the introduction fails to equip you with all the information
you feel you need about the visualisation, then the visualiser has
neglected to include all the assistance that might be necessary.
In ‘The Pursuit of Faster’ project, the introductory text provides sufficient
initial information about the background of the project based on a curiosity
about what improvements in speed have been seen throughout the history
of the Olympics. As mentioned, there is a ‘Read more…’ link to find more
information that was perhaps too much to include in the main opening
paragraph. This includes a comprehensive ‘How to use it’ guide providing
a detailed account about the content and role of each section of the project,
including advice on how to read the chart and utilise the interactive
features.
Figure 11.3 Excerpt from ‘The Pursuit of Faster’
What Data?
Any visualisation of data should include clear information to explain the
origin of the data and what has been done with it in preparation for its
visual portrayal.
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Data source: Typically, details of the data source will be located in
the introduction, as a footnote beneath a chart or at the bottom of a
page. It is important to demonstrate transparency and give credit to
the origin of your data. If none is provided, that lowers trust.
Data handling: It is also important to explain how the data was
gathered and what, if any, criteria were applied to include or exclude
certain aspects of the subject matter. These might also mention
certain assumptions, calculations or transformations that have been
undertaken on the data and are important for the reader to appreciate.
In ‘The Pursuit of Faster’ project, the link you saw earlier to ‘Read more
…’ provides details about the origin of the data and the fact that it only
includes medal winners from summer Olympic events that have a time-
based measure.
What Interactive Functions Exist?
As you have seen in Chapter 8, interactive visualisations (typically hosted
on the Web or in an app) aim to provide users with a range of features to
interrogate and customise the presentation of the data.
Sometimes, interactive features are enabled but not visible on the surface
of a project. This might be because visualisers feel that users will be
experienced enough to expect certain interactive capabilities without
having to make these overly conspicuous by labelling or signposting their
presence. For example, rather than show all the value labels on a bar chart
you might be able to move the mouse over a bar of choice and a pop-up
will reveal the value. The project might not tell you that you can do this,
but you may intuitively expect to. Always fully explore the display with
the mouse or through touch in order to gain a sense of all the different
visible and possibly invisible ways you can interact with the visualisation.
In ‘The Pursuit of Faster’ project (Figure 11.4), you will see multiple tabs
at the top, one for each of the four sports being analysed. Clicking on each
one opens up a new set of sub-tabs beneath for each specific event within
the chosen sport.
Figure 11.4 Excerpt from ‘The Pursuit of Faster’
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Choosing an event will present the results in the main chart area (Figure
11.5). Once a chart has loaded up, you can then filter for male/female and
also for each of the medals using the buttons immediately below the chart.
Within the chart, hovering above a marker on the chart will reveal the
specific time value for that result. Clicking on the marker will show the
full race results and offer further analysis comparing those results with the
all-time results for context.
Figure 11.5 Excerpt from ‘The Pursuit of Faster’
Finally, the collapsible menus below the chart show further detailed
analysis and comparisons within and between each sporting event (Figure
11.6). The location of this implies that it is of lower relative importance
than the chart or maybe is a more detailed view of the data.
Figure 11.6 Excerpt from ‘The Pursuit of Faster’
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Inside the Chart
Now you have acquainted yourself with the key features of a visualisation
outside the chart, the next stage is to start the process of deriving
understanding from the chart.
The process of consuming a chart varies considerably between different
chart types: the approach to drawing observations from a chart showing
trends over time is very different from how you might explore a map-
based visualisation. The charts I profiled in Chapter 6 were each
accompanied by detailed information on the type of observations you
should be looking to extract in each case.
In Chapter 1 you learnt how there were three elements involved in the
achievement of understanding a chart: perceiving, interpreting and
comprehending. Let’s work through these steps by looking at the analysis
shown for the 100m Finals.
Perceiving: The first task in perceiving a chart is to establish your
understanding about the role of every aspect of the display. Here we
have a line chart (Figure 11.7) which shows how quantitative values
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for categories have changed over time. This chart is structured around
a horizontal x-axis showing equal intervals from the earliest
Olympics (1896) on the left through to the most recent (2012) on the
right, although the latest values in the data only seem to reach 2008.
Depending on your interest in this topic, the absence of data for the
more recent Olympics may undermine your sense of its completeness
and representativeness.
Figure 11.7 Excerpt from ‘The Pursuit of Faster’
The vertical y-axis is different from what you might normally see for
two reasons. Firstly, it moves downwards below the x-axis (rather
than upwards, as is more common), and secondly, there is no
labelling, either of the variable plotted or of scale values.
I can see that the encoding is formed by points (marking the race
results) and connecting lines showing the change over time. Through
the use of colour there are plotted lines for the gold, silver and bronze
medal winning times for each Olympics. There are two sets of medal
lines but there is no obvious distinction to explain what these are.
With no direct labelling of the values I hover over the point (‘medal’)
markers and a tooltip annotation comes up with the athlete’s name
and time in a medal-coloured box. I compare tooltip info for the lines
at the top and those below and discover the lower lines are the
women’s results and the upper lines are the men’s results.
From the tooltip info I can determine that the quicker times (the gold
medal line) are at the top so this suggests that the y-axis scale is
inverted with quicker (smaller) times at the top and slower (larger)
times at the bottom. This also reveals that there is no origin of zero in
the vertical axis; rather the quickest time is anchored just below the
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top of the chart, the slowest stretches down to the bottom of the chart,
and then all the values in between are distributed proportionally.
Interjecting as the visualiser responsible for this project, let me explain
that the focus was on patterns of relative change over time, not
necessarily absolute result times. As every different event has a
different distance and duration behind the final timed results, a common
scale for all results needed to be established, which is why this decision
was taken to standardise all results and plot them across the vertical
chart space provided.
Inside the chart I now try clicking on the markers and this brings up
details about the event (for that gender), including the three medal
winners, their times and small flags for the countries they represented.
I can also read an interesting statistic that explains if the time for the
medallist I selected had been achieved throughout the event’s history,
it would have secured gold, silver or bronze medals on x number of
occasions.
I now know enough about the chart’s structure and encodings to be
able to start the process of perceiving the patterns to make some
observations about what the data is showing me:
I can see that there is a general rise across all Olympics for the
event in both men’s and women’s results.
It feels like the women’s times are getting closer to the men’s,
with Florence Griffith Joyner’s victory time in 1988 being the
closest that the respective times have been – her result there
would have been good enough for a men’s bronze in 1956.
There are no real patterns between medal times; they are neither
always more packed closely together, nor always spread out – it
changes on each occasion.
I notice the gaps where there were no events, during the First
and Second World Wars, and also the presence of an obscure
1906 event, the only Olympic Games that did not follow the
four-year interval.
Interpreting: As someone who follows a lot of sport and, like most
people, is particularly familiar with the 100m event, I feel there is a lot of
information I can get out of this display at both a general level, looking at
the relative patterns of change, and a local level, checking up on individual
medallists and their absolute values. Thinking about what these patterns
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mean, on looking at the times from the first Olympic Games in 1896 until
the 1960s there was a lot of improvement and yet, since the 1960s, there is
generally a much flatter shape – with only a gradual improvement in the
times for both genders. This tells me that maybe the threshold for the
capacity of athletes to run faster is getting closer. Even with all the
contributions of sports science over the past few generations, the increase
in speed is only ever marginal. That was until Usain Bolt blew the world
away in 2008 and, likewise for women, Shelly-Ann Fraser improved the
women’s results for the first time in 20 years.
Comprehending: What does this all mean to me? Well it is
interesting and informative and, while I have no direct investment in
this information in terms of needing to make decisions or it triggering
any sense of emotion in me, in outcome terms I feel I have learnt
more about a topic through this chart than I would have done just
looking at the data. My understanding of the history of the Olympic
100m final has been expanded and, in turn, I have a better
appreciation of the advancements in speed across and between both
genders.
Becoming a More Sophisticated Consumer
Effective visualisation requires the visualiser and viewer to operate in
harmony, otherwise the possibility of facilitating understanding is
compromised. Beyond the mechanics of perceiving a visualisation, there
are softer ‘attitudinal’ differences you can make to give yourself even
more of a chance of gaining understanding. This is about modifying your
mindset to be more critically appreciative of the challenges faced by the
visualiser responsible for producing the work as well as its intended
purpose. It is about showing empathy in your critical evaluation which will
markedly help you become an increasingly sophisticated consumer.
Appreciation of context: When consuming a visualisation try to
imagine some of the circumstances and constraints that might have
influenced the visualiser’s decisions:
You might not find the subject matter interesting, but other
people might. You have the right not to read or interact with a
visualisation that has no relevance to you. If it should have
relevance, then that’s when there may be some problems!
If you are struggling to understand a visualisation it could be that
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the project was aimed more at specialists, people with specific
domain knowledge. Your struggles are possibly not a reflection
of an ineffective visualisation or any deficit in your expected
knowledge – it just was not intended for you.
If the size of the text is frustratingly tiny on your screen, maybe
it was intended primarily for printing as a poster and would have
been the right size if consumed in its native format?
When criticising a work, spare a thought for what could have
been done differently. How would you imagine an alternative
way to represent the data? What other design solutions would
you have tried? Sometimes what is created is a reflection of
crippling constraints and might more closely resemble the least-
worst solution than the best.
Overview first, details if provided: Sometimes a visualiser only aims to
offer a sense of the big picture – the big values, the medium and the small
ones. Just because we cannot instantly read precise values from a chart it is
important to avoid getting frustrated. Our default state as viewers is often
to want every detail available. Sometimes, we just need to accept the idea
that a gist of the hierarchy of values is of more worth than the precise
decimal point precision of specific values. It may be that it was not
feasible to use a chart that would deliver such detailed reading of the data
– many charts simply cannot fulfil this. We might not even realise that we
are just a mouseover or click away from bringing up the details we desire.
False consciousness: Do you really like the things you like?
Sometimes we can be too quick to offer a ‘wow’ or a ‘how cool is
that?’ summary judgement before even consuming the visualisation
properly. It is quite natural to be charmed by a superficial surface
appeal (occasionally, dare I say it, following the crowd?). Ask
yourself if it is the subject, the design or the data you like? Could any
portrayal of that compelling data have arrived at an equally
compelling presentation of that content?
Curiosities answered, curiosities not answered: Just because the
curiosity you had about a subject is not answerable does not make the
visualisation a bad one. Statements like ‘This is great but I wish
they’d shown it by year …’ are valid because they express your own
curiosity, to which you are entirely entitled. However, a visualiser can
only serve up responses to a limited number of different angles of
analysis in one project. The things you wanted to know about, which
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might be missing, may simply have not been possible to include or
were deemed less interesting than the information provided. If you
are thinking ‘this would have been better on a map’, maybe there was
no access to spatial data? Or maybe the geographical details were too
vague or inaccurate to generate sufficient confidence to use them?
11.2 Creating: The Capabilities of the
Visualiser
Now that you are reaching the end of this journey, it will be quite evident
that data visualisation design is truly multidisciplinary. It is the variety that
fuels the richness of the subject and makes it a particularly compelling
challenge. To prepare you for your ongoing development, the second part
of this final chapter aims to help you reflect on the repertoire of skills,
knowledge and mindsets required to achieve excellence in data
visualisation design.
The Seven Hats of Data Visualisation
Inspired by Edward de Bono’s Six Thinking Hats, the ‘Seven hats of data
visualisation’ is a breakdown of the different capabilities that make up the
multi-talented visualiser. The attributes listed under each of these hats can
be viewed as a wish-list of personal or team capabilities, depending on the
context of your data visualisation work.
Project Manager
The coordinator – oversees the project
Initiates and leads on formulating the brief
Identifies and establishes definitions of key circumstances
Organises the resources according to the ambition of a project
Manages progress of the workflow and keeps it cohesive
Has a ‘thick skin’, patience and empathy
Gets things done: checks, tests, finishes tasks
Pays strong attention to detail
Communicator
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The broker – manages the people relationships
Helps to gather and understand requirements
Manages expectations and presents possibilities
Helps to define the perspective of the audience
Is a good listener with a willingness to learn from domain experts
Is a confident communicator with laypeople and non-specialists
Possesses strong copy-editing abilities
Launches and promotes the final solution
Scientist
The thinker – provides scientific rigour
Brings a strong research mindset to the process
Understands the science of visual perception
Understands visualisation, statistical and data ethics
Understands the influence of human factors
Verifies and validates the integrity of all data and design decisions
Demonstrates a system’s thinking approach to problem solving
Undertakes reflective evaluation and critique
Data Analyst
The wrangler – handles all data work
Has strong data and statistical literacy
Has the technical skills to acquire data from multiple sources
Examines the physical properties of the data
Undertakes initial descriptive analysis
Transforms and prepares the data for its purpose
Undertakes exploratory data analysis
Has database and data modelling experience
Journalist
The reporter – pursues the scent of an enquiry
Defines the trigger curiosity and purpose of the project
Has an instinct to research, learn and discover
Driven by a desire to help others understand
Possesses or is able to acquire salient domain knowledge
Understands the essence of the subject’s data
Has empathy for the interests and needs of an audience
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Defines the editorial angle, framing and focus
Designer
The conceiver – provides creative direction
Establishes the initial creative pathway through the purpose map
Forms the initial mental visualisation: ideas and inspiration
Has strong creative, graphic and illustration skills
Understands the principles of user interface design
Is fluent with the full array of possible design options
Unifies the decision-making across the design anatomy
Has a relentless creative drive to keep innovating
Technologist
The developer – constructs the solution
Possesses a repertoire of software and programming capabilities
Has an appetite to acquire new technical solutions
Possesses strong mathematical knowledge
Can automate otherwise manually intensive processes
Has the discipline to avoid feature creep
Works on the prototyping and development of the solution
Undertakes pre- and post-launch testing, evaluation and support
Assessing and Developing Your Capabilities
Data visualisation is not necessarily a hard subject to master, but there are
plenty of technical and complicated matters to handle. A trained or natural
talent in areas like graphic design, computer science, journalism and data
analysis is advantageous, but very few people have all these hats. Those
that do cannot be exceptional at everything listed, but may be sufficiently
competent at most things and then brilliant at some. Developing mastery
across the full collection of attributes is probably unachievable, but it
offers a framework for guiding an assessment of your current abilities and
a roadmap for the development of any current shortcomings.
I am painfully aware of the things I am simply not good enough at
(programming), the things I have no direct education in (graphic design)
and the things I do not enjoy (finishing, proofreading, note-taking).
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Compromise is required with the things you do not like – there are always
going to be unattractive tasks, so just bite the bullet and get on with them.
Otherwise, you must seek either to address your skills gap through
learning and/or intensive practice, finding support from elsewhere through
collaboration, or to simply limit your ambitions based on what you can do.
Regardless of their background or previous experience, everyone has
something to contribute to data visualisation. Talent is important, of
course, but better thinking is, in my view, the essential foundation to focus
on first. Mastering the demands of a systems’ thinking approach to data
visualisation – being aware of the options and the mechanics behind
making choices – arguably has a greater influence on effective work.
Thereafter, the journey from good to great, as with anything, involves hard
work, plenty of learning, lots of guidance and, most importantly, relentless
practice.
‘Invariably, people who are new to visualisation want to know where to
begin, and, frankly, it’s understandably overwhelming. There is so
much powerful work now being done at such a high level of quality,
that it can be quite intimidating! But you have to start somewhere, and I
don’t think it matters where you start. In fact, it’s best to start wherever
you are now. Start from your own experience, and move forward. One
reason I love this field is that everyone comes from a different
background – I get to meet architects, designers, artists, coders,
statisticians, journalists, data scientists … Data vis is an inherently
interdisciplinary practice: that’s an opportunity to learn something about
everything! The people who are most successful in this field are curious
and motivated. Don’t worry if you feel you don’t have skills yet; just
start from where you are, share your work, and engage with others.’
Scott Murray, Designer
The Value of the Team
The idea of team work is important. There are advantages to pursuing data
visualisation solutions collaboratively, bringing together different abilities
and perspectives to a shared challenge. In workplaces across industries and
sectors, as the field matures and becomes more embedded, I would expect
to see a greater shift towards recognising the need for interdisciplinary
teams to fulfil data visualisation projects collectively.
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The best functioning visualisation team will offer a collective blend of
skills across all these hats, substantiating some inevitably, but also,
critically, avoiding skewing the sensibilities towards one dominant talent.
Success will be hard to achieve if a team comprises a dominance in
technologists or a concentration of ‘ideas’ people whose work never
progresses past the sketchbook. You need the right blend in any team.
We have seen quite a lot of great examples of visualisation and infographic
work from newspaper and media organisations. In the larger organisations
that have the fortune of (relatively) large graphics departments, team
working is an essential ingredient behind much of the success they have
had. Producing relentlessly high-quality, innovative and multiple projects
in parallel, within the demands of the news environment, is no mean feat.
Such organisations might have the most people and also some of the best
people, but their output is still representative of their punching above their
weight, no matter how considerable that base.
Developing Through Evaluating
There are two components in evaluating the outcome of a visualisation
solution that will help refine your capabilities: what was the outcome of
the work and how do you reflect on your performance?
Outcome: Measuring effectiveness in data visualisation remains an
elusive task – in many ways it is the field’s ‘Everest’ – largely
because it must be defined according to local, contextual measures of
success. This is why establishing an early view of the intended
‘purpose’, and then refining it if circumstances change, was necessary
to guide your thinking throughout this workflow.
Sometimes effectiveness is tangible, but most times it is entirely
intangible. If the purpose of the work is to further the debate about a
subject, to establish one’s reputation or voice of authority, then those
are hard things to pin down in terms of a yes/no outcome. One option
may be to flip the measure of effectiveness on its head and seek out
evidence of tangible ineffectiveness. For example, there may be
significant reputation-based impacts should decisions be made on
inaccurate, misleading or inaccessible visual information.
There are, of course, some relatively free quantitative measures that
are available for digital projects, including web-based measures such
as visitor counts and social media metrics (likes, retweets, mentions).
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These, at least, provide a surface indicator of success in terms of the
project’s apparent appeal and spread. Ideally, however, you should
aspire also to collect more reliable qualitative and value-added
feedback, even if this can, at times, be rather expensive to secure.
Some options include:
capturing anecdotal evidence from comments submitted on a
site, opinions attributed to tweets or other social media
descriptors, feedback shared in emails or in person;
informal feedback through polls or short surveys;
formal case studies which might offer more structured interviews
and observations about documented effects;
experiments with controlled tasks/conditions and tracked
performance measures.
Your performance: A personal reflection or assessment of your
contribution to a project is important for your own development. The best
way to learn is by considering the things you enjoyed and/or did well (and
doing more of those things) and identifying the things you did not enjoy/do
well (and doing less of those things or doing them better). So look back
over your project experience and consider the following:
Were you satisfied with your solution? If yes, why; if no, why and
what would you do differently?
In a different context, what other design solutions might you have
considered?
Were there any skill or knowledge shortcomings that restricted your
process and/or solution?
Are there aspects of this project that you might seek to recycle or
reproduce in other projects? For instance, ideas that did not make the
final cut but could be given new life in other challenges?
How well did you utilise your time? Were there any activities on
which you feel you spent too much time?
Developing effectiveness and efficiency in your data visualisation work
will take time and will require your ongoing efforts to learn, apply, reflect
and repeat again. I am still learning new things every day. It is a journey
that never stops because data visualisation is a subject that has no ending.
‘There is not one project I have been involved in that I would execute
exactly the same way second time around. I could conceivably pick any
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of them – and probably the thing they could all benefit most from?
More inter-disciplinary expertise.’ Alan Smith OBE, Data
Visualisation Editor, Financial Times
However, to try offer a suitable conclusion to this book, at least, I will
leave you with this wonderful bit of transcribed from a video of Ira Glass,
host and producer of ‘This American Life’.
Nobody tells this to people who are beginners, I really wish someone
had told this to me. All of us who do creative work, we get into it
because we have good taste… [but] there is this gap and for the first
couple of years that you’re making stuff, what you’re making is just
not that good… It’s trying to be good, it has potential, but it’s not.
But your taste, the thing that got you into the game, is still killer. And
your taste is why your work disappoints you. A lot of people never
get past this phase, they quit. Most people I know who do interesting,
creative work went through years of this. We know our work doesn’t
have this special thing that we want it to have. We all go through this.
And if you are just starting out or you are still in this phase, you gotta
know it’s normal and the most important thing you can do is do a lot
of work. Put yourself on a deadline so that every week you will finish
one story. It is only by going through a volume of work that you will
close that gap, and your work will be as good as your ambitions. And
I took longer to figure out how to do this than anyone I’ve ever met.
It’s gonna take awhile. It’s normal to take awhile. You’ve just gotta
fight your way through.
Summary: Visualisation Literacy
Viewing: Learning to See
Before You Begin
Setting: is the situation you are in conducive to the task of consuming
a visualisation? In a rush? Travelling?
Visual appeal: are you sufficiently attracted to the appearance of the
work?
Relevance: do you have an interest or a need to engage with this
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topic?
Initial scan: quickly orientate yourself around the page or screen, and
allow yourself a brief moment to be drawn to certain features.
Outside the Chart
The proposition: what task awaits? What format, function, shape and
size of visualisation have you got to work with?
What’s the project about?: look at the titles, source, and read through
any introductory explanations.
What data?: look for information about where the data has originated
from and what might have been done to it.
What interactive functions exist?: if it is a digital solution browse
quickly and acquaint yourself with the range of interactive devices.
Inside the Chart Refer to the Chart Type Gallery in Chapter 6 to learn
about the approaches to perceiving and interpreting different chart types.
Perceiving: what does it show?
Interpreting: what does it mean?
Comprehending: what does it mean to me?
Becoming a More Sophisticated Consumer
Appreciation of context: what circumstances might the visualiser
have been faced with that are hidden from you as a viewer?
Overview first, details if provided: accept that sometimes a project
only aims to (or maybe only can) provide a big-picture gist of the
data, rather than precise details.
False consciousness: don’t be too quick to determine that you like a
visualisation. Challenge yourself, do you really like it? Do you really
gain understanding from it?
Curiosities answered, curiosities not answered: just because it does
not answer your curiosity, it might answer those of plenty of others.
Creating: The Capabilities of the Visualiser
The Seven Hats of Data Visualisation Design
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Project Manager: the coordinator – oversees the project.
Communicator: the broker – manages the people relationships.
Scientist: the thinker – provides scientific rigour.
Data analyst: the wrangler – handles all the data work.
Journalist: the reporter – pursues the scent of enquiry.
Designer: the conceiver – provides creative direction.
Technologist: the developer – constructs the solution.
Assessing and Developing Your Capabilities
The importance of reflective learning: evaluating the outcome of the
work you have created and assessing your own performance during
its production.
Tips and Tactics
The life and energy of data visualisation are online: keep on top of
blogs, the websites of major practitioners and agencies creating great
work. On social media (especially Twitter, Reddit) you will find a
very active and open community that is willing to share and help.
Practise, practise, practise: experience is the key – identify personal
projects to explore different techniques and challenges.
Learn about yourself: take notes, reflect, self-critique, recognise your
limits.
Learn from others: consume case studies and process narratives,
evaluate the work of others (‘what would I do differently?’).
Expose yourself to the ideas and practices of other related creative
and communication fields: writing, video games, graphic design,
architecture, cartoonists.
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References
These references relate to content mentioned in the body text and/or
attributed quotes that do not come from individual interviews with the
author. Extensive further reading lists to support each chapter’s content are
provided in the companion digital resources.
Bertin, Jacques (2011) Semiology of Graphics: Diagrams, Networks,
Maps. Redlands, CA: ESRI Press.
Beveridge, Harriet and Hunt-Davis, Ben (2011) Will it Make the Boat Go
Faster? Olympic-winning Strategies for Everyday Success. Leicester:
Matador.
Booker, Christopher (2004) The Seven Basic Plots: Why We Tell Stories.
New York: Continuum.
Buckle, Catherine (2014) Millions, billions, trillions: Letters from
Zimbabwe, 2005−2009. http://www.cathybuckle.com/Millions-Billions-
Trillions.php
Butterick, Matthew (2013) Practical Typography.
http://practicaltypography.com
Buxton, Bill (2007) Sketching User Experiences. San Francisco, CA:
Elsevier.
Cairo, Alberto (2012) The Functional Art. San Francisco, CA: Peachpit.
Chimero, Frank (2012) The Shape of Design.
http://shapeofdesignbook.com/
Cleveland, William S. and McGill, Robert M. (1984) ‘Graphical
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http://www.cathybuckle.com/Millions-Billions-Trillions.php

http://practicaltypography.com

http://shapeofdesignbook.com/

Perception: Theory, Experimentation, and Application to the
Development of Graphical Methods’. Journal of the American
Statistical Association, vol. 79, no. 387, pp. 531–54.
Coats, Emma (2011) Originally via Twitter, collated via
http://www.aerogrammestudio.com/2013/03/07/pixars-22-rules-of-
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550

Index
Titles of charts are printed in italics
absent data 115, 275
accessibility 30, 37–42, 218–219
and angles of analysis 133
of annotation 259–260
audience influence on 38–41
and colour 265, 275, 286–288
in composition 307–309
of data 108, 115
of interactive design 223, 243, 244
and purpose 83
testing 147
and visual impairment 244
visualiser’s influence on 41–42
see also audiences
Accurat viii, 252
aesthetic qualities see elegance
aims/uses of this book 3, 5, 6–7
Aisch, Gregor 29, 252
Aisch, Gregor and Kevin Quealy (The New York Times) viii, 138–140
Al-Jamea, Sohail, Wilson Andrews, Bonnie Berkowitz and Todd
Lindeman (Washington Post) xi, 231
Albers Equal-area Conic projection 308
analysis vs communication 8–9
Andrews, Wilson, Amanda Cox et al vii, 84
angle/slope as attribute 153
angles of analysis 116, 132–134, 136, 216
and composition 303
number of 133–134
relevance 132–133
animation 241, 242
speed 241
annotation 137, 247–261
absence of 37, 42
audiences 258
551

captions 255–256
chart apparatus 252, 253
clutter 260
focus 259
footnotes 251
headings, subheadings and titles 248
imagery 249, 250
introductions 248–249
labels 47, 252–254
legends 254–255
multimedia 249–251, 250
attribution 250
credits 250
data sources 250
integration 250–251
time/date stamps 250
usage 250
project annotations 260
reading guides 251–252
tone and experience 258–259
transparency 259
typography 256–257
understanding 259–260
user guides 249
voiceover 255–256
area marks 153
Art in the Age of Mechanical Reproduction: Walter Benjamin 279
Arteries of the City 206
Asia Loses Its Sweet Tooth for Chocolate 45
attention to detail 58–59
attributes and marks 21, 151–152
audiences 1, 70
and annotation 258
and anticipated intrigue 67
and data handling 36
definition 49
feelings and emotion 85
interests 40, 70, 85
moods and attitudes 41
needs of 38, 39, 40, 66, 70, 133
552

personal tastes 41
and relevance 133
size of 75
stakeholders 66
of this book 3
prerequisites 4–5
time needed to view and understand 40
understanding/knowledge 39–41, 70
see also accessibility
axes 51
line charts 305
truncated 37, 304–305
bandings charts 218
bar charts 31, 51, 161, 215
Battling Infectious Diseases in the 20th Century: The Impact of
Vaccines 273–274
Beer Brands of SAB InBev 181
Benjamin Walter 278, 279
Berkowitz, Bonnie, Emily Chow and Todd Lindeman (Washington
Post)
Berliner Morgenpost xiii, 268
Bertin, Jacques 212
bias 36, 37
Big Data 50
black and white printing 282–283
Bloch, Matthew, Lee Byron, Shan Carter and Amanda Cox (New
York Times) x, 197
Bloomberg Billionaires 155, 215, 250, 251
Bloomsberg Visual Data vii, ix, x, 101, 166, 194, 203, 218, 251
Bocoup and the World Economic Forum ix, 168
Boice, Jay, Aaron Bycoffe, Andrei Scheinkman and Simon Jackman
x, 208
Bolt, Usain 325
Booker, Christopher 1
Bostock, Mike and Jason Davies x, 182
Bostock, Mike and Shan Carter and Kevin Quealy (New York Times)
x, 198
Bostock, Mike, Shan Carter (New York Times) xi, 233
brushing 234, 235
553

Buckle, Catherine, and Graham van de Ruit xiv, 303
Buckminster Fuller, Richard 43
Bui, Quoctrung viii, 136
bullet charts 218
Bump, Phillip (Washington Post) ix, 180
Burn-Murdoch, John 85
Butterick, Matthew 257
Buxton, Bill 68
Buying Power: The Families Funding the 2016 Presidential Election
84
Cairo, Alberto 29, 64
Cameos, Jorge 29
Carbon Map 207
Carli, Luis vii, 79
cartesian charts 304
Casualties 279–280
categorical attributes 154
categorical charts 158
Census Bump: Rank of the Most Populous Cities at Each Census,
1790-1890 192
central tendency 113
challenges 10
Chang, Kai ix, 185
Chart Structures 304
chart types 21, 42, 50–51, 126, 137, 157–160
acronym CHRTS 157, 158, 216, 220
choosing 210–220, 243
accessibility 218–219
angles of analysis 216
assessing chart types 211
data examination 215
data exploration 215
elegance 219–220
perception 214
purpose 211–212
ranking of tasks 212, 213
skills and tools 211
tone 212
trustworthiness 216–218
554

examples
area cartogram 207
area chart 195
back-to-back bar chart 178
bar chart 161
box-and-whisker plot 171
bubble chart 167
bubble plot 184
bump chart 192, 212
chord diagram 189, 299
choropleth map 201, 271
clustered bar chart 162
connected dot plot 164
connected scatter plot 194
connected timeline 198
dashboards 159
dendrogram 181
Dorling cartogram 208
dot map 205
dot plot 161, 162, 163
dual families 159
flow map 206
Gantt chart 199
grid map 209
heat map 186
histogram 173
horizon chart 196
instance chart 200
isarithmic map 202
line chart 191, 305
matrix chart 187
node–link diagram 188
parallel coordinates 185
pictogram 165
pie chart 37, 157, 158, 175, 303
polar chart 169, 304
prism map 204
proportional shape chart 166
proportional symbol map 203
radar chart 168
555

Sankey diagram 190, 211, 299
scatter plot 137, 183
slope graph 193, 299
stacked bar chart 177, 270
stream graph 197
sunburst chart 182
treemaps 45, 88–89, 179, 303
univariate scatter plot 172
Venn diagram 180
waffle chart 176
word clouds 158, 174
storytelling 159–160
text visualisation 159
Charting the Beatles: Song Structure 269
Chen, Lina and Anita Rundles viii, 155
Cheshire, James,Ed Manley, John Barratt and Oliver O’Brien xi, 238
Chimero, Frank 43
Christiansen, Jan 309
circle size encoding 216–217
City of Anarchy 294
Ciuccarelli, Paolo 113
Clark, Duncan and Robin Houston (Kiln) ix, 182, 207
Cleveland, William and McGill, Robert 212, 214
Clever, Thomas 44, 250
clutter 260
Coal, Gas, Nuclear, Hydro? How Your State Generates Power 193
Coats, Emma 59
Color of Debt 249, 250
colour 77, 91, 263–291
accessibility
colour associations 287–288
colour incongruence 288
consistency 286
cultural sensitivities 288
visual 286–287
black and white printing 282–283
categorical colours 267–269, 268
chart-type choice 285
choices 33–34, 42, 137
CIELAB 266
556

CMYK (Cyan, Magenta, Yellow and Black) 265, 282
contrast 276–278, 280
data encoding 285
data examination 284
diverging/converging scales 269, 270–272
rainbow scale 274–275
editorial salience 276–278
elegance
justification for colour use 289
neutral colouring 288–289
unity 288
format 282–283
functional harmony 278–282
annotations 280–281
composition 281–282
interactivity 279
multimedia 280–281
greyscale 280
HSL (Hue, Saturation, Lightness) 265, 266, 267
hue 213, 265
ideas and inspiration 283–284
illusions 286
inappropriate usage 37
interval and ratio 270–276
lightness 266, 267
meaningfulness 285
nominal data 267–269
ordinal data 269–270
purpose map 283
quantitative data 270–276
RBG (Red, Blue, Green) colour model 264, 265, 266, 282
saturation 153, 265, 266
setting 283
style guidelines of organisations 283
and texture 269
theory 264–267
white background 283
colour blindness 286–287
Colour-blind Friendly Alternatives to Red and Green 287
columnar charts 304
557

communication 58, 149
comparison of judging line size vs area size 213
Comparison of Judging Related Items using Variation in Colour
(hue) vs Variation in Shape 214
completion 148
complex subjects 39, 41
complexity of data visualisation 2
complicated subjects 39
composition 138, 293–311
angles of analysis 303
aspect ratios 306–307
chart composition 295–301
acronym LATCH 298–299
chart orientation 297–298
chart scales 296–297
chart size 296
value sorting 298–301
chart type choice 303–304
chart-scale optimisation 304–306
data examination 302–303
elegance
thoroughness 309
unity 309
format 302
mapping projections 307
project composition 293–295
hierarchy of content 294
hierarchy and size 294
unobtrusive design 307, 308–309
comprehending 22, 23, 26–27, 74, 79
conceiving ideas 144, 145
consumption
definition 49
frequency 71
settings 72
context 36, 64–75
audiences 70
circumstances 68–69
constraints and limitations 74
consumption 71–72
558

curiosity 64–68
format 73
pressures 70–71
purpose 74–75
quantities/workload 72
resources 73–74
rules 71
stakeholders 69
correlations 52
Corum, Jonathan (New York Times) and Nicholas D. Pyenson xiii,
281
Countries with the Most Land Neighbours 83
Cox, Amanda 83
Crawford, Kate 36
creation: definition 49
Crime Rates by State 184
critical friends 147
critical thinking 7–8, 10
Critics Scores for Major Movie Francises 172
Crude Oil Prices 195
Culp, S. xiv, 297
curiosity 64–68, 131
anticipated intrigue 67
audience intrigue 66
personal intrigue 65
potential intrigue 67–68
and purpose 75
stakeholder intrigue 66
Current Electricity Prices in Switzerland 271
Daily Indego Bike Share Station Usage 272
Daily Mail 33–34
data 97–129
absence 275
acquisition 36, 106–110, 128
quantity needed 106–107
resolution 107
sources 50, 107–110
API (Application Programme Interface) 110
foraging 108
559

issued by client 109
pdf files 108
raw (primary) data 108
system report or export 109
third-party services 109–110
web 108–109
when available 110
data literacy 97–98
examination 110–117, 128
absence of data 115
animation 241
and choice of chart types 215
completeness 115–116
data operations 112
identification of type 111
influence of examination 116–117
inspection and scanning 112
for interactivity 241
meaning of data 113–114
quality 111
size 111
statistical methods 112–113
underlying phenomena of data 114
exploratory data analysis (EDA) 121–128
analyst instinct 124–125
chart types 126
choosing chart types 215
datasets 127
efficiency 125
finding nothing 127
interrogating the data 125
knowns and unknowns 122–124, 123
machine learning 127
need 128
reasoning 125
research 126–127
statistical methods 127
filtering 134
fundamental role in visualisation 20
and goals 121, 124
560

‘open data’ 108
range 117
raw (primary) 49–50, 99, 108
representativeness 115
samples 115
source 322
statistics 98, 105–106
in data examination 112–113
inference techniques 105
univariate and multivariate techniques 105
tabulated datasets 99
cross-tabulated datasets 99, 100
normalised datasets 99
transformation 118–121, 128, 141
backups 118
cleaning 118–119
consolidation 121
conversions 119–121
example 119
quantitative 120
textual data 119–120
creating 120–121
junk 119
transparency in handling 36
types 20, 100–105, 111
acronym TNOIR 100, 128
discrete and continuous 104–105
qualitative
nominal 101–102
ordinal 102–103
textual 100–101, 119–120
quantitative
interval 103
ratio 103–104
temporal 104
data art 48
data journalism 48
data privacy 233
data representation 19, 21, 151–221
chart types 157–160, 161–209
561

choosing chart types 210–220
deception 36, 36–37, 216–218
definition 19
unfamiliar 40
visual encoding 151–157, 152–156
data science 48
data visualisation: definition 19–20
and data vis 47
datapresentation
forms of deception 37
datasets: definition 50
Deal, Michael xiii, 269
deception 36, 36–37, 216–218
decisions/choices 1, 8, 10, 29
heuristic judgements 57
significance 53
and truth 32
see also chart types; design
decoration 44–46
deductive reasoning 125
D’Efilippo, Valentina and James Ball xii, 254
D’Efilippo, Valentina and Nicolas Pigelet viii, 146
D’Efilippo, Valentina 149
depth 41
design 28
Dieter Ram’s general principles 37, 41, 42
environmentally friendly 31
guiding principles 29–30, 146–147
innovative design 30
‘invisibility’ of 244–245
long lasting 31
rules 71
see also chart types
deuteranopia 286–287
diagrams 51
digital resources 9
Dilnott, Andrew 32
Dimensional Changes in Wood 79–80
distortions created by 3D decoration 217–218
Doping under the Microscope 306
562

duration of task 70
dynamic of need 39
Ebb and Flow of Movies: Box Office Receipts 1986-2008 197
ECB Bank Test Results 236
Economic Research Service (USDA) xiv, 297
editorial thinking 36, 131–142, 139, 140
angle/s 132–134, 136, 138–139, 142, 216
annotation 137, 141
colour 137, 139, 141
and design choices 137–141, 140–141
example: Fall and Rise of U.S. Inequality, in Two Graphs
135–138, 136
example: Why Peyton Manning’s Record Will Be Hard to Beat
138–141, 139, 140
focus 135, 137, 139, 142
framing 134, 136–137, 139, 142
influences 135–141
interactivity 137, 140–141
representation 137, 140
Election Dashboard 208
Elections Performance Index 278
elegance 30, 42–46, 147
in composition 309
decoration 44–46
definition 42–43
eliminating the arbitrary 43–44
in interactive design 244–245
style 44–46
thoroughness 44
visual appeal 219–220
Elliot, Kennedy 35, 55
Elliot, Kennedy, Ted Mellink and Richard Johnson (Washington
Post) xi, 237
emptiness/nothingness, representations of 275, 282
enclosure charts 304
encoded overlays 78, 218, 277
environmentally friendly design 31
ER Wait Watcher: Which Emergency Room Will You See the Fastest?
299, 300
563

errors 58–59
Excel 112, 239
Executive Pay By Numbers 267
exhibitory visualisation 25, 76, 77, 81–82, 259
experimental 361
explanatory visualisation 25, 76, 77–79, 241, 258
exploratory visualisation 76, 77, 79–80, 241, 259
expressiveness 210–211
facilitating understanding 21, 28, 38
see also accessibility
Fairfled, Hannah 67
Fairfield, Hannah and Graham Roberts (New York Times) xi, 219
Fall and Rise of U.S. Inequality, in 2 Graphs 191
Fast-food Purchasers Report More Demands on Their Time 297
Fedewa, Peter A. vii, 35
feedback 147–148
Few, Stephen 29
Fewer Women Run Big Companies Than Men Named John 276, 296
figure-ground perception 34
filtering 134
financial restraints 70
Financial Times 85
Finviz 225, 226
First Fatal Accident in Spain on a High-Speed Line 280
fit 117
Five Hundred and Twelve Paths to the White House 233
FiveThirtyEight vii, 78, 298
flat design 31
Florida: Murders by Firearms 34–35, 114
flow maps 160
focus 135, 137, 139, 142
fonts 256, 257
Football Player Dashboard 277
footnotes 251
For These 55 Marijuana Companies, Every Day is 4/20 166
form marks 153
formats 41, 42, 51, 88
restrictions 69
formulating briefs 36, 63–95
564

context 36, 64–75
curiosity 64–68
definitions 63, 64
establishing vision 75–94
framing 134, 136–137, 139, 142, 225–226
Fraser, Shelly-Ann 325
Frequency of Words Used in Chapter 1 of This Book 174
fun 84, 85
functional restrictions 71
functionality 43
functions 51
Gagnon, Francis xiv, 297
Gallup xiv, 306
gateway layers 89
Gemignani, Zach 39
Gender Pay Gap US? 164, 251, 301
Geography of a Recession 234, 275–276
geometric calculations 216–217
geometric zoom 226
Glass Ceiling Persists 296, 297
Glass, Ira 332
Global Competitveness Report 2014-2015 168
Global Flow of People 189
Goddemeyer, Daniel, Moritz Stefaner, Dominikus Baur and Lev
Manovich xiv, 299
Goldsberry, Kirk 114
Gore, Al 82
grahics 51
Grape Expectations 91
Graphic Language: The Curse of the CEO 101
graphs 51
Gray, Marcia 98, 316
greyscale 280
Groeger, Lena 41, 148
Groeger, Lena, Mike Tigas and Sisi Wei (ProPublica) xiv, 300
Groskopf, Christopher, Alyson Hurt and Avie Schneider x, 193
guiding principles
accessibility 37–42
elegance 42–46
565

trustworthiness 30, 32–37
Gun Deaths in Florida 34, 286
Gun Deaths in Florida redesign 35
Harper, Bryce 231
Hemingway, Ernest 160
Here’s Exactly Where the Candidates’ Cash Came From 203
heuristic techniques 57
HEX codes 265
hierarchical charts 158
Highest Max Temperatures in Australia (1st to 14th January 2013)
274
historical context 8
History Though the President’s Words 237, 240
Hobbs, Amanda viii, 163
Hobbs, Amanda 40, 58
Holdouts Find Cheapest Super Bowl Tickets Late in the Game 194,
252
Holmes, Nigel 93, 285
home owners
Falling Number of Young Homeowners 33–34
Housing and Home Ownership in the UK 33
Horse in Motion 243
How Americans Die 230, 237
How the ‘Avengers’ Line-up Has Changed Over the Years 186, 200
How Big Will the UK Population be in 25 Years’ Time? 234
How the Insane Amount of Rain in Texas Could Turn Rhode Island
Into a Lake 156
How Long Will We Live – And How Well? 183, 268
How Nations Fare in PhDs by Sex 163, 268
How Old Are You? 233
How Well Do You Know Your Area? 232
How Y’all, Youse and You Guys Talk 80, 202
Hubley, Jill xiii, 284
Hunt-Davis, Ben 30
Hurt, Alyson 70, 125, 127
ideas
conceiving 145
keywords 91–92
566

limitations 93
mental visualisation 90–91
other people’s 94
research and inspiration 93
Sketch by Giorgia Lupi 92
sketching ideas 92, 145
sources of imagery 93
If Vienna Would be an Apartment 45
Image From the Home Page of visualisingdata.com 156
Images from Wikipedia Commons 308
Impact of colour blindness 286
inductive reasoning 125
inference techniques 105
info-posters 47
Infographic History of the World 254
infographics 46, 47–48
information design 48
information visualisation 47
Ingold, David, Keith Collins and Jeff Green vii, 101
Ingraham, Christopher (Washington Post) viii, 156
innovative design 30
Inside the Powerful Lobby Fighting for Your Right to Eat Pizza 220
Interactive Fixture Molecules 187
interactivity 21, 42, 51, 137, 223–246
advantages 223
data adjustments
animating 228–229, 241, 242, 243
contributing data 232–233
framing 225–226, 242
navigating 226–228
sequencing 230–231
event, control and function 224
influencing factors
angle 241
chart-type choice 243
data examination 241
ease of usability 244
feature creep 244
format 239–240
fun 245
567

purpose map 241
setting 239
skills and resources 238–239
timescales 239
trustworthiness 243
visual accessibility 244
presentation adjustments
annotating 235–236
focusing 234–235
orientating 236–238
usefulness 223, 244
interests of audiences 40
interpreting 22–23, 24–26, 74, 79, 126
factors 25
and previous knowledge 25–26, 27
Iraq’s Bloody Toll 34, 35, 298
Jaws (film) 74
Jenkins, Nicholas and Scott Murray xii, 249
Jones, Ben x, 199
Jordan, Chris 87
Kahneman, Daniel 90–91, 317
Kane, Wayne 25, 26
Kasich Could Be The GOP’s Moderate Backstop 298
Katz, Josh (New York Times) vii, 80
Keegan, Jon (Wall Street Journal) ix, 186
Killing the Colorado: Explore the Robot River 238
Kindred Britain 249
Kirk, Andy 289, 301, 318–324
Kirk, Andy and Andy Witherley xiv, 318–324
Klein, Matthew C. and Bloomberg Visual Data xi, 230
knowns and unknowns 122–124, 123
Kosara, Robert 263
labels 47, 252–254
axis labels 252, 253
axis titles 252
categorical labels 253–254
value labels 253–254
568

Lambert Azimuthal Equal-area 308
Lambrechts, Maarten (Mediafin) ix, 181
launching 144, 148–149
layouts 37, 71
legends 51
levels of data see data, types
Life Cycle of Ideas 152
line charts 140, 157
aspect ratios 37, 306–307
truncated axes 305
line marks 153
linking data 234, 235
Lionel Messi: Games and Goals for FC Barcelona 23, 24, 25, 27, 28,
155, 156
listening 58
Literacy Proficiency 177
London is Rubbish at Recycling and Many Boroughs are Getting
Worse 209
long lasting design 31
Losing Ground 89, 90, 239
Lunge Feeding 281
Lupi, Giorgia viii, 46, 92, 295
Lustgarten, Abrahm, Al Shaw, Jeff Larson, Amanda Zamora, Lauren
Kirchner and John Grimwade xii, 238
McCandles, David 29
McCandles, David, Miriam Quick and Philippa Thomas xii, 251
McCandles, David and Tom Evans xi, 233
McKinlay, J.D. x, 213
Mackinley, Jock 212
McLean, Kate 113, 147
managing progress 56
maps/mapping 37, 51, 157, 216
projections 307, 308
thematic 307
zooms 226
see also under chart types
markers overlays 219
market influences 71
Marshall, Bob, The Lens, Brian Jacobs and Al Shaw (ProPublica) vii,
569

89
Martin, Andrew and Bloomberg Visual Data xi, 220
Martino, Mauro, Clio Andris, David Lee, Marcus J. Hamilton,
Christian E. Gunning and John Armistead Selde ix, 188
meaning 74
Meirelles, Isabel 38
memorability 31
Mercator projection 307, 308
Messi, Lionel 23, 24, 25, 27, 28
Mider, Zach, Christopher Cannon, and Adam Pearce (Bloomberg
Visual Data) x, 203
Minard, Charles Joseph 8
Mizzou’s Racial Gap Is Typical On College Campuses 77–78, 253
mock-ups see prototypes
Model Projections of Maximum Air Temperatures Near the Ocean
and Land Surface on the June Solstice in 2014 and 2099 231
Mollweide projection 308
Morton, Jill 278
mouse/trackpad events 224
Movies Starring Michael Caine 173
multiple assets 88
multivariate analysis 127
Munsell, Albert 265
Munzner, Tamara 29
Murray, Scott 69, 330
Muybridge, Eadweard xii, 243
MyCuppa Mug 269
narrative visualisation 78
National Public Radio (NPR) 135, 136
Native and New Berliners – How the S-Bahn Ring Divides the City
201
needs of audiences 38, 39, 40, 66, 70, 133
Nelson, John 114, 126
network diagrams 159, 160
NFL Players: Height and Weight Over Time 229
Nightingale, Florence 8
Nobel Laureates 234
Nobels no Degrees 298
non-linear logarithmic scales 302
570

note-taking 57–58
Nutrient Contents 185
NYC Street Trees by Species 284, 285
NYPD, Council Spar Over More Officers 277
Obama, Barack 114
Obama’s Health Law: Who Was Helped Most 271–272
Obesity Around the World 226, 228
objectives of this book 9–11
objectivity 32
OECD Better Life Index 89–90, 116, 117, 215, 233
Olson, Randy xiii, 272
On Broadway 299
On NSA, 30 Percent Either Want No Limits on Surveillance or Say
‘Shut It Down’ 178
ONS Digital Content team vii, 33
‘open data’ 108
organisation and contents of this book 11–15
Ortiz, Santiago 90
outliers 51, 117
Peek, Katie 58, 258
pen and paper 57
Per Capita US Cheese Consumption 20
perceiving 22, 23–24, 26, 74, 79
Percentage Change in Price for Select Food Items, Since 1990 196
perfection, pursuit of 53–54
personal tastes 41
pie charts 37, 157, 158, 175, 215
plagiarism 93
planning 56
Playfair, William 8
plots 51
Plow 242
point marks 153
Political Polarization in the American Public 170
Pong, Jane 73, 93
Pong, Jane (South China Morning Post) xiv, 300
Posavec, Stefanie 43, 93, 145, 278
PowerPoint 239
571

pragmatic approach 7–8, 54
precision 216
prerequisites for data visualisation 4–5
presentation of work in progress 70
Presidential Gantt Chart 199
pressures 70–71
print 73
production cycle 56, 144–149
conceiving ideas 144, 145
launching 144, 148–149
prototypes 144, 146
refining and completion 144, 148
testing 144, 146–148
wireframing and storyboarding 144, 145–146
project: definition 49
Proportion of Total Browser Usage for Internet Explorer and
Chrome 176
ProPublica 239, 240
prototypes 144, 146
Psychotherapy in the Arctic 289, 301
publicising 149
purpose 74–75
and curiosity 75
purpose map 76–90
experience 76–82
in practice 86–90
tone 82–86, 87, 88, 116, 212
Pursuit of Faster 318–326
Qiu, Yue and Michael Grabell (ProPublica) xi, 235
quantitative attributes 153–154
quantity 153
Racial Dot Map 205, 227
radial charts 304
Rain Patterns 300
Rams, Dieter 29, 33, 37, 41, 42
range charts 170
Rapp, Bill 72
Razor Sales Move Online, Away From Gillette 220
572

reading guides 251–252
reasoning 125
Record-high 60 Percent of Americans Support Same-sex Marriage
306
redundant encoding 301
Rees, Kim 112, 113
reference lines 219
refining 148
Reichenstein, Oliver 44, 55
relational attributes 154
relational charts 158
Relative Value of the Daily Performance of Stocks Across the 500
Index 179
relevance of content 41
research and inspiration 93, 126–127
resources 73–74
responsive design 239
Rise of Partisanship and Super-cooperators in the US House of
Representatives 188
Rosling, Hans 82
Roston, Eric and Blacki Migliozzi (Bloomberg Visual Data) x, 218
Rumsfeld, Donald 122
Saint-Exupéry, Antoine de 148
sampling 115
Sander, Nikola, Guy J. Abel and Ramon Baur ix, 189
Satanarayan, Arvind 210
scales 51
scales of measurement see data, types
Scarr, S., C. Chan, and F. Foo (Reuters Graphics) viii, 91
Scarr, Simon vii, 34 134, 284
Schneiderman, Ben 88
Scientific American 85
scientific visualisation 48
scope of data visualisation 3–4
Seeing Data Visualisation project 39, 315
series of values 50
settings 42, 72
Shankley, Bill 280
Shaw, Al, Annie Waldman, and Paul Kiel (ProPublica) xii, 249
573

shelf life 149
Silva, Rodrigo, Antonio Alonso, Mariano Zafra, Yolanda Clemente
and Thomas Ondarra (El Pais) xiii, 280
Simmon, Robert xiii, 270
simplicity 39, 40
simplification 39, 41
size 153, 304–305
sketching 92, 145, 295
skeuomorphism 31
skills 73
Slobin, Sarah vii, 20, 30, 43, 132
small multiples technique 296
Smith, Alan 109, 332
Snow, John 8
Social Progress Imperative 228
sources of data 322
spark bars 161
spatial charts 158, 304
speaking 58
Spielberg, Steven 74
Spotlight on Profitability 81–82, 117
stakeholders 1, 107, 147
as audience 66
Stalemate 297
State of the Polar Bear 275
statistics 98, 105–106
in data examination 112–113
and data exploration 127
inference techniques 105
multivariate analysis 127
univariate and multivariate techniques 105
Stefaner, Moritz 117, 132
Stefaner, Moritz, Dominikus Baurs and Raureif GambH vii, 89
Stevens, Joshua xi, 231
Stevens, Stanley 100
storyboarding 144, 145–146, 146, 295
storytelling 1, 51, 78, 159–160
style 44–46
style guidelines 71
subject neutrality of data visualisation 4
574

subject-matter appeal 39
subject-matter knowledge 25–26, 27, 39
subjectivity 32, 36
Summary of Eligible Voters in the UK General Election 2015 175
support for the work 149
Swing of Beauty 231
Sydney Olympics 2000 30
System 1 and System 2 thinking 90–91, 317
Szücs, Krisztina vii, 81
tables 20–21
tabulation 50
teamwork 330–331
technical skills needed 5–6
technological tools 6, 73
temporal charts 158
Ten Actors with the Most Oscar Nominations but No Wins (2015)
161, 162
testing 144, 146–148
‘Texas Department of Criminal Justice’ Website 86–87
meaning and completeness of data 114, 115
text visualisation 159
theoretical context 7
This Chart Shows How Much More Ivy League Grads Make Than
You 171
Thorp, Jer 115
three D data representation 37, 217
time slider control 137
time to consume a visualisation 40
timescales 56, 70
tone 76, 259
Total Sightings of Winglets and Spungles 26, 27
tower graphics 47
Tracing the History of N.C.A.A. Conferences 198
treemaps 45, 88–89, 179, 215
Tribou, Alex and Adam Pearce (Bloomberg Visual Data) ix, 166
Tribou, Alex, David Ingold and Jeremy Diamond (Bloomberg Visual
Data) xii, 251
Trillions of Trees 204
Tröger, Julius, André Pätzold, David Wendler and Moritz Klack x,
575

201
trustworthiness 30, 32–37, 98, 113
annotation 259
and colour 285–286
in composition 304–307
and deception 216–218, 217
of interactive designs 243
key matters 35–37
testing 147
truth 32, 33, 131
Tufte, Edward 29, 85, 244–245
Tukey, John 124
Tulp, Jan Willem x, 204
Tulp, Jan Willem 73, 121
Twitter NYC: A Multilingual Social City 238, 255
typefaces 256–257
UK Election Results by Political Party, 2010 vs 2015 190
UK Office for National Statistics 33, 34
Ulmanu, Monica, Laura Noonan and Vincent Flasseur (Reuters
Graphics) xi, 236
UN Global Pulse Survey 270
understanding 19, 21
and complexity 42
facilitating 21, 28, 38
and needs of audiences 38
of new symbols/technology 316–317
process 74
and purpose 74
stages of 22–28
unfamiliar representation 40
univariate and multivariate techniques 105
updating 149
US Gun Deaths 225, 255
US Presidents by Ethnicity (1789 to 2015) 114
user guides 249
Vallandingham, Jim x, 192
value sorting
LATCH 298–301
576

location 298–299
alphabetical 299, 300
time-based 300
categorical 300–301
hierarchical 301
values 111, 112–113
frequency counts 112
frequency distribution 112
range 117
spread/dispersion 113
Van de Ruit, Graham: Millions, Billions, Trillions 302–303
variables 50, 111, 112–113
Veltman, Noah xi, 229
Viégas, Fernanda and Martin Wattenberg xiii, 289
viewers
definition 49
effort needed by 317
learning new symbols/technology 316–317
number of 75
see also audiences
vision: definition 76
visual analytics 48
visual appeal 219–220
visual encoding 151–157
attributes 152, 153–154
form 156, 157
marks 21, 151–152, 153, 155, 156
see also chart types
visual mood 88
visualisation
experience 76–82
exhibitory 76, 77, 81–82, 88
explanatory 76, 77–79, 88
exploratory 76, 77, 79–80, 88
narrative 78
harnessing ideas 90–92
tone 82–86, 87–88
feeling tone 83–86
reading tone 82–83
visualisation literacy 315–334
577

first thoughts
initial scan 319
relevance 319
setting 319
visual appeal 319
key features
data 322
format 320
interactive functions 322–323
introductory text 321–322
shape and size 320
source 321
studying the project 320
subject matter 320
tasks
comprehending 326
interpreting 325
observations 325
perceiving 324–325
understanding symbols 315–316
viewer–visualiser harmony 326
appreciation of context 326
false consciousness 326–327
curiosities 327
overview 326
visualisers
capabilities 327–329
communicator 328
data analyst 328
designer 329
journalist 328–329
project manager 327
scientist 328
technologist 329
definition 49
developing capabilities 329–330
evaluating
outcome 331
performance 331–332
teamwork 330–331
578

Vitruvius Pollio, Marcus 46
voiceover 255–256
Voronoi treemap 158
Washington Post 231
waterfall charts 158
Wayne Kane: Games and Points for Toronto Rangers 25–26
Wealth Inequality in America 78, 256
web design 239
Weber, Matthew (Reuters Graphics) xi, 234
Weldon Cooper Center for Public Service, Rector and Visitors of the
University of Virginia (Dustin A. Cable, creator) x, 205
What Good Marathons and Bad Investments Have in Common 124
What’s Really Warming the World? 218
Where You Can Both Smoke Weed and Get a Same-sex Marriage 180
Which Fossil Fuel Companies are Most Responsible for Climate
Change? 182
White, Alex 282
Who Wins the Stanley Cup Playoff Beards 165
Why Is Her Paycheck Smaller 219
Wind Map 289
Winkel-Tripel projection 308
Wireframe Sketch 295
wireframing 144, 145–146, 294–295
Wolfers, Justin viii, 124
Wooton, Sir Henry 46
workflow
process
adaptability 55
experimentation 54–55
four stages 54
importance of process 53–54
mindset activities: thinking, doing, making 56
ongoing tasks 55, 88
pragmatism 54
purpose 54
process in practice
attention to detail 58–59
communication 58
heuristics 57
579

honesty with yourself 59
making it work 59
management 56
note-taking 57–58
pen and paper 57
reflective learning 59
research 58
thinking 56–57
workload 72
Worst Board Games Ever Invented 303
Wurman, Richard Saul 298
Yau, Nathan ix, 184
Years Since First Movie table 152
YouTube user ‘Politizane’ vii, 78
zero quantity 305
zooms 226
580

Table of Contents
Half Title 2
Publisher Note 3
Title Page 4
Copyright Page 5
Contents 7
Illustration List 8
Acknowledgements 18
About the Author 19
Introduction 21
Part A Foundations 41
1 Defining Data Visualisation 42
2 Visualisation Workflow 94
Part B The Hidden Thinking 105
3 Formulating Your Brief 106
4 Working With Data 153
5 Establishing Your Editorial Thinking 202
Part C Developing Your Design Solution 219
6 Data Representation 227
7 Interactivity 386
8 Annotation 420
9 Colour 444
10 Composition 488
Part D Developing Your Capabilities 519
11 Visualisation Literacy 520
References 546
Index 551
581

Half Title
Publisher Note
Title Page
Copyright Page
Contents
Illustration List
Acknowledgements
About the Author
Introduction
Part A Foundations
1 Defining Data Visualisation
2 Visualisation Workflow
Part B The Hidden Thinking
3 Formulating Your Brief
4 Working With Data
5 Establishing Your Editorial Thinking
Part C Developing Your Design Solution
6 Data Representation
7 Interactivity
8 Annotation
9 Colour
10 Composition
Part D Developing Your Capabilities
11 Visualisation Literacy
References
Index