Spatial Data Types

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Jeff Jenness

jeffj@jennessent.com (928) 607-4638

Main Topics

Main Topics

• Managing Spatial Datasets; copying, sharing, etc.

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Main Topics

• Managing Spatial Datasets; copying, sharing, etc.
• Workspaces

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Main Topics

• Managing Spatial Datasets; copying, sharing, etc.
• Workspaces
• Vector Data

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Main Topics

• Managing Spatial Datasets; copying, sharing, etc.
• Workspaces
• Vector Data
• Raster Data

​

​

Main Topics

• Managing Spatial Datasets; copying, sharing, etc.
• Workspaces
• Vector Data
• Raster Data
• Special cases of Spatial Data

​

​

Main Topics

• Managing Spatial Datasets; copying, sharing, etc.
• Workspaces
• Vector Data
• Raster Data
• Special cases of Spatial Data
• Metadata

​

​

Main Topics

• Managing Spatial Datasets; copying, sharing, etc.
• Workspaces
• Vector Data
• Raster Data
• Special cases of Spatial Data
• Metadata
• Data Management Strategies

​

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Spatial Data

• Spatial data is tricky to work with outside of a GIS.

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Spatial Data

• Spatial data is tricky to work with outside of a GIS.
• A single dataset is often composed of multiple files, sometimes distributed into different folders.

Spatial Data

• Spatial data is tricky to work with outside of a GIS.
• A single dataset is often composed of multiple files, sometimes distributed into different folders.
• This makes it harder to share spatial data. Not easy to just email someone a spatial dataset, like you can with other files.

For Example:

Shapefile datasets:

• Relatively simple
• Composed of at least 3 and often many more files, all in the same folder and all named with the same name.

The single spatial dataset named Beale_Wagon_Road_Campsites (seen in Catalog on the top) requires eight separate files on your hard drive (as seen in Windows File Explorer on the bottom).

TIN datasets:

• Triangulated Irregular Network
• A bit more complicated
• Composed of a folder of files, plus
• Additional files in an “Info” folder

The single spatial dataset named Wallow_Burn_Severity (seen in Catalog on the top) requires two separate folders on your hard drive (as seen in Windows File Explorer on the bottom).

The single spatial dataset named mdow_hlshd (seen in Catalog on the top) actually requires two separate folders and one additional file on your hard drive (as seen in Windows File Explorer on the bottom).

GRID datasets:

• Proprietary Esri raster format
• Even more complicated
• Composed of a folder of files, plus
• Additional files in an “Info” folder
• Additional files in containing folder

Catalog (on the top) shows you a single spatial dataset in this geodatabase. Windows File Explorer (on the bottom) is not able to view or extract this dataset at all.

Geodatabases:

• Wonderful way to store data
• Requires GIS to view
• No way to identify individual datasets using Windows File Explorer.

• It is certainly possible to share spatial datasets. You just have to be careful about how you do it.

• It is certainly possible to share spatial datasets. You just have to be careful about how you do it.
• Shapefiles: Good idea to compress all individual sub-files into single zip file.

• It is certainly possible to share spatial datasets. You just have to be careful about how you do it.
• Shapefiles: Good idea to compress all individual sub-files into single zip file.
• Other complex datasets: Export them into an empty folder or workspace, then compress that entire workspace into a single zip file.

• It is certainly possible to share spatial datasets. You just have to be careful about how you do it.
• Shapefiles: Good idea to compress all individual sub-files into single zip file.
• Other complex datasets: Export them into an empty folder or workspace, then compress that entire workspace into a single zip file.
• If you don’t have an alternative, 7-Zip is a good free compression utility for Windows and Linux (www.7-zip.org). WinZip is a good commercial utility (www.winzip.com).

• It is certainly possible to share spatial datasets. You just have to be careful about how you do it.
• Shapefiles: Good idea to compress all individual sub-files into single zip file.
• Other complex datasets: Export them into an empty folder or workspace, then compress that entire workspace into a single zip file.
• If you don’t have an alternative, 7-Zip is a good free compression utility for Windows and Linux (www.7-zip.org). WinZip is a good commercial utility (www.winzip.com).
• Use Catalog to review, move, copy and/or delete spatial datasets.

• Just FYI: Esri also offers a system for sharing spatial data called ArcGIS Online (aka “AGOL”).

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• Just FYI: Esri also offers a system for sharing spatial data called ArcGIS Online (aka “AGOL”).
• Alternatively, Portal, which looks and acts the same as ArcGIS Online, but it is hosted by your organization.

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• Just FYI: Esri also offers a system for sharing spatial data called ArcGIS Online (aka “AGOL”).
• Alternatively, Portal, which looks and acts the same as ArcGIS Online, but it is hosted by your organization.
• Both ArcGIS Online and Portal let you store your data “in the cloud”.

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• Just FYI: Esri also offers a system for sharing spatial data called ArcGIS Online (aka “AGOL”).
• Alternatively, Portal, which looks and acts the same as ArcGIS Online, but it is hosted by your organization.
• Both ArcGIS Online and Portal let you store your data “in the cloud”.
• Makes it easier to share data with the world or with your organization, but not necessarily with specific individuals.

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So what is a Workspace?

• Before discussing individual types of datasets, you should understand what a Workspace is.

So what is a Workspace?

• Before discussing individual types of datasets, you should understand what a Workspace is.
• ArcGIS Pro tools, commands and help files refer to workspaces a lot.

So what is a Workspace?

• Before discussing individual types of datasets, you should understand what a Workspace is.
• ArcGIS Pro tools, commands and help files refer to workspaces a lot.
• Simply a location where data is kept.

So what is a Workspace?

• Before discussing individual types of datasets, you should understand what a Workspace is.
• ArcGIS Pro tools, commands and help files refer to workspaces a lot.
• Simply a location where data is kept.
• The “workspace” does not imply that all your work must be done in that location, or that all your data must be stored there. Often you will access several workspaces in any particular GIS project.

Two Broad Categories of Workspace�(with a few exceptions)

Two Broad Categories of Workspace�(with a few exceptions)

• Folders

Two Broad Categories of Workspace�(with a few exceptions)

• Folders
• Simply directories or folders on the hard drive.

Two Broad Categories of Workspace�(with a few exceptions)

• Folders
• Simply directories or folders on the hard drive.
• If your data are in a folder, those data are called file-based data.

Two Broad Categories of Workspace�(with a few exceptions)

• Folders
• Simply directories or folders on the hard drive.
• If your data are in a folder, those data are called file-based data.

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• Geodatabases

Two Broad Categories of Workspace�(with a few exceptions)

• Folders
• Simply directories or folders on the hard drive.
• If your data are in a folder, those data are called file-based data.

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• Geodatabases
• Specialized workspaces that allow you to save your data in a more sophisticated manner.

Two Broad Categories of Workspace�(with a few exceptions)

• Folders
• Simply directories or folders on the hard drive.
• If your data are in a folder, those data are called file-based data.

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• Geodatabases
• Specialized workspaces that allow you to save your data in a more sophisticated manner.
• In most cases, geodatabases allow you to load, view and analyze data faster than file-based data.

Warning! Tedious slides to follow

• It’s hard to make an interesting graphic of a database…

Geodatabases, continued…

• Two general types of geodatabases

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets
• History of changes to the data

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets
• History of changes to the data
• Different versions of the data

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets
• History of changes to the data
• Different versions of the data
• Special symbology and display parameters for the data

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets
• History of changes to the data
• Different versions of the data
• Special symbology and display parameters for the data
• Lots of concurrent users

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets
• History of changes to the data
• Different versions of the data
• Special symbology and display parameters for the data
• Lots of concurrent users
• Set sophisticated permission levels for users

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases: These can store all your individual datasets, plus:
• Relationships between datasets
• History of changes to the data
• Different versions of the data
• Special symbology and display parameters for the data
• Lots of concurrent users
• Set sophisticated permission levels for users

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• Used more for larger data-centers, not so much for smaller organizations that focus more on analytical work.

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Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases, continued
• Generally they are “relational database management systems” (RDBMS) produced by other companies. ArcGIS connects to them using ArcSDE (Spatial Database Engine).

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases, continued
• Generally they are “relational database management systems” (RDBMS) produced by other companies. ArcGIS connects to them using ArcSDE (Spatial Database Engine).
• Relational geodatabases are a big thing in ArcGIS. Many people make an entire career out of database administration.

Geodatabases, continued…

• Two general types of geodatabases
• Relational Geodatabases, continued
• Generally they are “relational database management systems” (RDBMS) produced by other companies. ArcGIS connects to them using ArcSDE (Spatial Database Engine).
• Relational geodatabases are a big thing in ArcGIS. Many people make an entire career out of database administration.
• We won’t go into relational geodatabases in this class. The topic requires much more time than we have, and we don’t need them for the work you are doing in this class.

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases: These store all your individual datasets, plus:

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases: These store all your individual datasets, plus:
• Relationships between datasets, although not to the degree that relational geodatabases can.

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases: These store all your individual datasets, plus:
• Relationships between datasets, although not to the degree that relational geodatabases can.

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• Two specific types of Non-Relational Geodatabases:

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases: These store all your individual datasets, plus:
• Relationships between datasets, although not to the degree that relational geodatabases can.

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• Two specific types of Non-Relational Geodatabases:
• Mobile Geodatabase: A geodatabase built for Esri’s mobile apps. Exists as a single file, and appears to only hold vector and tabular data.

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases: These store all your individual datasets, plus:
• Relationships between datasets, although not to the degree that relational geodatabases can.

​

• Two specific types of Non-Relational Geodatabases:
• Mobile Geodatabase: A geodatabase built for Esri’s mobile apps. Exists as a single file, and appears to only hold vector and tabular data.
• File Geodatabase: A special Esri-designed geodatabase that has lots of good features.

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Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases: These store all your individual datasets, plus:
• Relationships between datasets, although not to the degree that relational geodatabases can.

​

• Two specific types of Non-Relational Geodatabases:
• Mobile Geodatabase: A geodatabase built for Esri’s mobile apps. Exists as a single file, and appears to only hold vector and tabular data.
• File Geodatabase: A special Esri-designed geodatabase that has lots of good features.

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• In this class, we won’t use the Mobile Geodatabase. We’ll use the File Geodatabase a lot, though!

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases, continued…
• Are these really “non-relational”? That’s debatable.

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases, continued…
• Are these really “non-relational”? That’s debatable.
• They provide far less functionality than the ArcSDE-connected databases.

Geodatabases, continued…

• Two general types of geodatabases, Continued…
• Non-Relational Geodatabases, continued…
• Are these really “non-relational”? That’s debatable.
• They provide far less functionality than the ArcSDE-connected databases.
• Unless you are managing data for a large enterprise, these usually work just fine for most projects.

More on the File Geodatabase

More on the File Geodatabase

• They allow you to store most Esri spatial data types.

More on the File Geodatabase

• They allow you to store most Esri spatial data types.
• Exceptions are cloud-based and online datasets

More on the File Geodatabase

• They allow you to store most Esri spatial data types.
• Exceptions are cloud-based and online datasets
• They can store unlimited amounts of data

More on the File Geodatabase

• They allow you to store most Esri spatial data types.
• Exceptions are cloud-based and online datasets
• They can store unlimited amounts of data
• Although individual datasets are limited to 1TB.

More on the File Geodatabase

• They allow you to store most Esri spatial data types.
• Exceptions are cloud-based and online datasets
• They can store unlimited amounts of data
• Although individual datasets are limited to 1TB.
• They have fast data access times.

More on the File Geodatabase

• They allow you to store most Esri spatial data types.
• Exceptions are cloud-based and online datasets
• They can store unlimited amounts of data
• Although individual datasets are limited to 1TB.
• They have fast data access times.
• They can be compressed with no loss in performance.

More on the File Geodatabase

• They allow you to store most Esri spatial data types.
• Exceptions are cloud-based and online datasets
• They can store unlimited amounts of data
• Although individual datasets are limited to 1TB.
• They have fast data access times.
• They can be compressed with no loss in performance.
• They show up as a single folder in Windows File Explorer.

What is Not a Workspace?

What is Not a Workspace?

• No single dataset is a workspace!

What is Not a Workspace?

• No single dataset is a workspace!
• Remember that workspaces are containers for data, not the data themselves!

What is Not a Workspace?

• No single dataset is a workspace!
• Remember that workspaces are containers for data, not the data themselves!
• An ArcGIS Pro Project (*.aprx file) is not a workspace!

What is Not a Workspace?

• No single dataset is a workspace!
• Remember that workspaces are containers for data, not the data themselves!
• An ArcGIS Pro Project (*.aprx file) is not a workspace!
• It is not a container holding data. In fact, it has no data in it at all. It has layers, which tell it where the datasets are located on the hard drive or network, but it does not hold the datasets themselves.

In this class…

• We will use two types of workspace:

In this class…

• We will use two types of workspace:
• Folders (containing shapefiles, tables and some images).

In this class…

• We will use two types of workspace:
• Folders (containing shapefiles, tables and some images).
• File Geodatabases (containing all types of spatial data).

A Note regarding Excel Files

A Note regarding Excel Files

• In ArcMap (prior to ArcGIS Pro), some of you may have used Excel files (which were actually workspaces because they were Containers holding tables (worksheets).

A Note regarding Excel Files

• In ArcMap (prior to ArcGIS Pro), some of you may have used Excel files (which were actually workspaces because they were Containers holding tables (worksheets).
• Great for loading tabular data

A Note regarding Excel Files

• In ArcMap (prior to ArcGIS Pro), some of you may have used Excel files (which were actually workspaces because they were Containers holding tables (worksheets).
• Great for loading tabular data
• Many of us record data in Excel

A Note regarding Excel Files

• In ArcMap (prior to ArcGIS Pro), some of you may have used Excel files (which were actually workspaces because they were Containers holding tables (worksheets).
• Great for loading tabular data
• Many of us record data in Excel
• ArcGIS Pro no longer recognizes Excel files unless you download and install extra drivers:

A Note regarding Excel Files

• In ArcMap (prior to ArcGIS Pro), some of you may have used Excel files (which were actually workspaces because they were Containers holding tables (worksheets).
• Great for loading tabular data
• Many of us record data in Excel
• ArcGIS Pro no longer recognizes Excel files unless you download and install extra drivers:
• https://pro.arcgis.com/en/pro-app/latest/help/data/excel/work-with-excel-in-arcgis-pro.htm

A Note regarding Excel Files

• In ArcMap (prior to ArcGIS Pro), some of you may have used Excel files (which were actually workspaces because they were Containers holding tables (worksheets).
• Great for loading tabular data
• Many of us record data in Excel
• ArcGIS Pro no longer recognizes Excel files unless you download and install extra drivers:
• https://pro.arcgis.com/en/pro-app/latest/help/data/excel/work-with-excel-in-arcgis-pro.htm
• Be warned that this can cause compatibility problems with ArcMap, since ArcGIS Pro is 64-bit (and requires the 64-bit version of Excel), while ArcMap is 32-bit.

A Note on Catalog and Workspaces…

A Note on Catalog and Workspaces…

• Catalog acts a lot like Windows File Explorer, but it will not automatically show you all directories.

A Note on Catalog and Workspaces…

• Catalog acts a lot like Windows File Explorer, but it will not automatically show you all directories.
• Also can’t use 7-Zip or WinZip from Catalog

A Note on Catalog and Workspaces…

• Catalog acts a lot like Windows File Explorer, but it will not automatically show you all directories.
• Also can’t use 7-Zip or WinZip from Catalog
• The first time you open it, you may not see any spatial data listed at all.

A Note on Catalog and Workspaces…

• Catalog acts a lot like Windows File Explorer, but it will not automatically show you all directories.
• Also can’t use 7-Zip or WinZip from Catalog
• The first time you open it, you may not see any spatial data listed at all.
• You need to tell Catalog which folders to “connect” to.

A Note on Catalog and Workspaces…

• Catalog acts a lot like Windows File Explorer, but it will not automatically show you all directories.
• Also can’t use 7-Zip or WinZip from Catalog
• The first time you open it, you may not see any spatial data listed at all.
• You need to tell Catalog which folders to “connect” to.
• Connections are stored with your ArcGIS Pro Project.

A Note on Catalog and Workspaces…

• Catalog acts a lot like Windows File Explorer, but it will not automatically show you all directories.
• Also can’t use 7-Zip or WinZip from Catalog
• The first time you open it, you may not see any spatial data listed at all.
• You need to tell Catalog which folders to “connect” to.
• Connections are stored with your ArcGIS Pro Project.
• If you want the connection to be Permanent, add it to your “Favorites”

Finally, a slide that is not all text!

• You will do this in the lab, and then the data folder will be available to you the next time you open ArcGIS Pro.

Another Note on Catalog and Workspaces…

Another Note on Catalog and Workspaces…

• Also unlike Windows File Explorer, Catalog does not keep a live connection to a workspace

Another Note on Catalog and Workspaces…

• Also unlike Windows File Explorer, Catalog does not keep a live connection to a workspace.
• If you make a change to a workspace (like add a dataset), Catalog will not automatically know you have made the change.

Another Note on Catalog and Workspaces…

• Also unlike Windows File Explorer, Catalog does not keep a live connection to a workspace.
• If you make a change to a workspace (like add a dataset), Catalog will not automatically know you have made the change.
• If you know there is a dataset in a workspace, and Catalog doesn’t show it, then click the [F5] button to refresh Catalog.

Another Note on Catalog and Workspaces…

• Also unlike Windows File Explorer, Catalog does not keep a live connection to a workspace.
• If you make a change to a workspace (like add a dataset), Catalog will not automatically know you have made the change.
• If you know there is a dataset in a workspace, and Catalog doesn’t show it, then click the [F5] button to refresh Catalog.
• You can also right-click on a workspace and choose “Refresh” from the menu.

Another Note on Catalog and Workspaces…

Now, Actual Spatial Data

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:
• Precisely-defined spatial objects like Points, Lines and Polygons

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:
• Precisely-defined spatial objects like Points, Lines and Polygons
• Defined by one or more points called Vertices.

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:
• Precisely-defined spatial objects like Points, Lines and Polygons
• Defined by one or more points called Vertices.
• Each vertex has X, Y and sometimes Z coordinates.

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:
• Precisely-defined spatial objects like Points, Lines and Polygons
• Defined by one or more points called Vertices.
• Each vertex has X, Y and sometimes Z coordinates.
• Points are composed of a single vertex.

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:
• Precisely-defined spatial objects like Points, Lines and Polygons
• Defined by one or more points called Vertices.
• Each vertex has X, Y and sometimes Z coordinates.
• Points are composed of a single vertex.
• Lines (called Polylines in the GIS world) are linear features composed of a series of connected vertices.

Now, Actual Spatial Data

• Two Broad Categories: Vector and Raster
• Vector Data:
• Precisely-defined spatial objects like Points, Lines and Polygons
• Defined by one or more points called Vertices.
• Each vertex has X, Y and sometimes Z coordinates.
• Points are composed of a single vertex.
• Lines (called Polylines in the GIS world) are linear features composed of a series of connected vertices.
• Polygons are areal features composed of connected vertices, where the last vertex is connected back to the first vertex.

Vector Example

“Multipart” Vectors

• All vector objects can be composed of a single or multiple parts.

“Multipart” Vectors

• All vector objects can be composed of a single or multiple parts.
• The dataset may contain Hawaii as a single object with seven parts, or as seven separate objects.

“Multipart” Vectors

• All vector objects can be composed of a single or multiple parts.
• The dataset may contain Hawaii as a single object with seven parts, or as seven separate objects.
• Multi-part points are called multipoints, but multipart polylines or polygons are just called polylines or polygons.

Vector Special Cases

Vector Special Cases

• Almost all vector data are made of some combination of points, polylines or polygons.

Vector Special Cases

• Almost all vector data are made of some combination of points, polylines or polygons.
• All vector data we work with in this workshop will be points, polylines or polygons.

Vector Special Cases

• Almost all vector data are made of some combination of points, polylines or polygons.
• All vector data we work with in this workshop will be points, polylines or polygons.
• There are exceptions, especially in CAD (Computer-Aided Design) datasets:

Vector Special Cases

• Almost all vector data are made of some combination of points, polylines or polygons.
• All vector data we work with in this workshop will be points, polylines or polygons.
• There are exceptions, especially in CAD (Computer-Aided Design) datasets:

• Some datasets contain true curves such as circular arcs, elliptical arcs or Bezier curves.

Common Vector Dataset Types

Common Vector Dataset Types

1. Feature Class

Common Vector Dataset Types

1. Feature Class
2. Feature Dataset

Common Vector Dataset Types

1. Feature Class
2. Feature Dataset
3. Triangulated Irregular Network

1. Feature Class

1. Feature Class

• The most common example of vector dataset.

1. Feature Class

• The most common example of vector dataset.
• Simply a collection of points, polylines or polygons, along with attribute data. Points, Polylines and Polygons are called Shape Types.

1. Feature Class

• The most common example of vector dataset.
• Simply a collection of points, polylines or polygons, along with attribute data. Points, Polylines and Polygons are called Shape Types.
• Any feature class can only contain one shape type! You cannot have a feature class that contains both polygons and points, for example.

1. Feature Class

• The most common example of vector dataset.
• Simply a collection of points, polylines or polygons, along with attribute data. Points, Polylines and Polygons are called Shape Types.
• Any feature class can only contain one shape type! You cannot have a feature class that contains both polygons and points, for example.
• Individual vector objects are called geometries or shapes.

1. Feature Class

• The most common example of vector dataset.
• Simply a collection of points, polylines or polygons, along with attribute data. Points, Polylines and Polygons are called Shape Types.
• Any feature class can only contain one shape type! You cannot have a feature class that contains both polygons and points, for example.
• Individual vector objects are called geometries or shapes.
• Each geometry/shape is paired with a set of attribute data. The combination of geometry and attribute data makes a feature.

1. Feature Class

• The most common example of vector dataset.
• Simply a collection of points, polylines or polygons, along with attribute data. Points, Polylines and Polygons are called Shape Types.
• Any feature class can only contain one shape type! You cannot have a feature class that contains both polygons and points, for example.
• Individual vector objects are called geometries or shapes.
• Each geometry/shape is paired with a set of attribute data. The combination of geometry and attribute data makes a feature.
• Feature classes can be either a shapefile (which is a file-based dataset) or stored in a geodatabase.

Feature Class Example

Features

Attributes

Point Feature Class

• Points are a single pair of X- and Y-Coordinates
• They have no area, but they can be symbolized according to attribute values.

Polyline Feature Class

• Polylines are a connected linear series of vertices.
• They have no width, but they can be symbolized according to attribute values.

“A line is a dot that went for a walk..” Paul Klee

Polygon Feature Class

• Polygons are a connected series of vertices, with the last vertex connected back to the first vertex.

Multipatch Feature Class

• Relatively uncommon.
• Polygons (usually composed of triangles) form 3-dimensional objects.
• Used a lot in 3D analysis, but we won’t use them in this class.

2. Feature Dataset

2. Feature Dataset

• Not an intuitive name!

2. Feature Dataset

• Not an intuitive name!
• A set of several feature classes (and possibly other data types) that are related in some way, and stored as a single object.

2. Feature Dataset

• Not an intuitive name!
• A set of several feature classes (and possibly other data types) that are related in some way, and stored as a single object.
• For example, a polyline feature class of water pipes, and a point feature class of pumping stations.

2. Feature Dataset

• Not an intuitive name!
• A set of several feature classes (and possibly other data types) that are related in some way, and stored as a single object.
• For example, a polyline feature class of water pipes, and a point feature class of pumping stations.
• Almost always stored in a geodatabase.

2. Feature Dataset

• Not an intuitive name!
• A set of several feature classes (and possibly other data types) that are related in some way, and stored as a single object.
• For example, a polyline feature class of water pipes, and a point feature class of pumping stations.
• Almost always stored in a geodatabase.
• Exceptions are Coverages, Smart Data Compression datasets and CAD datasets.

Feature Dataset Example

Another Feature Dataset Example

3. Triangulated Irregular Networks (TINs)

3. Triangulated Irregular Networks (TINs)

• A special type of vector data which forms a 3-dimensional surface covering a landscape.

3. Triangulated Irregular Networks (TINs)

• A special type of vector data which forms a 3-dimensional surface covering a landscape.
• Composed of a mesh of 3D triangles.

3. Triangulated Irregular Networks (TINs)

• A special type of vector data which forms a 3-dimensional surface covering a landscape.
• Composed of a mesh of 3D triangles.
• Unlike feature classes, TINs do not store attribute data.

3. Triangulated Irregular Networks (TINs)

• A special type of vector data which forms a 3-dimensional surface covering a landscape.
• Composed of a mesh of 3D triangles.
• Unlike feature classes, TINs do not store attribute data.
• TINs provide several useful methods to measure slope, aspect or elevation at any point on the TIN surface.

3. Triangulated Irregular Networks (TINs)

• A special type of vector data which forms a 3-dimensional surface covering a landscape.
• Composed of a mesh of 3D triangles.
• Unlike feature classes, TINs do not store attribute data.
• TINs provide several useful methods to measure slope, aspect or elevation at any point on the TIN surface.
• TINs are good at illustrating topography and landscape shape.

3. Triangulated Irregular Networks (TINs)

• A special type of vector data which forms a 3-dimensional surface covering a landscape.
• Composed of a mesh of 3D triangles.
• Unlike feature classes, TINs do not store attribute data.
• TINs provide several useful methods to measure slope, aspect or elevation at any point on the TIN surface.
• TINs are good at illustrating topography and landscape shape.
• Also useful for calculating surface area or volume.

TIN Example

Raster Data

• Remember Two Broad Categories: Vector and Raster

Raster Data

• Remember Two Broad Categories: Vector and Raster
• Raster Data:

Raster Data

• Remember Two Broad Categories: Vector and Raster
• Raster Data:
• Composed of square pixels that completely cover a region. Occasionally they have rectangular pixels.

Raster Data

• Remember Two Broad Categories: Vector and Raster
• Raster Data:
• Composed of square pixels that completely cover a region. Occasionally they have rectangular pixels.
• Each pixel has a value (such as a color or number) which represents the general raster value within the pixel area.

Raster Data

• Remember Two Broad Categories: Vector and Raster
• Raster Data:
• Composed of square pixels that completely cover a region. Occasionally they have rectangular pixels.
• Each pixel has a value (such as a color or number) which represents the general raster value within the pixel area.
• Not as precisely defined as vector data. The pixel value is a general descriptor of the cell, not the value at an exact location.

Raster Example

Raster Example

• They are often images, but with spatial parameters so we know exactly where they are located.

Raster Example

• They are often images, but with spatial parameters so we know exactly where they are located.
• The pixel cell size on the ground is called the Resolution.

A Comment on Raster Resolution

A Comment on Raster Resolution

• The resolution does not automatically imply precision!

A Comment on Raster Resolution

• The resolution does not automatically imply precision!
• Both of these rasters have similar resolutions, but the right one is clearly more precise.

Raster File Types

Raster File Types

• ArcGIS recognizes lots of raster file types, including several that are recognized by other image analysis software.

Raster File Types

• ArcGIS recognizes lots of raster file types, including several that are recognized by other image analysis software.

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• JPEG, GIF, PNG, TIFF, BMP, Imagine, MrSID, etc.

Raster File Types

• ArcGIS recognizes lots of raster file types, including several that are recognized by other image analysis software.

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• JPEG, GIF, PNG, TIFF, BMP, Imagine, MrSID, etc.

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• Some image files have spatial information embedded inside the actual image file while others have an associated “World” file.

Raster World File

Raster World File

• If your image is named “Flagstaff.tiff”, then the world file (if it has one) will be named “Flagstaff.tfw”.

Raster World File

• If your image is named “Flagstaff.tiff”, then the world file (if it has one) will be named “Flagstaff.tfw”.

• World files are simple ASCII files, which can be opened in WordPad (or any text editor).

Raster World File

• If your image is named “Flagstaff.tiff”, then the world file (if it has one) will be named “Flagstaff.tfw”.

• World files are simple ASCII files, which can be opened in WordPad (or any text editor).

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• The numbers tell ArcGIS how to position the raster in the map.

Raster World File

• If your image is named “Flagstaff.tiff”, then the world file (if it has one) will be named “Flagstaff.tfw”.

• World files are simple ASCII files, which can be opened in WordPad (or any text editor).

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• The numbers tell ArcGIS how to position the raster in the map.

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• Not all rasters use world files.

Raster World File

• In case you’re curious…

• There is no true spatial reference information here...

Raster World File

• If you export your map from Map View, you have the option to also create a world file and thereby georeference your map.

Raster World File

• If you export your map from Map View, you have the option to also create a world file and thereby georeference your map.

• This world file means that your map could be loaded into ArcGIS Pro just like any other raster dataset.

Raster World File

• If you export your map from Map View, you have the option to also create a world file and thereby georeference your map.

• This world file means that your map could be loaded into ArcGIS Pro just like any other raster dataset.

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• The world file option is not always available if you export the map from Layout View.

Raster World File

• If you export your map from Map View, you have the option to also create a world file and thereby georeference your map.

• This world file means that your map could be loaded into ArcGIS Pro just like any other raster dataset.

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• The world file option is not always available if you export the map from Layout View.
• Exceptions are PDF and TIFF formats

More on Rasters

• All rasters are composed of either a single or multiple raster bands. Images commonly have at least three raster bands for different electromagnetic wavelengths.

More on Raster Bands

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.
• One shows reflectance in the Red wavelength

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.
• One shows reflectance in the Red wavelength
• One shows reflectance in the Green wavelength

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.
• One shows reflectance in the Red wavelength
• One shows reflectance in the Green wavelength
• One shows reflectance in the Blue wavelength

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.
• One shows reflectance in the Red wavelength
• One shows reflectance in the Green wavelength
• One shows reflectance in the Blue wavelength
• ArcGIS shows 3 bands at a time, and each band is shown as Red, Green or Blue.

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.
• One shows reflectance in the Red wavelength
• One shows reflectance in the Green wavelength
• One shows reflectance in the Blue wavelength
• ArcGIS shows 3 bands at a time, and each band is shown as Red, Green or Blue.
• These are known as the Red, Green and Blue “Color Guns”. They are also referred to as “Channels”.

More on Raster Bands

• Most “true-color” satellite and aerial images are not really true color images at all, but rather a composite of 3 separate images.
• One shows reflectance in the Red wavelength
• One shows reflectance in the Green wavelength
• One shows reflectance in the Blue wavelength
• ArcGIS shows 3 bands at a time, and each band is shown as Red, Green or Blue.
• These are known as the Red, Green and Blue “Color Guns”. They are also referred to as “Channels”.
• Important Point: Any raster band can be plugged into any color gun/channel!

• Recall that the visible range is a small part of the electromagnetic spectrum.

• Recall that the visible range is a small part of the electromagnetic spectrum.
• Each image shows reflectance at some narrow slice of the electromagnetic spectrum, either inside the visible range our outside of it.

Even More on Raster Bands

Even More on Raster Bands

• Many satellite and aerial images have far more than just 3 bands.

Even More on Raster Bands

• Many satellite and aerial images have far more than just 3 bands.
• Often include bands showing reflectance of wavelengths outside the visible spectrum.

Even More on Raster Bands

• Many satellite and aerial images have far more than just 3 bands.
• Often include bands showing reflectance of wavelengths outside the visible spectrum.
• Landsat Enhanced Thematic Mapper (ETM+) is a good example, with 7 specific bands and 1 “panchromatic” band that sharpens the image.

Even More on Raster Bands

• Many satellite and aerial images have far more than just 3 bands.
• Often include bands showing reflectance of wavelengths outside the visible spectrum.
• Landsat Enhanced Thematic Mapper (ETM+) is a good example, with 7 specific bands and 1 “panchromatic” band that sharpens the image.
• 3 bands correspond with visible red, green and blue wavelengths, and are used to make “Natural Color” image.

Even More on Raster Bands

• Many satellite and aerial images have far more than just 3 bands.
• Often include bands showing reflectance of wavelengths outside the visible spectrum.
• Landsat Enhanced Thematic Mapper (ETM+) is a good example, with 7 specific bands and 1 “panchromatic” band that sharpens the image.
• 3 bands correspond with visible red, green and blue wavelengths, and are used to make “Natural Color” image.
• 4 bands in the Infrared range, used to measure a variety of things such as vegetation health.

Landsat Example: Natural Color

Landsat Example: Some Infrared

Multispectral Imagery

Multispectral Imagery

• Using multiple images taken at different wavelengths

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Multispectral Imagery

• Using multiple images taken at different wavelengths.
• Landsat Enhanced Thematic Mapper (ETM+) is a multispectral system with 7 wavelengths.

Multispectral Imagery

• Using multiple images taken at different wavelengths.
• Landsat Enhanced Thematic Mapper (ETM+) is a multispectral system with 7 wavelengths.
• Some systems take thousands of images, each at different wavelengths. At this point they start calling it Hyperspectral imagery.

Multispectral Imagery

• Using multiple images taken at different wavelengths.
• Landsat Enhanced Thematic Mapper (ETM+) is a multispectral system with 7 wavelengths.
• Some systems take thousands of images, each at different wavelengths. At this point they start calling it Hyperspectral imagery.
• Research into how to identify phenomena (such as vegetation health, or presence of water) from multi- and hyperspectral imagery is a very active field in remote sensing.

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Multispectral Imagery

• Using multiple images taken at different wavelengths.
• Landsat Enhanced Thematic Mapper (ETM+) is a multispectral system with 7 wavelengths.
• Some systems take thousands of images, each at different wavelengths. At this point they start calling it Hyperspectral imagery.
• Research into how to identify phenomena (such as vegetation health, or presence of water) from multi- and hyperspectral imagery is a very active field in remote sensing.
• NDVI: Normalized Difference Vegetation Index

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Multispectral Imagery

• Using multiple images taken at different wavelengths.
• Landsat Enhanced Thematic Mapper (ETM+) is a multispectral system with 7 wavelengths.
• Some systems take thousands of images, each at different wavelengths. At this point they start calling it Hyperspectral imagery.
• Research into how to identify phenomena (such as vegetation health, or presence of water) from multi- and hyperspectral imagery is a very active field in remote sensing.
• NDVI: Normalized Difference Vegetation Index
• NDWI: Normalized Difference Water index

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Imagery Trivia!

Imagery Trivia!

• Satellite and Aerial Photography are not the first systems to use multispectral methods to create color images.

Imagery Trivia!

• Satellite and Aerial Photography are not the first systems to use multispectral methods to create color images.

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• Some of the first color photographs used this method, by taking several images of an object and using colored filters for each image.

France, 1877

Russia, 1910

Numeric Rasters

Numeric Rasters

• Rasters do not have to show imagery.

Numeric Rasters

• Rasters do not have to show imagery.
• Many rasters contain some kind of numeric values that describe or measure the landscape in some way.

Numeric Rasters

• Rasters do not have to show imagery.
• Many rasters contain some kind of numeric values that describe or measure the landscape in some way.
• DEM: Digital Elevation Model, containing elevation or bathymetric values

Numeric Rasters

• Rasters do not have to show imagery.
• Many rasters contain some kind of numeric values that describe or measure the landscape in some way.
• DEM: Digital Elevation Model, containing elevation or bathymetric values
• Slope, Aspect, Curvature, Surface Area rasters

Numeric Rasters

• Rasters do not have to show imagery.
• Many rasters contain some kind of numeric values that describe or measure the landscape in some way.
• DEM: Digital Elevation Model, containing elevation or bathymetric values
• Slope, Aspect, Curvature, Surface Area rasters
• Streamflow, flow direction, contributing area (watershed)

Numeric Rasters

• Rasters do not have to show imagery.
• Many rasters contain some kind of numeric values that describe or measure the landscape in some way.
• DEM: Digital Elevation Model, containing elevation or bathymetric values
• Slope, Aspect, Curvature, Surface Area rasters
• Streamflow, flow direction, contributing area (watershed)
• Habitat quality

DEM: Digital Elevation Model

• Each raster cell contains elevation or bathymetric values.

Any numeric raster can be treated like a DEM.

• A statistical habitat suitability raster. High points in the Habitat raster indicate good habitat

Any numeric raster can be treated like a DEM.

• A statistical habitat suitability raster. High points in the Habitat raster indicate good habitat.
• The blue ridge in the middle is actually a riparian drainage bottom, but it shows up like a ridge because it is high-quality habitat.

Raster Attribute Data

Raster Attribute Data

• Some rasters have attribute tables, some do not.

Raster Attribute Data

• Some rasters have attribute tables, some do not.
• The actual cell values of all rasters are numeric. Attribute tables can add text values (Vegetation Cover Types, for example).

Raster Attribute Data

• Some rasters have attribute tables, some do not.
• The actual cell values of all rasters are numeric. Attribute tables can add text values (Vegetation Cover Types, for example).
• If a raster has an attribute table, then it will have separate attribute tables for each raster band.

Raster Attribute Data

• Some rasters have attribute tables, some do not.
• The actual cell values of all rasters are numeric. Attribute tables can add text values (Vegetation Cover Types, for example).
• If a raster has an attribute table, then it will have separate attribute tables for each raster band.
• If the raster cells have decimal values (1.0001, for example), then the raster probably will not have an attribute table.

Raster Attribute Data

• Some rasters have attribute tables, some do not.
• The actual cell values of all rasters are numeric. Attribute tables can add text values (Vegetation Cover Types, for example).
• If a raster has an attribute table, then it will have separate attribute tables for each raster band.
• If the raster cells have decimal values (1.0001, for example), then the raster probably will not have an attribute table.
• If the raster cells have integer values (1,2,3,4, for example), then the raster probably will have an attribute table.

Raster Attribute Data

• Some rasters have attribute tables, some do not.
• The actual cell values of all rasters are numeric. Attribute tables can add text values (Vegetation Cover Types, for example).
• If a raster has an attribute table, then it will have separate attribute tables for each raster band.
• If the raster cells have decimal values (1.0001, for example), then the raster probably will not have an attribute table.
• If the raster cells have integer values (1,2,3,4, for example), then the raster probably will have an attribute table.
• Raster attributes are associated with specific cell values, not specific cells. This makes them different from feature class attribute tables, which are associated with specific vector objects.

• Landfire Vegetation Type Raster doesn’t tell us much if we just go by the raster cell values.

• Raster attributes are associated with cell values, not specific cells. This makes them different from feature class attribute tables, which are associated with specific vector objects.

• If it has an attribute table, we might be able to symbolize it in a more informative way.

Special Types of Spatial Data

Special Types of Spatial Data

• Layers

Special Types of Spatial Data

• Layers
• Tables

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional

Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids
• Raster Catalogs

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids
• Raster Catalogs
• Relationship Classes

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids
• Raster Catalogs
• Relationship Classes
• Data Models

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids
• Raster Catalogs
• Relationship Classes
• Data Models
• Mosaic Rasters

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids
• Raster Catalogs
• Relationship Classes
• Data Models
• Mosaic Rasters
• Voxels

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Special Types of Spatial Data

• Layers
• Tables
• Web Maps / Apps
• KML/KMZ files
• Annotation
• Geometric Networks
• Network Datasets
• Multi-Dimensional
• LiDAR

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• Terrains
• Topology
• Pyramids
• Raster Catalogs
• Relationship Classes
• Data Models
• Mosaic Rasters
• Voxels
• Digital Twins (3D models)

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Layers

Tables

• Attribute Tables
• Standalone Tables

• Text Files

Viewing Tables in ArcGIS Pro

Viewing Tables in ArcGIS Pro

• Tables that are not associated with a feature class or raster are called Standalone Tables.

Viewing Tables in ArcGIS Pro

• Tables that are not associated with a feature class or raster are called Standalone Tables.
• You can add them to a map, but they are not spatial so they don’t draw.

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Viewing Tables in ArcGIS Pro

• Tables that are not associated with a feature class or raster are called Standalone Tables.
• You can add them to a map, but they are not spatial so they don’t draw.
• If the table has fields containing coordinates, it’s easy to convert to a feature class.

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• Table of Contents sorted by List by Drawing Order:
• Standalone Tables in a separate section

Web Maps and Web Apps

• Museum Fire Example from Mark Manone, Dept. of Geography

Web Maps and Web Apps

• Biota of Grand Canyon Story Map (Museum of Northern Arizona)

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Web Maps and Web Apps

KML/KMZ Files

• Spatial data file invented by Google.

KML/KMZ Files

• Spatial data file invented by Google.
• KML = Keyhole Markup Language

KML/KMZ Files

• Spatial data file invented by Google.
• KML = Keyhole Markup Language
• KMZ is a zipped KML file.

KML/KMZ Files

• Spatial data file invented by Google.
• KML = Keyhole Markup Language
• KMZ is a zipped KML file.
• Can contain both raster and vector data.

Annotation

Annotation

• Text labels that are locked into a spatial location.

Annotation

• Text labels that are locked into a spatial location.
• Zooming into them makes them larger.

Networks

Networks

• Trace and Utility Networks

Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.

Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).

Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.

Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.
• Stored in Feature Datasets.

Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.
• Stored in Feature Datasets.
• Trace Networks are simpler:

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Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.
• Stored in Feature Datasets.
• Trace Networks are simpler:
• Good for finding features that are connected, or finding shortest paths.
• River systems, Roads

Networks

• Trace Network, with derived Shortest Path

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Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.
• Stored in Feature Datasets.
• Utility Networks help you manage active complex networks with sophisticated behaviors.
• Electrical, gas or water networks

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Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.
• Stored in Feature Datasets.
• Utility Networks help you manage active complex networks with sophisticated behaviors.
• Electrical, gas or water networks
• Different components behave differently: High-voltage to Low-Voltage connections

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Networks

• Trace and Utility Networks
• Vector datasets that incorporate connectivity between features.
• Graph Theory, with edges and nodes (aka “Junctions”).
• You can model stream connectivity, for example.
• Stored in Feature Datasets.
• Utility Networks help you manage active complex networks with sophisticated behaviors.
• Electrical, gas or water networks
• Different components behave differently: High-voltage to Low-Voltage connections
• Model disruptions in the network: Blown transformers, for example

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Networks

• Network Datasets

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Networks

• Network Datasets
• Similar to Trace Networks, but specifically designed to work with Esri’s Network Analyst extension.

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Networks

• Network Datasets
• Similar to Geometric Networks, but specifically designed to work with Esri’s Network Analyst extension.
• Also used for finding shortest path between points on a road network.

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Networks

• Network Datasets
• Similar to Geometric Networks, but specifically designed to work with Esri’s Network Analyst extension.
• Also used for finding shortest path between points on a road network.
• Incorporates local street issues

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Networks

• Network Datasets
• Similar to Geometric Networks, but specifically designed to work with Esri’s Network Analyst extension.
• Also used for finding shortest path between points on a road network.
• Incorporates local street issues
• One-way streets

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Networks

• Network Datasets
• Similar to Geometric Networks, but specifically designed to work with Esri’s Network Analyst extension.
• Also used for finding shortest path between points on a road network.
• Incorporates local street issues
• One-way streets
• Street on overpass might not intersect lower street

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Networks

• Network Datasets
• Similar to Geometric Networks, but specifically designed to work with Esri’s Network Analyst extension.
• Also used for finding shortest path between points on a road network.
• Incorporates local street issues
• One-way streets
• Street on overpass might not intersect lower street
• Local “No Turn” rules

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Networks

• Network Datasets
• Similar to Geometric Networks, but specifically designed to work with Esri’s Network Analyst extension.
• Also used for finding shortest path between points on a road network.
• Incorporates local street issues
• One-way streets
• Street on overpass might not intersect lower street
• Local “No Turn” rules
• Stored in Feature Datasets

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Network Datasets

Resource Allocation

Best Routes

Multi-dimensional Datasets

• NetCDF (Network Common Data Form), HDF, Cloud Raster Format, “Image Cubes”

Two-dimensions

(Longitude and Latitude)

Add 3^rd Dimension

(Altitude)

Multi-dimensional Datasets

• NetCDF (Network Common Data Form), HDF, Cloud Raster Format, “Image Cubes”
• Often used for data-intensive scientific applications, like 3D climate or ocean current modeling.

Two-dimensions

(Longitude and Latitude)

Add 3^rd Dimension

(Altitude)

Multi-dimensional Datasets

• NetCDF (Network Common Data Form), HDF, Cloud Raster Format, “Image Cubes”
• Often used for data-intensive scientific applications, like 3D climate or ocean current modeling.
• Can be a real pain to work with.

Two-dimensions

(Longitude and Latitude)

Add 3^rd Dimension

(Altitude)

Multi-dimensional Datasets

Add 4^th Dimension

(Time)

Multi-dimensional Datasets

Add 5^th Dimension

(Who knows?)

NetCDF (Network Common Data Form)

• Very common Multidimensional Format. A lot of global climate and ocean model data come in NetCDF.

NetCDF (Network Common Data Form)

• Very common Multidimensional Format. A lot of global climate and ocean model data come in NetCDF.

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• Format includes internal query functions, to extract subsets of the full dataset.

NetCDF (Network Common Data Form)

• Very common Multidimensional Format. A lot of global climate and ocean model data come in NetCDF.

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• Format includes internal query functions, to extract subsets of the full dataset.
• Best tools I’ve found to work with it are in Linux, and intended more for supercomputer or high-end computer cluster analysis.

Terrains

Terrains

• TIN-based vector datasets optimized for quick analysis, editing and viewing.

Terrains

• TIN-based vector datasets optimized for quick analysis, editing and viewing.
• They look like TINs but are not stored the same way. The triangles are created on-the-fly as you view the dataset.

Terrains

• TIN-based vector datasets optimized for quick analysis, editing and viewing.
• They look like TINs but are not stored the same way. The triangles are created on-the-fly as you view the dataset.
• They use Pyramids, which allows them to be drawn at different resolutions when viewed at different scales.

Terrains

• TIN-based vector datasets optimized for quick analysis, editing and viewing.
• They look like TINs but are not stored the same way. The triangles are created on-the-fly as you view the dataset.
• They use Pyramids, which allows them to be drawn at different resolutions when viewed at different scales.
• Stored in Feature Datasets

LiDAR

LiDAR

• Like Radar, but with lasers.

LiDAR

• Like Radar, but with lasers.
• Produces a point cloud of 3D points.

LiDAR

• Like Radar, but with lasers.
• Produces a point cloud of 3D points.
• Based on the speed of light, and the time it takes to get a return signal from a laser.

LiDAR

• Like Radar, but with lasers.
• Produces a point cloud of 3D points.
• Based on the speed of light, and the time it takes to get a return signal from a laser.
• Different wavelengths can see different things, like multispectral imagery.

Parcel Fabrics

• Specifically designed to map Property Boundaries.
• Stored in a Feature Dataset.

Topology

• A special type of data structure that tells related vector objects how they are spatially related to each other.

Topology

• A special type of data structure that tells related vector objects how they are spatially related to each other.
• For example, suppose you have two adjacent polygons representing timber stands. If you want to move the boundary that separates them, you would like both polygons to incorporate the new boundary.

More on Topology

More on Topology

• ArcGIS implements topology in many ways.

More on Topology

• ArcGIS implements topology in many ways.
• Some datasets have a basic topology built into them (Coverages and TINs, for example).

More on Topology

• ArcGIS implements topology in many ways.
• Some datasets have a basic topology built into them (Coverages and TINs, for example).
• Most datasets have no internal topology defined, but ArcGIS can create a temporary topology when it needs to (as in editing).

More on Topology

• ArcGIS implements topology in many ways.
• Some datasets have a basic topology built into them (Coverages and TINs, for example).
• Most datasets have no internal topology defined, but ArcGIS can create a temporary topology when it needs to (as in editing).
• ArcGIS also allows you to define complex topological relationships between datasets in geodatabases.

More on Topology

• ArcGIS implements topology in many ways.
• Some datasets have a basic topology built into them (Coverages and TINs, for example).
• Most datasets have no internal topology defined, but ArcGIS can create a temporary topology when it needs to (as in editing).
• ArcGIS also allows you to define complex topological relationships between datasets in geodatabases.
• You can add them to feature datasets to help find problems in the data.

More on Topology

• ArcGIS implements topology in many ways.
• Some datasets have a basic topology built into them (Coverages and TINs, for example).
• Most datasets have no internal topology defined, but ArcGIS can create a temporary topology when it needs to (as in editing).
• ArcGIS also allows you to define complex topological relationships between datasets in geodatabases.
• You can add them to feature datasets to help find problems in the data.
• Polylines that almost, but don’t quite, connect.

More on Topology

• ArcGIS implements topology in many ways.
• Some datasets have a basic topology built into them (Coverages and TINs, for example).
• Most datasets have no internal topology defined, but ArcGIS can create a temporary topology when it needs to (as in editing).
• ArcGIS also allows you to define complex topological relationships between datasets in geodatabases.
• You can add them to feature datasets to help find problems in the data.
• Polylines that almost, but don’t quite, connect.
• Polyline or polygon segments that overshoot.

More on Topology

Pyramids

• Interesting innovation to speed up drawing time for large rasters and terrains.

Pyramids

• Interesting innovation to speed up drawing time for large rasters and terrains.
• Several versions of the data are created at different scales. Depending on the scale of the map, ArcGIS may show a coarser resolution version rather than the actual dataset.

Pyramids

• Interesting innovation to speed up drawing time for large rasters and terrains.
• Several versions of the data are created at different scales. Depending on the scale of the map, ArcGIS may show a coarser resolution version rather than the actual dataset.
• The coarser versions draw a lot faster than the fine versions, and you can’t tell the difference on the screen.

Raster Catalogs

• Another interesting innovation.
• A collection of rasters that are treated as a single object, and portrayed as either a vector or raster object depending on your view extent.

Relationship Classes

• Not actually spatial data in themselves, but sometimes used to define relationships between datasets.

Relationship Classes

• Not actually spatial data in themselves, but sometimes used to define relationships between datasets.
• For example, a utility company may have:

Relationship Classes

• Not actually spatial data in themselves, but sometimes used to define relationships between datasets.
• For example, a utility company may have:
• A table of property owners, with names, addresses, etc.

Relationship Classes

• Not actually spatial data in themselves, but sometimes used to define relationships between datasets.
• For example, a utility company may have:
• A table of property owners, with names, addresses, etc.
• A feature class of electrical distribution lines, with property owner IDs.

Relationship Classes

• Not actually spatial data in themselves, but sometimes used to define relationships between datasets.
• For example, a utility company may have:
• A table of property owners, with names, addresses, etc.
• A feature class of electrical distribution lines, with property owner IDs.
• The utility company could create a relationship class that connects the distribution line feature class with the property owner attribute table. Any analysis done on the property lines could then automatically extract property owner information.

Relationship Classes

• Not actually spatial data in themselves, but sometimes used to define relationships between datasets.
• For example, a utility company may have:
• A table of property owners, with names, addresses, etc.
• A feature class of electrical distribution lines, with property owner IDs.
• The utility company could create a relationship class that connects the distribution line feature class with the property owner attribute table. Any analysis done on the property lines could then automatically extract property owner information.
• Creating relationship classes in a Geodatabase requires an ArcGIS Standard or Advanced license.

Data Models

• For cases where a simple data type is insufficient to describe the complexity.

Data Models

• For cases where a simple data type is insufficient to describe the complexity.
• For example, hydrologists study river systems, but a simple polyline feature class is not complex enough.

Data Models

• For cases where a simple data type is insufficient to describe the complexity.
• For example, hydrologists study river systems, but a simple polyline feature class is not complex enough.
• They need to incorporate flow direction, catchment area, surface and subsurface flow rates, etc.

Data Models

• For cases where a simple data type is insufficient to describe the complexity.
• For example, hydrologists study river systems, but a simple polyline feature class is not complex enough.
• They need to incorporate flow direction, catchment area, surface and subsurface flow rates, etc.
• There is an example of a popular data model (ArcMarine, for marine analysis and management) in your class outline.

ArcMarine Data Model

Mosaic Dataset

• An example of a Data Model designed to deliver multiple raster datasets as a single unit. It can be analyzed like any other raster dataset.

Voxels (Like Pixels, but with Volume)

3D Models (aka Digital Twins)

(see https://tinyurl.com/yh9papmu)

Done with Data Types

Done with Data Types

• A few last words on Catalog:

Done with Data Types

• A few last words on Catalog:
• As mentioned before, Catalog is the recommended way to copy, move or delete spatial datasets.

Done with Data Types

• A few last words on Catalog:
• As mentioned before, Catalog is the recommended way to copy, move or delete spatial datasets.
• If you create a new dataset in a workspace (perhaps using one of the geoprocessing tools), you will usually need to refresh Catalog before you will see it (use the F5 key).

Done with Data Types

• A few last words on Catalog:
• As mentioned before, Catalog is the recommended way to copy, move or delete spatial datasets.
• If you create a new dataset in a workspace (perhaps using one of the geoprocessing tools), you will usually need to refresh Catalog before you will see it (use the F5 key).
• Your class handout has an appendix showing the symbols Catalog uses for different types of datasets.

ArcCatalog Symbols (ArcGIS Pro Catalog symbols are similar)

Metadata

• Data that describes your data

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it
• Purpose and Description

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it
• Purpose and Description
• Dates it might be valid for

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it
• Purpose and Description
• Dates it might be valid for
• Spatial Reference and Extent

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it
• Purpose and Description
• Dates it might be valid for
• Spatial Reference and Extent
• Describe what Attribute Fields mean

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it
• Purpose and Description
• Dates it might be valid for
• Spatial Reference and Extent
• Describe what Attribute Fields mean
• Processing History (a Slope dataset might list the source elevation dataset, and the parameters used to calculate slope).

“I never metadata I didn’t like!

Unknown

Metadata

• Data that describes your data
• Who created it
• Purpose and Description
• Dates it might be valid for
• Spatial Reference and Extent
• Describe what Attribute Fields mean
• Processing History (a Slope dataset might list the source elevation dataset, and the parameters used to calculate slope).
• Lots more! Hundreds of potential attributes

“I never metadata I didn’t like!

Unknown

Metadata

Metadata

• Metadata can be very important!

Metadata

• Metadata can be very important!
• Can tell you whether the data is really valid for your project.

Metadata

• Metadata can be very important!
• Can tell you whether the data is really valid for your project.
• Gives you documentation to back up any conclusions you make from the data.

Metadata

• Metadata can be very important!
• Can tell you whether the data is really valid for your project.
• Gives you documentation to back up any conclusions you make from the data.
• Not all datasets have metadata!

Metadata

• Metadata can be very important!
• Can tell you whether the data is really valid for your project.
• Gives you documentation to back up any conclusions you make from the data.
• Not all datasets have metadata!
• If you make a dataset available to the public, you should add metadata.

Metadata

• Metadata can be very important!
• Can tell you whether the data is really valid for your project.
• Gives you documentation to back up any conclusions you make from the data.
• Not all datasets have metadata!
• If you make a dataset available to the public, you should add metadata.
• If you are just making intermediate datasets in a larger analysis, you probably won’t make metadata.

​

Metadata in ArcGIS Pro View

“Maps are like milk: their information is perishable, and it is wise to check the date.”

Mark Monmonier, How to Lie with Maps

Metadata in Catalog View

Metadata Styles

• These correspond with various Metadata Standards

Creating and Editing Metadata

You will do this in a lab exercise…

Be warned…

Be warned…

• ArcGIS tools typically record what you have done into your metadata.

Be warned…

• ArcGIS tools typically record what you have done into your metadata.
• This means that often you can see a record of everything that has been done to a particular dataset.

Be warned…

• ArcGIS tools typically record what you have done into your metadata.
• This means that often you can see a record of everything that has been done to a particular dataset.
• This is often a very useful feature, but it might reveal things you didn’t intend to make public.

Be warned…

• ArcGIS tools typically record what you have done into your metadata.
• This means that often you can see a record of everything that has been done to a particular dataset.
• This is often a very useful feature, but it might reveal things you didn’t intend to make public.
• Your “LeBarronStands_v1_NAD83” feature class has some interesting things in the processing history…

​

Be warned…

• When you send data to a colleague (or the general public), this kind of information can travel with it:

​

Data Management Strategies

Data Management Strategies

• It is quite common to use multiple workspaces in a single ArcGIS session.

Data Management Strategies

• It is quite common to use multiple workspaces in a single ArcGIS session.
• If you do a lot of analysis, you will probably create a lot of intermediate datasets as you work.

Data Management Strategies

• It is quite common to use multiple workspaces in a single ArcGIS session.
• If you do a lot of analysis, you will probably create a lot of intermediate datasets as you work.
• This quickly leads to cluttered workspaces where you can’t find the data you want.

Data Management Strategies

• It is quite common to use multiple workspaces in a single ArcGIS session.
• If you do a lot of analysis, you will probably create a lot of intermediate datasets as you work.
• This quickly leads to cluttered workspaces where you can’t find the data you want.
• It is a very good idea to plan where you are going to store your data before you start.

Data Management Strategies

• It is quite common to use multiple workspaces in a single ArcGIS session.
• If you do a lot of analysis, you will probably create a lot of intermediate datasets as you work.
• This quickly leads to cluttered workspaces where you can’t find the data you want.
• It is a very good idea to plan where you are going to store your data before you start.
• For example, make several folders or geodatabases where you will store particular datasets. Then be disciplined about storing them in the correct place as you work.

Lab Exercise 1

• Only if you have not done it already!
• Connect to your data folder.

Lab Exercise 2

• Create a new File Geodatabase using Catalog.

Lab Exercise 3

• Import a feature class into your new file geodatabase.

Lab Exercise 4

• Import a raster into your new file geodatabase.

Lab Exercise 5

• Investigate a multiband raster
• Analyze a typical multiband raster containing images from multiple electromagnetic wavelengths.

Lab Exercise 6

• Investigate a Layer File
• Observe the difference between loading a basic feature class into ArcGIS Pro vs. loading a layer file.

Lab Exercise 7

• Investigate a TIN dataset

Lab Exercise 8

• Investigate an Attribute Table

Lab Exercise 9

• Zoom to feature using an attribute table.

Lab Exercise 10

• Connect to an Online Web service

Lab Exercise 11

• View and Edit Metadata

Lab Exercise 12

• Learn how to share spatial data with colleagues.
• Learn what files make up a shapefile, and how to compress those files into a single file suitable for emailing or posting online.
• Learn what files make up a file geodatabase workspace, and how to share that workspace with others.