This project Foundations of Geographic Information Systems have two parts. This is for the first part, which due in 24 hours.

A 1 page proposal.

The whole project needs to be assigned to one tutor. 

The second part is due at March. 22.

Please make sure you have at least master levels Geographic info system knowledge.

Final Project Overview

The following document outlines deliverables and expectations for the final practicum exercise in GIS 5103. This is an open-ended, individual project to be completed in full by the end of the semester. The project will consist of two key deliverables, a single-page double spaced proposal, and a five-page report also double space.
 

Proposal – due March 1, 2020 (50 points)
The proposal shall be a one-page document in which you will outline the research problem you are trying to solve, what types of data you would like to leverage to solve it, and what GIS methods or tools from our class you may use. You can search for external GIS data sets, use data from our course, or use data you already have access to. This document must not exceed one page.   Your instructors will review your proposals and provide feedback on accessing data sets and overall feasibility. 

Project Report – due March 20, 2020 (100 points)
The report will contextualize the problem you are solving, and should detail the structure of your data, and your methodology. You must also discuss the results of your study and frame them in the context of the problem. You will also be evaluated based on your map products. Note that map products do NOT count toward the total page count. You may submit up to five pages of text in addition to maps. 

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Foundations of Geographic Information Science
GIS 5103
Fall 2019 – Week 3
Spatial Data Modeling

By the end of Week Three you should be able to:
Provide a definition of a ‘spatial data model’
Explain how spatial entities are used to create a data model
Distinguish between rasters and vectors
Describe a spatial data structure
Explain what topology is
List the advantages and disadvantages of different types of spatial models

File Types

Files in a GIS may be binary data files. They can be read by computers (a program or a hardware understands in advance exactly how it is formatted) but not people.
All executable programs and most numeric data files are stored in binary files.
Or they can be ASCII text. ASCII files can be read by humans.
Binary is faster to read and smaller.

Raster and Vector Data
A file must have a mental model of how the physical data represent a geographic feature.
GIS traditionally use either raster or vector models to store data.
Rasters represent phenomenon distributed continuously over a large area.
Vectors are object based (point, line, polygon).

Raster Data Example
Satellite data has a RASTER data structure (GRID
CELL), composed of matrix of pixels;
Each pixel records the “radiation intensity” measured by the remote sensing instrument called its radiometric resolution.
The intensity of radiation is represented by a data number (DN), that is, the “brightness value”.

One grid cell (pixel) has one attribute that represents its data number/ brightness value (even if 0); a cell also has a spatial resolution, given as the cell size in ground units.
Grid Structure of Raster Data, p.81-82

Vector Data Example
Spatial feature files that are object (vector) based:
Have identifiable boundaries or spatial extent;
Relevant to an intended application;
They have “ATTRIBUTES” – descriptive characteristics of a feature.
For example, a tax parcel can have a rectangular shape and be represented by a polygon, that is the feature’s geometry. The owner name, parcel id, property value, etc. would be considered the
polygon’s attributes.
Attribute data are stored in tables comprised of a matrix of numbers and values
and are stored in rows and columns, like a spreadsheet, e.g. attribute tables.
OBJECTS may be:
Exact, e.g., man-made objects; or
Inexact (fuzzy), e.g., soil types and forest stands etc.
Recall: spatial objects represented by vector files may be represented either as
points, lines or polygons (areas) [slides 9-11, class 2].
Vectors vs Rasters:
Vectors can store information about topology (relational space, p. 51-52).

GIS Data File Formats
Most GIS systems can import different data formats, or use utility programs to convert them;
Data formats can be industry standard or commonly accepted standard;
Example vector formats: DLG (Digital Line Graph) and TIGER (Topologically Integrated Geographic Encoding and Referencing system), shp. (ESRI)
Personal, File, and Enterprise GeoDatabase (ESRI);
Most raster formats are digital image formats, e.g. Tiff (Tagged Image File
Format) or DEMs (Digital Elevation Models).

Data Models
Once you have determined which features you would like to map or create data
layers for you next have to decide how you wish to portray those features
and also consider the following:

How the geographic features are to be represented for each theme (for example, as points, lines, polygons, or rasters) along with their tabular attributes
How the data will be organized into datasets, such as feature classes, attributes, raster datasets, and so forth
What additional spatial and database elements will be needed for integrity rules, for implementing rich GIS behavior (such as topologies, networks, and raster catalogs), and defining spatial and attribute relationships between datasets.

Representation
Each GIS database design begins with a decision as to what the geographic representations will be for each dataset. Individual geographic entities can be represented as:
Feature classes (sets of points, lines, and polygons)
Imagery and rasters (aerial photography, grid datasets)
Continuous surfaces that can be represented using features (such as contours), rasters (digital elevation models [DEM]), or triangulated irregular networks (TINs) using terrain datasets
Attribute tables for descriptive data – these tables can be stand-alone tables that contain information related to a feature in a feature class – such as an inspection, an activity (catch basin cleaning, tree maintenance, etc.)

Data Themes
Geographic representations are organized in a series of data themes (sometimes referred to as thematic layers). A key concept in a GIS is one of data layers, or themes. A data theme is a collection of common geographic elements such as a road network, a collection of parcel boundaries, soil types, an elevation surface, satellite imagery for a certain date, well locations, and so on.
The concept of a thematic layer was one of the early notions in GIS. Practitioners thought about how the geographic information in maps could be partitioned into logical information layers—as more than a random collection of individual objects (such as a road, a bridge, a hill, a house, a peninsula). These early GIS users organized information in thematic layers that described the distribution of a phenomenon and how it should be portrayed across a geographic extent. These layers also provided a protocol (capture rules) for collecting the representations (as feature sets, raster layers, attribute tables, and so on).

In GIS, thematic layers are one of the main organizing principles for GIS database design.
Each GIS will contain multiple themes for a common geographic area. The collection of themes acts as layers in a stack. Each theme can be managed as an information set independent of other themes. Each has its own representations (points, lines, polygons, surfaces, rasters, and so on). Because the various independent themes are spatially referenced, they overlay one another and can be combined in a common map display. Plus, GIS analysis operations, such as overlay, can fuse information between themes.

GIS datasets are collections of representations for a data
theme
Geographic data collections can be represented as feature classes and raster-based datasets in a GIS database.
Many themes are represented by a single collection of homogeneous features such as a feature class of soil type polygons and a point feature class of well locations. Other themes, such as a transportation framework, are represented by multiple datasets (such as a set of spatially related feature classes for streets, intersections, bridges, highway ramps, and so on).
Raster datasets are used to represent continuous surfaces, such as elevation, slope, and aspect, as well as to hold satellite imagery, aerial photography, and other gridded datasets (such as land cover and vegetation types).

GIS datasets are collections of representations for a data
theme – Cont.
Both the intended use and existing data sources influence spatial representations in a GIS. When designing a GIS database, users have a set of applications in mind. They understand what questions will be asked of the GIS. Defining these uses helps to determine the content specification for each theme and how each is to be represented geographically. For example, there are numerous alternatives for representing surface elevation: as contour lines and spot height locations (such as hilltops, peaks), as a continuous terrain surface (a TIN), or as shaded relief. Any or all of these may be relevant for each particular GIS database design. The intended uses of the data will help to determine which of these representations will be required.
Frequently, the geographic representations will be predetermined to some degree by the available data sources for the theme. If a preexisting data source was collected at a particular scale and representation, it will often be necessary to adapt your design to use it.

Individual GIS datasets often are collected in concert with other data
layers
While each GIS dataset can be used independently of other GIS data, it is often quite important to collect datasets in concert with other information layers so that the fundamental spatial behavior and spatial relationships are maintained and consistent between the related GIS data layers. Here are a few examples that help to illustrate this concept:
The spatial relationships between elevation, landform, soil type, slope, vegetation, surficial geology, and other terrain properties are typically compiled in unison to characterize environmental resource units. Understanding the science behind these spatial relationships helps to build a consistent, logical database where features from each data layer are consistent with each other.
Topographic basemap information is compiled in an integrated manner. Hydrography, transportation, structures, administrative boundaries, and other topographic map layers are compiled in unison. These cartographic representations in the map display are built in an integrated manner to communicate clearly and accurately and draw attention to key map locations.

Individual GIS datasets often are collected in concert with other data
layers – Cont.
In each of these cases, a data model defines a collection of related data themes that fit into an overall information framework. Each framework is essentially a collection of related data themes that are best captured in unison with each other. The data capture guidelines follow sound scientific principles about their spatial behavior and relationships. Each theme plays an important part in the holistic characterization of a particular landscape. For example:
Terrain landscape. Topographic maps, elevation, drainage network, transportation network, map features, cross-country movement, and so forth
Urban landscape. Buildings, critical infrastructure, and so forth
Imagery landscape. Satellite and aerial, local, regional, and national assets, and so forth
Human landscape. Demographics (population characteristics), cultural centers, citizens, administrative districts and zones and so forth

Workforce landscape. Mobile workforce tracking, service centers, traffic
conditions, warehouses, and so forth
Sensor landscape. Camera locations, devices, and so forth
Operations and plans landscape. Zones of control, planned movements,
response, and so forth
This concept of collecting integrated data themes in unison is one of the key design principles used in each of the ArcGIS Data Models.

Topology
Topology is a collection of rules that, coupled with a set of editing tools and techniques, enables the geodatabase to more accurately model geometric relationships. ArcGIS implements topology through a set of rules that define how features may share a geographic space and a set of editing tools that work with features that share geometry in an integrated fashion. A topology is stored in a geodatabase as one or more relationships that define how the features in one or more feature classes share geometry. The features participating in a topology are still simple feature classes—rather than modifying the definition of the feature class, a topology serves as a description of how the features can be spatially related.

Why Topology?
Topology has long been a key GIS requirement for data management and integrity. In general, a topological data model manages spatial relationships by representing spatial objects (point, line, and area features) as an underlying graph of topological primitives—nodes, faces, and edges. These primitives, together with their relationships to one another and to the features whose boundaries they represent, are defined by representing the feature geometries in a planar graph of topological elements.

Why topology?
Topology is a collection of rules that, coupled with a set of editing tools and techniques, enables the geodatabase to more accurately model geometric relationships. ArcGIS implements topology through a set of rules that define how features may share a geographic space and a set of editing tools that work with features that share geometry in an integrated fashion. A topology is stored in a geodatabase as one or more relationships that define how the features in one or more feature classes share geometry. The features participating in a topology are still simple feature classes—rather than modifying the definition of the feature class, a topology serves as a description of how the features can be spatially related.

Why topology?
Topology is fundamentally used to ensure data quality of the spatial relationships and to aid in data compilation. Topology is also used for analyzing spatial relationships in many situations, such as dissolving the boundaries between adjacent polygons with the same attribute values or traversing a network of the elements in a topology graph.
Topology can also be used to model how the geometry from a number of feature classes can be integrated. Some refer to this as vertical integration of feature classes.

Ways that features share geometry within a topology
Features can share geometry within a topology. Here are some examples among adjacent
features:
Area features can share boundaries (polygon topology).
Line features can share endpoints (edge-node topology).
In addition, shared geometry can be managed between feature classes using a geodatabase topology. For example:
Line features can share segments with other line features.
Area features can be coincident with other area features. For example, parcels can
nest within blocks.
Line features can share endpoint vertices with other point features (node topology).
Point features can be coincident with line features (point events).
The next slide shows some commonly used topology rules found in ArcGIS.

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What are the advantages of topology?
The results are that topological primitives (nodes, edges, and faces) and their relationships to one another and their features can be efficiently discovered and assembled. This has several advantages:
Simple feature geometry storage is used for features. This storage model is open, efficient, and scales to large sizes and numbers of users.
This simple features data model is transactional and is multiuser. By contrast, the older topological storage models will not scale and have difficulties supporting multiple editor transactions and numerous other GIS data management workflows.
Geodatabase topologies fully support all the long transaction and versioning capabilities of the geodatabase. Geodatabase topologies need not be tiled, and many users can simultaneously edit the topological database—even their individual versions of the same features if necessary.
Feature classes can grow to any size (hundreds of millions of features) with very strong performance.

What are the advantages of topology?
Topology can be added this to an existing schema of spatially related feature classes. The alternative is that you must redefine and convert all your existing feature classes to new data schemas holding topological primitives.
There need only be one data model for geometry editing and data use, not two or more.
It is interoperable because all feature geometry storage adheres to simple features specifications from the Open Geospatial Consortium and ISO.
Data modeling is more natural because it is based on user features (such as parcels, streets, soil types, and watersheds) instead of topological primitives (such as nodes, edges, and faces). Users will begin to think about the integrity rules and behavior of their actual features instead of the integrity rules of the topological primitives. For example, how do parcels behave? This will enable stronger modeling for all kinds of geographic features. It will improve our thinking about streets, soils types, census units, watersheds, rail systems, geology, forest stands, land forms, physical features, and on and on.

What are the advantages of topology?
Geodatabase topologies provide the same information content as maintained topological implementations—either you store a topological line graph and discover the feature geometry (like coverages) or you store the feature geometry and discover the topological elements and relationships (like geodatabases).
In cases where users want to store the topological primitives, it is easy to create and post topologies and their relationships to tables for various analytic and interoperability purposes (such as users who want to post their features into an Oracle Spatial warehouse that stores tables of topological primitives).
At a pragmatic level, the ArcGIS topology implementation works. It scales to extremely large geodatabases and multiuser systems without loss of performance. It includes validation and editing tools for building and maintaining topologies in geodatabases. It includes rich and flexible data modeling tools that enable users to assemble practical, working systems on file systems, in any relational database, and on any number of schemas.

Foundations of Geographic Information Science

GIS5103 – Fall 2019

Week 5

Data Input, Throughput, & Quality

By the end of this week you should be able to:
Describe methods of data input
Describe Digitizing sources ‐ the cartographic base
Contrast Digitizing methods within a GIS (Tablet vs. “Heads Up”)
Describe Data editing (correcting digitizing errors, re‐projections
and generalizations)
Define accuracy and precision
Describe the importance of data standards and metadata
List sources of errors in a GIS project
Differentiate spatial and attribute errors
Describe techniques for coping with errors in Data

The Data Stream
The focus of this week’s lecture component is on creating GIS or spatial data from a variety of
sources. We will also be looking at how GIS data is updated.

Digitizing GIS Data
As we have discussed over the past several weeks one of the primary features of a GIS is its ability to integrate data from a variety of sources. Depending on how the data is to be used the need to convert the data or extract features from it may arise.
Fortunately there are a number of techniques and tools available to assist in acquiring data for
a new GIS is straightforward these days. Some of the sources for this data can include some of the following:
GPS (Global Positioning Systems) has become a major source of new GIS data. Data acquired through GPS can be highly spatially accurate (1cm horizontal) depending upon the type of receiver used.
Digital map images such as scanned maps, LiDAR , satellite data, and aerial photographs (Ortho photos) are now often used as a cartographic base for digitizing; this is another way to create new GIS data layers.
Remotely sensed satellite data are becoming an important source of GIS data as the cost of data falls, free data becomes available and new types of data emerge.
The following examples show the different spatial scales commonly found
with remote sensing data. Examples are also provided that show how they can be used to digitize temporal changes or changes to features over time (urbanization) or after an event (Natural Disaster).

Satellite data are raster data. The amount of spatial detail represented by one pixel of a raster
image determines its spatial resolution. So, an image with one meter spatial resolution means
that each pixel in the image represents one square meter on the ground. With improvements in
cameras and digital image acquisition the cost of acquiring aerial images has decreased. It is now possible to obtain imagery with a 6” pixel resolution for a few thousand dollars.
30 meters
10 meters
5 meters
The Different Spatial Resolutions

Image Resolutions Continued
2 meters
1 foot
In cartographic applications, higher spatial resolutions are often favored. However, for applications such as weather forecasting or to monitor seasonal plant growth on a
yearly basis for North America, coarser spatial resolutions are favored. While this type of data
can be extremely useful there are data storage considerations and large file sizes to consider which may impact the accessibility of these images especially in web based applications.
1 meter

Where to obtain remote sensing data
There are a number of useful websites from which remote
sensing data (satellite images) can be downloaded. Here are some examples:
Free satellite image data @ University of Maryland:

http://glcf.umd.edu/data/
It is also possible to BUY data! e.g. http://gs.mdacorporation.com/products/index.asp

Free Digital Terrain data:

http://www.webgis.com/terraindata.html

Digitizing within a GIS
Sometimes digitizing is necessary if:
We need new to input new features.
Map features are incorrectly mapped.
Updates are needed for existing features.

And so, just what is digitizing?
Digitizing is the process of capturing map data in a GIS layer by tracing points, lines, or polygons from a map or image by using a mouse or a “puck” on on a digitizing tablet.
It can be done by either using a digitizing tablet, or by digitizing directly from a satellite image or a scanned map on screen.
On‐screen or “heads up” digitizing creates a spatial dataset by tracing over features displayed on a computer monitor with a mouse. The newly created dataset picks up the spatial reference of the source document and results in a string of points with (x, y) values. You will learn how to digitize in tutorial 6 of your analysis class but we will examine how we can do this now as well.

Digitizing table and PC workstation
A few years ago, the digitizing table was widely used in GIS. This method allows features to be input into a GIS
database as points, lines or polygons but often requires extensive data clean-up and editing. With the updates made to GIS software this method of data entry is essentially obsolete and has been replaced by on-screen digitizing.

Digitizing directly on screen
Digitizing directly on screen or “heads up” digitizing, is the approach I am most familiar with and it is what you will be doing in the analysis class.
In this case, you use your computer mouse to digitize paper maps, aerial photos, or other images displayed in the GIS. We can refer to this layer as a stable base map (that is geocoded) ‐ recall from slide 15 that the newly created dataset picks up
the spatial reference of the source document? Below is an example, where the outline of fluvial features (blue) and contour lines (brown) have been digitized. Only the fluvial features can have been digitized directly from the image on the left, however.

Selecting points to digitize
A vertex
Digitizing is not difficult to do. You can choose to digitize either a point, a line (snap to feature) or a polygon (snap to end). Snapping is a routine embedded within the GIS that makes sure the separate vertices in a line or an area are connected. That is, it ensures there are no gaps. This is important if calculations or correlations are going to be performed on the new layers. In these cases,
gaps would cause errors. The next slide shows how snapping can be achieved –it uses topology

Geocoding Address Data
What is it?
Geocoding address data is the process of relating an address to a geographic location (such as latitude/longitude coordinates) or geographic area (such as census tract, block group, block, or ZIP code). The address itself then is used to determine the geographical coordinates.
Geocoding can be affected by the quality of data, e.g., incorrect spelling and the use of different abbreviations (e.g., for Street and Avenue). Therefore, the use of standards is relevant to geocoding address data.

Address Matching
Address matching is the process of geocoding street addresses to a street network.
(Modified) ESRI’s definition of address matching:
A process that compares an address or a table of addresses to the address attributes of a reference dataset (e.g., locations may be determined based on address ranges stored for each street segment). This determines whether a particular address falls within an address range associated with a feature in the reference dataset.
If it does, it is considered a match and a location can be returned. The next slide provides an example of how an address can be converted to a point feature and drawn along a line segment based upon an address range.

Address Matching
In most instances a geocoding service or engine is built upon a street network in which line segments are assigned an address number range on the left and right side of the streets, a city or twin name, and a zip code. When a user enters an address the geocoding engine finds a match along a line segment and places the location of the address (a point) based upon where it falls along the street line segment.

GIS Services: Address Matching Resources
Creating a geocoding engine can be a time consuming process. However, there are some commercially available geocoding services that allow us to enter a table of addresses for geocoding
and then download the results and add them to our GIS database. Some geocoding services are free and some charge a fee.
http://www.batchgeo.com

https://geomap.ffiec.gov/FFIECGeocMap/GeocodeMap1.aspx https://www.census.gov/geo/maps-data/data/geocoder.html

Error, Accuracy, and Precision
Until quite recently, people involved in developing and using GIS paid little attention to the problems caused by error, inaccuracy, and imprecision in spatial datasets. Surely, a GIS is too powerful for error?
Not true! It is now recognized that error, inaccuracy, and imprecision, if left unchecked, can make the results of a GIS analysis almost worthless, it is like putting garbage in means getting garbage out.
But where do these errors come from?
Since a GIS can collate and cross‐reference many types of data by location and can integrate many discrete datasets (which is the heart of its power), it can also inherit error from the imported datasets.
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Error, Accuracy, and Precision
We can discuss error in terms of data quality.
Data quality refers to the relative accuracy and precision of a GIS database. These facts are often documented in data quality reports.
Errors may exist both in map data (which can be reduced by maintaining topological integrity) and also attribute data.
Next we will discuss the differences between accuracy and precision in GIS data.
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Data Accuracy
Accuracy is the degree to which information on a map, or in a database, matches true values. The level of accuracy required for particular applications and GIS analyses varies greatly.
Highly accurate data can be very difficult and costly to produce and maintain often requiring highly accurate GPS and/or survey data or the acquisition of LiDAR or remotely sends data (satellite imagery, aerial photographs, etc.).
In discussing a GIS database, it is possible to consider horizontal and vertical (spatial) accuracy with respect to geographic position (i.e. how close is the mapped feature to its actual location on the earths surface) as well as attribute accuracy (i.e. do the values in the Attribute table match the real world values).
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Data Accuracy
1:1,200 ± 1 m
1:2,400 ± 2 m
1:4,800 ± 4 m
1:10,000 ± 8.5 m
1:12,000 ± 10 m
1:24,000 ± 12 m
1:63,360 ± 32 m
1:100,000 ± 50 m
This means that when we see a point on a map we have its “probable” location within a certain area. For example, a sewer manhole shown on a 1:1,200 scale map should be within 1 meter (3 ft) of it’s true location on the ground to be considered within the 1:1,200 accuracy standard. It’s important to understand the limitations of your data as well. While a layer of sewer manholes within the 1:1200 data standard may be suitable for broad planning or maintenance activities, using this data at scales it was not intended (engineering purposes for example) for can have negative and potentially costly consequences.
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Defining Precision
Precision refers to the level of measurement and exactness of description in a GIS database.
The level of precision required for particular applications varies greatly. Engineering projects such as road and utility construction require very precise information measured
to the millimeter. Demographic analyses of marketing or electoral trends can often make do
with less. For example to the closest zip code or precinct boundary. Highly precise data can also be very difficult and costly to collect.
Precise data ‐ no matter how carefully measured ‐ may be inaccurate and vice-versa. Let’s try to understand this better by using Bolstad’s diagram on page 625, which I have included in the next slide.
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Accuracy and Precision
Points (yellow circles) are digitized to represent the center of the cloverleaf intersection. Average accuracy is high when the average of the points falls near the true location, as in the panels on the left side of the figure. Precision is high when the points are all clustered near each other (top panels). A group of points may be accurate, but not precise (lower left), or precise, but not accurate (upper right). We typically strive for a process that provides both accuracy and precision (upper left), and avoid low accuracy and low precision (lower right).
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Sources of Inaccuracy and Precision
Let us take a minute to discuss attribute accuracy and precision, that is the non‐spatial data linked to lo
cation.
Inaccuracies may result from mistakes of many sorts, including basic data entry. With respect to precision, precise attribute information describes phenomena in great detail. For example, a precise description of a person living at a particular address might include gender, age, income, occupation, level of education, and many other characteristics. A less precise description might include just income or gender. It is the application that will determine whether precise or less precise data are needed.

There are many sources of error that may affect the quality of a GIS dataset. Some are quite obvious, but others can be difficult to discern. Few of these will be automatically identified by the GIS itself.
It is the user’s responsibility to prevent them.
Sources of error can be divided into 3 main categories:
Conceptual errors;
Errors arising through data processing; and
Errors arising from source data
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Errors Arising from Source Data
Age of Data
Data sources may be too old to be useful or relevant to current GIS projects. Past collection standards may be unknown, non‐existent, or currently acceptable. For instance, John Wesley Powell’s nineteenth century survey data of the Grand Canyon lacks the precision of data that can be developed and used today. Additionally, erosion, deposition, and other geomorphic processes will have modified the landscape. Therefore, reliance on old data could skew, bias, or negate results.
Density of Observations
The number of observations within an area is a guide to data reliability and they should be known by the map user. An insufficient number of observations may not provide the level of resolution required to adequately perform spatial analysis and determine the patterns GIS projects seek to resolve or define.
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Errors Arising from Source Data
There are four ways we describe errors in spatial data
Positional accuracy – describes how close the locations of objects represented in a digital dataset correspond to the true locations of the real-world entities.
Attribute accuracy – summarizes how different the attributes are from their real world values.
Logical Consistency – reflects the presence, absence, or frequency of inconsistent data. Tests for logical consistency often require comparisons among themes (i.e. all buildings must be on dry land).
Completeness – Describes how well a layer reflects all of the real world features it is supposed to represent.
The next slide shows a figure from the text (Figure 14-3, Page 624) that depicts examples of these types of errors.
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Errors Arising from Source Data
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Data Standards
Regular checks and tests should be employed during a project to make sure that standards are being followed. This allows a designer to pinpoint difficulties at an early stage and correct them. Establishing data standards helps with data exchange –unfortunately, the history of GIS data exchange has been chaotic and has been wasteful in the past.

Examples of good data standards include:
USGS, National Mapping Program Standards, http://nationalmap.gov/standards//

Spatial Data Transfer Standard http://mcmcweb.er.usgs.gov/sdts/
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Foundations of Geographic Information Science

GIS5103 – Fall 2019

Week 4 Database Management

Outline
Differentiate between relational database management systems (RDBMS), databases, and data files.
Explain why we select the database approach for GIS
Define what a relational database is
Describe how databases are created
Describe GIS database applications

Database Management System (DBMS)
What is a Database Management System (DBMS)?

A DBMS is a system [or software] designed to manage a database and allow user-
selected operations to be performed on the data.
1.
2.
3.
4.
It is a logical construct for the storage and retrieval of information. It puts the database into a geographic coordinate system.
Attribute data models are needed for the DBMS.
It contains data definition language; a data-entry module; a data update module; a report generator; and query language.

What is the difference between and database and a database management system?
A database is a simply a collection of related data. This can include non-computerized data such as those found in a ‘phone or address book. It can also include information contained in spreadsheets or also in relational databases or databases comprised of multiple tables that are connected or related to each other using common fields or
“keys” found in each of the tables.
Data in a computer database, however, are managed and accessed through a database management system (DBMS). The database management system is used to manage, access the database
The Database is distinct from the DBMS

In order for there to be a DBMS, there must be a database. In order for there to be a database there must be data files
Bolstad’s Figure 8-4. It shows the database approach to data handling data.

First comes the Data File, then the database and then the DBMS
So, what is the difference between a data file and a database? Heywood discusses this on p.110. For simplicity, below is a comparison in tabular form.
The image above from Screencast illustrated the differences between a flat file (like a spreadsheet) and the relational database model structure (RDBMS).

Creating the Database, 337-339
To design a database, the user must:
Identify the features (i.e. entities) to be represented (e.g., forests, trails, and recreational features. These are all nouns);
Identify the appropriate tables for a relational database. What is the ID# that could allow a relational join for e.g. (Forest-ID)?
Identify the feature’s attributes
(e.g., Trail Difficulty)
Identify relationships between the features (e.g., what activities, trails, can be explored at each of the forests? What trails are in each forest and how difficult area they?);
Figure 8-6: Forest data in a relational database structure.

Creating the Database
Keys or Key ID’s are extremely important in a relational database. They allow us to combine and display data from other tables and also allow us to store, query, and edit data more efficiently. There are two types of keys, primary and foreign.
A primary key, also called a primary keyword, is a key in a relational database that is unique for each record. It is a unique identifier, such as a driver license number, telephone number (including area code), or vehicle identification number (VIN). In the example to the left, the Forest-ID would be the primary key. A relational database must always have one and only one primary key.
A foreign key is a set of one or more columns in a table that refers to the primary key in another table. There isn’t any special code, configurations, or table definitions you need to place to officially “designate” a foreign key. For example, trail name.

Cardinality
In the context of databases, cardinality refers to the uniqueness of data values contained in a column.
High cardinality columns are those with very unique or uncommon data values. For example, in a
database table that stores bank account numbers, the “Account Number” column should have very high
cardinality – by definition, every item of data in this column should be totally unique.
Normal cardinality columns are those with a somewhat unique percentage of data values. For instance, if a table holds customer information, the “Last Name” column would have normal cardinality. Not every last name will be unique (for example, there will likely be several occurrences of “Smith”) but on the whole, the data is fairly non-repetitive.
Low cardinality columns are those with very few unique values. In a customer table, a low cardinality column would be the “Gender” column. This column will likely only have “M” and “F” as the range of values to choose from, and all the thousands or millions of records in the table can only pick one of these two values for this column.
Cardinality relationships between tables can take the form of one-to-one, one-to-many (whose reversal is many-to-one) or many-to-many. These terms simply refer to the relationships of data between the tables. For example, the relationship between the “Customers” table and the “Bank Accounts” table is one-to- many, that is, one customer can have several accounts, but one account cannot belong to more than one customer. That is, of course, assuming this bank has never heard of joint accounts!

Cardinality
Cardinality is commonly expressed in the following ways:

Databases
In many tabular databases (e.g., Dbase, SQL Server, ORACLE, Excel etc.), it is possible to list data, create queries, and sort the results.
Linking a tabular database to a GIS, however, provides additional “spatial”
capabilities that enhance the value of the data to provide useful information.
The “geographic search” is the secret to GIS data retrieval, in addition to the
ability to get back on demand data that were previously stored.
Let’s take a look some examples of GIS software and the “spatial” database
facilities they provide.

Multitier Architecture
.
Figure 8-5: Data are stored and accessed from the databases on the bottom tier and requests to view the data (queries) are made from the clients on the top tier consisting of desktop and web based products. Application servers act as the middle-men, processing transactions from the clients to the databases on the bottom tier. In well designed systems there are rules or policies setup that control how the data is accessed and by whom. We will look at this in more detail layer in the lecture.

Spatial Database facilities in ArcGIS
ESRI’s ArcGIS software is packaged with tutorials for ArcCatalog, ArcMap, ArcToolbox, and any extensions that you purchase. ESRI maintains on-line resources for teaching GIS in higher education, which includes links to lab exercises. The software you are using in the analysis course is, of course, ESRI software.

GIS and image processing software developed at Clark Labs – an educational and research institution at Clark University in Worcester, Massachusetts – is called Idrisi. It is easy to use while providing professional-level GIS, image processing, and spatial statistics analysis on Windows computers. It originally focused on handling raster data.
Database facilities in Idrisi http://www.clarklabs.org/

The historical approach to data handling is that a different version of the visitor’s details may be stored in separate databases. Additionally, data may be duplicated, errors may occur during transcription, and data storage mechanisms are often inflexible etc.
Why Choose a Database Approach?

Card index record from ski school manual database
It is also very difficult to update files using the traditional approach, as
illustrated in this example for a hardcopy index card held for an individual.

We are striving for the database approach for data handling
Key Points for databases:
They are collections of related material shared by different users;
Data is organized for easy reference with no duplication (e.g., think of a phone book);
Computer databases are superior to hard- copy ones because they are easier to update and data can be adapted to different uses (e.g., unlike the phonebook).

Relational Databases
Example of a relational attribute table data for hotels in Happy Valley, including the
terminology
/ ID number
Earlier this term you were introduced to flat file and hierarchical attribute tables. Relational attribute files are different to flat files and hierarchical files (see next slide for comparison). As we can see here, by organizing the data into 2D tables that contain records for each entity / feature, it is possible to “query” these data using a GIS, to find out which hotels are the cheapest or which is 5 star etc.
Examples will be given in later slides.

New attribute tables can be created using the relational ID number.
This is 1 example of the flexibility of computer databases over hardcopy forms, in that they can be adapted to different purposes to illustrate different relationships.
You will be doing these types of exercises during your analysis course.
Creating New Attribute Tables from Existing Ones

Retrieval Operations
Retrieval operations may be applied to either spatial or attribute data. For example, if searches are made to attribute data then the user would find or browse an entity.
As we saw in the last slide, and as you have experienced in your analysis course, this can be used for data reorganization by selecting, renumbering, and sorting the data.
Similarly, new attributes can be calculated by computation of selected values.

Spatial Retrieval Operations
In a spatial (map) database, records are features or themes.

The spatial equivalent of an “attribute find” is locate. The GIS then
highlights the result.
Spatial equivalents of the DBMS queries result in locating sets of features or building new GIS layers.

Queries
Functions, query language and applying algorithms could be difficult requiring a
command line approach. For example:
find in states where state_name = ‘California’
<1 record in result>
use states
calculate in states population_density = population / area
<50 records in result>
restrict in states where population_density > 1000
<20 records selected in result>
So you are coming into this field at a good time because it recent years it has become A LOT more user friendly.

Querying via SQL
At the highest level, a query in SQL is comprised of three key components or operations: SELECT, FROM, and WHERE. Intuitively, these operations used together tell your DBMS/GIS to SELECT some subset or fields of data FROM some data set WHERE some such condition is met.

Retrieval User Interface
Now the Retrieval User Interface simplifies the process. Most GIS packages use the GUI (Graphical User Interface) of the computer’s operating system to support both a menu-type query interface and a macro or programming language.
SQL (Standard Query Language) is a standard interface that works with relational databases and is supported by many GIS software packages.
So, let us examine a few things we can do operations with the GUI and SQL in the next slides. You will also look at this in more detail in week 8 (chapter 9) and you will be applying these techniques extensively in the analysis course.
I will start with spatial retrieval operations. Then go on to illustrate an application from my own work that uses complex retrieval operations. Finally, I will finish this week’s lecture component by discussing GIS applications using the web interface.

Examples of Attribute Searches and Queries
Identify– The image below shows a commonly used tool in GIS. The user clicks on the “identify” tool (circled in red) and then clicks on a feature in the map which will then display all of the attributes associated with that feature. This tool provides a quick
“snapshot” of attributes associated with a feature and does not allow the user to save or
work with the popup.

Examples of Attribute Searches and Queries
Select by Attributes – The “select by attributes” tool allows the user to build a SQL query and then view the results of the query on the map display. The user can also see the results in the layer’s attribute table, further refine the query, or export the data into a new table. The example below
shows features from the 2013 EPA_Temperature_2013 layer greater than January 2nd 2013 and less than or equal to April 1st 2013. Another advantage of using this tool is that more complex queries can be saved an re-used at a later date.

Examples of Spatial Searches and Queries
– Select by Location
In GIS there will be instances where you will be required to find features that are near or that intersect other features. While the select by attributes tool we saw on the previous slide is a powerful tool it is limited in that it can only use SQL language to select features. The Select by Location is an example of a tool that can be used to select features from one layer that intersect, touch, are within a distance of, etc. a feature or features in another layer. The example to the right retrieves all of the 2009 seismic events that are within a mile of the selected countries. Once the data is selected in can be exported to another feature layer or viewed as an attribute table which can be exported.

2

Foundations of Geographic Information Science

GIS 5103

Fall 2019 – Week Six

Vector Analysis

Objectives:
By the end of week seven you will be able to:
Describe Measurements in GIS;
Perform data queries in ArcGIS Pro
Explain techniques used in reclassification of data
Describe Buffering Functions and map overlays

Earlier in this course we covered some of the questions that can be answered by GIS. Over the past few weeks we have been discussing different types of data, data formats, data models, data creation and editing, and finding/correcting errors in data. Once we have our software, computer hardware, personnel, and data in place we can start to answer questions with our system. This week we will take a look at some of the questions a GIS can answer and also
some of the tools GIS uses to answer the questions like the ones listed below:
Where are particular features found?
What geographical patterns exist?
Where have changes occurred over a given period?
Where do certain conditions apply?
What will the spatial implications be if an organization takes a certain action?
Common questions answered by GIS

Questions for GIS Analysis
Of course “where” is the predominant question but there are some additional questions
beyond just where. Below are some examples of the questions beyond where.
Location: what is at or where is it? E.g., where is the nearest Mexican restaurant to my house?
Patterns: what spatial patterns exist? E.g., where do most of the students live in Boston?
Trends: what has changed since? Has the number of deer incidents on roads
reduced since commuters have used the deer whistles?

Conditions: where can I find something close to, within or by?
Modeling/Implications: what if? An example may include: if a new baseball park were built adjacent to Gillette stadium, how would traffic flow be affected?
Our data analysis procedures assist us in answering these questions. Let us look at them more closely now.

Making Measurements in GIS
Calculating lengths, perimeters and areas are common applications in GIS. For example, measuring the length of a road from a digital map is pretty straightforward. However, a GIS can make measurements on other types of features as well. There are a number of tools at your disposal and depending on the type of data you are using (raster or vector). The next few slides will introduce you to some of those measuring tools and techniques.

a. Length (distance); b. Perimeter; c. Area
Vector GIS measurements: (a) distance;
(b) area; and (c)perimeter can be measured as the sum of straight line lengths.
The figure to the right shows how these
quantities are calculated by the GIS, but as users it is much easier for us. All that is needed is to click on the measure tool in the toolbar of the ArcMap GIS software. The GIS does the rest.
In Vector GIS, length, perimeter and area data can be stored as attributes in a database. By storing the lengths and areas as attributes we can select features meeting a certain criteria and sum the areas or lengths. For example how many acres of wetlands do we have in a city?
Measurements in GIS

The figure above from Esri shows 3 and 5 minute drive time polygons from a downtown area. Drivetime polygons can be measured using either a straight line distance or by using road networks and traffic conditions.
Example of a Measurement

Queries in GIS
Queries are the main data retrieval operations that are useful at all stages of GIS analysis for everything from data quality checking to a final analysis.
Features may be selected by two primary methods:
Select by Location – select features based on their location relative to features in another layer. For instance, if you want to know how many homes were affected by a recent flood, you could select all the homes that fall within the flood boundary. You can use a variety of selection methods to select the point, line, or polygon features in one layer that are near or overlap the features in the same or another layer. (Esri, 2017)
Select by Attribute – Select By Attributes allows you to provide a SQL query expression that is used to
select features that match the selection criteria. (Esri, 2017).

An example procedure for selecting features by attribute might include:
A prospective buyer wants to know where all the 3 bedroom houses are for under $175,000 (apparently it’s possible, just not in Boston). The ArcGIS user can call the Selection menu in the software and then by pressing the “select by attributes” option. This will provide a new window with
fields, unique values and numerical operators within it. This window allows
the attribute table to be queried. A command line query question may then look like:
status=Y + Bedroom=3+ saleprice=$175000.

Where: status refers to house availability; 3 refers to the desired number of bedrooms; and sale price refers to… well, the sale price,and a dream sale price at that for any buyer in Boston.
Select by Attribute

Selecting by attributes involves the use of Boolean Operators (Pg. 380) in which use the conditions OR, AND, or NOT. In Esri’s ArcMap software we can use the “Select by Attributes” dialog box to build an expression. The dialog box to the right lists the layer “zebra_mussel” we want to select features from, the attribute fields “STATE” and “YEAR”. The buttons and dialog box
can be used to select all of the points from th. e feature layer “zebra_mussel” that are in the state of New York and are from 2005.
We can use this dialog box to build very complex expressions and even save the expressions for future use. The next slide will show an example of how more complex expressions can be built.
Select by Attributes

Queries Continued
This image illustrates how an attributes or non-spatial query is executed in ArcGIS. In previous chapters we had
discussed how SQL Queries can be used to select features from an attribute table. Notice the query “SELECT * FROM
Parcel_Poly_BT_2008_LOCID WHERE: BEDROOMS = 3 AND FULLBATHS= 2 AND TOTALVALUE<175,000. Essentially, the query is telling the software to find all of the homes in Waltham that have 2 full baths, 3 Bedrooms, that cost less than $175,000.By using operators such as “AND”, “OR”,“NOT”,etc. we can build queries based on whatever criteria we choose. I can execute this query by clicking on the “Apply” button. The parcels that are selected can then be exported to create a new layer or table. We can also modify our query to refine our selection. You will have an opportunity to gain some hands-on experience working with these queries in the co-requisite with this course; GIS5102. An example procedure of selecting features by location might include: An analyst wants to know where all the osprey nests are within a distance of 0.5 miles to a major highway. The ArcGIS user can call the Selection menu in the software and then by pressing the “selecting by location” tool. This will allow the user to use the operators: “are within a distance of” (0.5miles to freeway) and “contain” (osprey nests) in order to carry out the query. In the following slide, an example is included of selecting particular soil types by query as part of a step to produce a soil erosion model. It is important to realize that these techniques can be used sequentially and do not have to be standalone. Queries Continued Example of a typical spatial query A spatial query is used to find features that are adjacent, within a distance of, are contained by, crossed by, etc. The graphic to the right looks similar to the “Select by Attributes” dialog box on the previous slides but the main difference is that instead of using SQL queries or Boolean operators we are able to build expressions to find features that are near, within, are within a distance of, touching, or intersecting another feature. The following set of slides illustrates how a “Spatial Query”is executed. In this following example we will be selecting all of the parcels within 300 feet of our selected parcel (highlighted in red). Example of a typical spatial query We can select features adjacent to or within a specified distance of a selected feature using “Select by Location”. Example of a typical spatial query Notice the “Select by Location” toolbox that now appears on the screen. The first dropdownbox allows me to specify which operation I would like to use. In the second box, the “Parcel_Poly” layer is checked off. By checking off the “Parcel_Poly” layer I am specifying which layer I will be querying. I could also use the select by locations dialog to query features that are in separate layers, for instance I could also create a query that would find all of the hydrants within 300 feet of the selected property. The drop-down menu at the bottom of the dialog box allows me specify the type of location query I would like to use. In this example I will use the “are within a distance of” option. Example of a typical spatial query At the bottom of the dialog box I can specify a distance that I would like to use. In this case I will use 300 feet. Once I have built the select by location query I will click on the apply button and all of the parcels within 300 feet of the subject property are now highlighted in red. Earlier in the chapter we looked at ways of finding features that are within a certain distance of a selected feature or features. This distance is commonly referred to as a buffer. Buffers are drawn based on a user specified distance and vary in shape based upon whether the feature being buffered is a point (a), a line (b), or a polygon (c). The diagram below illustrates how these shapes are drawn. If multiple features are being buffered and the buffer polygons overlap you can dissolve the buffer boundaries if you choose to create a continuous buffer polygon. Buffering and neighborhood functions a b c We discussed one way to use a buffer is to designate an area for special protection. In this example, 1000 foot buffer zones (purple) around the rail network (black) are established to define an oil spill impact area. This is what buffers around line features look like. An Example of a Buffer The graphic above illustrates a buffer polygon that was created to show an area 300 feet around a parcel polygon. The buffer is shown in blue. When buffering a polygon, the buffer that is created emulates the shape of the object being buffered. In this case a polygon. An Example of a Buffer Map Overlay: Data Integration One of the most basic questions asked of a GIS is "What's on top of what?" For example: What land use is on top of what soil type? What parcels are within the 100-year floodplain? ("Within" is just another way of saying "on top of.") What roads are within what counties? What wells are within abandoned military bases? To answer such questions before the days of GIS, cartographers would create maps on clear plastic sheets and overlay these sheets on a light table (remember Week 1) to create a new map of the overlaid data. Because overlay yields such valuable information, it was paramount to the development of GIS. There are two types of overlays in GIS Vector and Raster Map Overlay: Data Integration An overlay operation is much more than a simple merging of line work; all the attributes of the features taking part in the overlay are carried through, as shown in the example to the right, where parcels (polygons) and flood zones (polygons) are overlaid (using the Union tool) to create a new polygon dataset. The parcels are split where they are crossed by the flood zone boundary, and new polygons created. The FID_flood value indicates whether polygons are outside (-1) or inside the flood zone, and all polygons retain their original land-use category values. Esri 2017. Map Overlay: Data Integration The total area of each land-use type in the flood zone can be calculated by selecting all polygons within the flood zone (using the Select Layer By Attribute tool, for example) and summarizing the area by land- use type (using the Frequency tool). Following is a chart illustrating the result of this calculation. (Esri 2018). Map Overlay: Data Integration There are a number of vector overlay tools that are common to most commercially available GIS softwares. The graphic to the right lists some of these tools. The tools you use vary based on the output you desire but they generally involve creating or updating new datasets. You will have a chance to use some of these tools in your analysis class and also in GIS5201 – Advanced Spatial Analysis. We have now looked at some of the common tools and techniques used to conduct a GIS analysis. Now that we have described some of the techniques and tools how and when do we apply them? Also, how and where do we begin? Below are five points to consider when conducting a GIS analysis. Frame the question – Begin by defining the objectives of your analysis. What are the primary questions you are trying to answer? Explore and prepare data – Do you have the data you need to conduct the analysis? Do you have to create new data or acquire it from another source? These are critical questions. You may not be able to acquire or develop the data you need from the sources you have access to within the time frame or cost associated with the analysis. Choose analysis methods and tools – What tools or methods do you need to perform your analysis? Perform the analysis – Your analysis should be conducted a number of times to make sure your results make sense. You should have a good working knowledge of the subject matter. Examine and refine results – Examine your results and refine your output products. Are your results repeatable? Your maps and tables should be designed to be easily understood by your intended audience. Common questions answered by GIS GIS 5103 – Fundamentals of GIS Lecture 7 Intro to R-Studio & Python for GIS Week 7 Winter 2019 What is R-Studio & why should I use it? Free open-source integrated coding language. Powerful computing/programing & simple UI Also very popular Intro to R & Python - Lecture 7 Applications & Capabilities Statistics Graphics Programing Calculator GIS Neural Networks Etc. Intro to R & Python - Lecture 7 Intro to R & Python - Lecture 7 Installing https://courses.edx.org/courses/UTAustinX/UT.7.01x/3T2014/56c5437b88fa43cf828bff5371c6a924/ Set working Directory Defining the working area / confining thinking space / Files of interest located here Intro to R & Python - Lecture 7 Import Data Files of interest located in working directory. Intro to R & Python - Lecture 7 Manipulate & Organize Data Intro to R & Python - Lecture 7 Various functions for parameters of interest Able to work with data and create data. Convert, Transpose, data with single commands. Plots / Diagrams plotMF(object.cls) plotMF(object) Intro to R & Python - Lecture 7 Most Popular Uses of R Data Management Conversion, sub-setting, merging, transformations Statistical & Mathematical Analysis ANOVA’s, Modeling, Regressions Programming / Operations / System Interface Automation, Special Conditions Graph / Data Display / Configuration Data Visualization, High Customization Simulations High level scenario modeling Intro to R & Python - Lecture 7 Variables Can define characters/numbers as nearly anything x = 5 First_Name = Mike f = (sin(y) / tan(sum(z)) * variance(y/z) Name – Type – Size – Value Created Title– character/matrix/list – Bytes/KB/MB – Output Intro to R & Python - Lecture 7 Functions & Syntax Mean(x, trim = 0, na.rm = FALSE, …) Function(input, argument 1, argument 2, …) *Commas used to separate arguments/inputs* Intro to R & Python - Lecture 7 Intro to R & Python - Lecture 7 Help Dialogue Intro to R & Python - Lecture 7 What is Python & why should I use it? Object-orientated, high level programming language with dynamic semantic Built in data structures, dynamic typing/binding Used as glue to connect programs / components together. Supports modules / packages / code re-use Intro to R & Python - Lecture 7 Python Libraries = R-Studio Packages GIS Libraries GDAL OGR Pyproj Shapely Mapnik MySQL PostGIS SpatiaLite Intro to R & Python - Lecture 7 How to download Python https://realpython.com/installing-python/ How to Pip install libraries https://packaging.python.org/tutorials/installing-packages/ Intro to R & Python - Lecture 7 Intro to R & Python - Lecture 7 How we will use python & Libraries Example of ArcGIS & python https://developers.arcgis.com/python/sample-notebooks/chennai-floods-analysis/ Intro to R & Python - Lecture 7 GIS Automation Python R-Studio Model Builder Intro to R & Python - Lecture 7 Common Automated processes Address systems Data Updating Map Templating Spatial Joins Digitizing Calculations Models Work Flows Intro to R & Python - Lecture 7 After reviewing this lecture you should be able to: List applications/uses of a coding languages Know many of the popular input file types Understand/Replicate functions, syntax, style Know the names of essential packages/libraries List commonly automated tasks Be able to download & Install the softwares Intro to R & Python - Lecture 7 GIS 5103 – Fundamentals of GIS Lecture 8 3D GIS Dimensionality A dimension is the minimum amount of coordinates needed to define the mathematical space of an object. Lecture 8 – 3D GIS 2D A figure that has only length & height as its dimensions. 2D shapes lie on a flat surface; known as plane figures or plane shapes. While they have areas, 2D shapes have no volume. 2D figures are plotted on two axes, the x- and y-axes. Lecture 8 – 3D GIS 3D Length + Height + Width X + Y + Z Volume + Surface Area Examples include: 3D multi-patch building, roof, interior floors, and foundation would all contain different z-values for the same 2D coordinate. Aircraft's 3D position or a walking trail up a mountain, would only have a single z-value for each X,Y location. Lecture 8 – 3D GIS 2.5D Highly used in GIS to represent Z data that is not continuous = 3D Z data does not need to be elevation. Pollution, # of cases, etc. Lecture 8 – 3D GIS 3D file types .3dd .sxd When you import an ArcGlobe or ArcScene document: First the .3dd file opens by default in global mode Secondly the .sxd file opens in local mode. Any new blank scene view defaults to global mode. Lecture 8 – 3D GIS Data Types Vector Features Point / Line / Polygon Surface types TIN (Triangular Irregular Networks) DEM’s (Digital Elevation Models) Raster Surface LAS Dataset Lecture 8 – 3D GIS 3D Styles Points Geometric Shapes Models Markers Imported Lines Textured Symbols Geometric Shapes Polygons Texture Fill Basic Colors Lecture 8 – 3D GIS Lecture 8 – 3D GIS Perspective View Perspective drawing is the most common drawing mode in 3D, where features in the foreground are shown larger than those off in the distance. This matches the way we see the world in our day-to-day lives, and the result is a realistic representation of 3D content. All scenes open in perspective viewing mode. You can switch between Perspective Perspective View and Parallel Isometric View viewing modes using the Drawing Mode drop-down menu in the Scene group on the View tab. Lecture 8 – 3D GIS Parallel / Isometric View Parallel drawing renders the 3D view using a parallel projection, where features of the same physical size are rendered on-screen identically, regardless of their distance from the viewing camera. Parallel drawing is useful for architectural drawings, as well as for representing statistical data in a 3D view, such as extruded shapes symbolizing numeric values. Lecture 8 – 3D GIS 3D Analyst Tools Data Conversion/Preparation Txt / Binary / Feature class / Raster / TIN Surface Creation Interpolation / LASD Creation Surface Analysis Aspect / Slope / Contour / Feature Interpolation 3D Operator & Visibility Skyline / Inter-visibility / Sun Shadow Analysis Lecture 8 – 3D GIS There are five exploratory analysis tools: The Line of Sight tool creates sight lines to determine if one or more targets are visible from a given observer location. The View Dome tool determines the parts of a sphere that are visible from an observer located at the center. The Viewshed tool determines the visible surface area from a given observer location through a defined viewing angle. The Slice tool visually cuts through the view's display to reveal hidden content. The Cut and Fill tool provides volume calculations while visually demonstrating the surface area where the ground will be filled or removed. Lecture 8 – 3D GIS Exploratory Analysis Tools The first three analysis tools determine the visible sight lines, field of view, or spherical visibility coverage. The Slice tool temporarily removes displayed content using clipping planes as volumes. The Cut and Fill tool calculates volumetric displacement using a planar surface. In all cases, the results are temporary and must be saved as feature classes if you want to reload the interactive objects again. Lecture 8 – 3D GIS illumination properties A unique element to authoring scenes is that you can also define illumination properties. This includes properties such as the time of day, whether or not the sun casts a shadow, and how much ambient light is used. In global mode, you can also simulate atmospheric diffusion. Lecture 8 – 3D GIS By the end of this week you should be able to: Understand dimensionality List the differences between 2D vs 2.5D vs 3D Know the data types, Views, & Scene Types for 3D GIS Understand Surfaces & How to create/find them Familiarize yourself with the Exploratory & Analyst tools List the unique properties of 3D GIS vs Traditional 2D maps Comprehend the issues of 3D GIS Lecture 8 – 3D GIS