Obtaining the Tutorial Data

Before beginning the tutorial, you will copy all of the required tutorial data onto your Desktop. Option 1 is best if you are completing this tutorial in one of our short courses or from the GIS/Data Center and Option 2 is best if you are completing the tutorial from your own computer.

OPTION 1: Accessing tutorial data from Fondren Library using the gistrain profile

If you are completing this tutorial from a public computer in Fondren Library and are logged on using the gistrain profile, follow the instructions below:

1. On the Desktop, double-click the Computer icon > gisdata (\\file-rnas.rice.edu) (R:) > Short_Courses > Analyzing_Spatial_Patterns.
2. To create a personal copy of the tutorial data, drag the Patterns folder onto the Desktop.
3. Close all windows.

OPTION 2: Accessing tutorial data online using a personal computer

If you are completing this tutorial from a personal computer, you will need to download the tutorial data online by following the instructions below:

1. Click Patterns.zip above to download the tutorial data.
2. Open the Downloads folder.
3. Right-click Patterns.zip and select Extract All....
4. In the 'Extract Compressed (Zipped) Folders' window, accept the default location into the Downloads folder and click Extract.
5. Drag the unzipped Patterns folder onto your Desktop.
6. Close all windows.

Spatial Statistics in ArcGIS Pro

Opening an Existing Project

1. On the Desktop, double-click the Patterns folder.

2. Double-click the Patterns.aprx project file to open the project in ArcGIS Pro.

Spatial Autocorrelation (Morans I)

1. In the Catalog pane on the right, expand the Databases folder.
2. Expand the Patterns.gdb geodatabase.
3. Right-click the CensusTracts_MTTW_2014 feature class and select Add To New Map.

This layer provides boundaries for all of the census tract boundaries in Harris County. 

3)    Percent_PublicTransportation

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Analyzing Patterns -> Incremental Spatial Autocorrelation

• Check report:

3.     High/Low Clustering (Getis-Ord General G) (Analyzing Patterns)

Using MTTW_CensusTracts dataset.

Check all “Generate Report” options for all following features:

1)    Percent_CarTruckOrVan_droveAlone

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Analyzing Patterns -> High/Low Clustering (Getis-Ord General G)

• Geoprocessing -> Results -> Current Session -> open “Report File”

Pay attention to the “General G Summary” table, which shows this is a “low-clusters” scenario.

2)    Percent_CarTruckOrVan_carpooled

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Analyzing Patterns -> High/Low Clustering (Getis-Ord General G)

• Geoprocessing -> Results -> Current Session -> open “Report File”

Pay attention to the “General G Summary” table, which shows this is a “high-clusters” scenario.

3)    Percent_PublicTransportation

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Analyzing Patterns -> High/Low Clustering (Getis-Ord General G)

• Geoprocessing -> Results -> Current Session -> open “Report File”

Pay attention to the “General G Summary” table, which shows this is a “high-clusters” scenario.

Hot Spot Analysis (Mapping Clusters)

Polygon_Race_Percent_of_White

Using Race_CensusTracts Dataset

• First is to find the Distance Band or Threshold Distance by using “Incremental Spatial Autocorrelation”.

Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Analyzing Patterns -> Incremental Spatial Autocorrelation.

Check the  report and threshold distance is 24962.

• Hot Spot Analysis

Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Mapping Clusters -> Hot Spot Analysis (Getis-Ord Gi*)

• Result:

2)    Point_CrimeRate

Using Freeways and total_crimes Datasets

• Aggregate crime data prior to analysis

Since the crimes are coincident points, we use Integrate with the Collect Events tool to aggregate the data.

But before aggregating, it is very important to make a copy of the original input data before proceeding, because the Integrate tool modifies the input dataset by changing the locations of the input features.

Step 1 Copy Features

• Geoprocessing -> ArcToolbox -> Data Management Tools -> Features -> Copy Features
• Fill the dialog in as follows:
• Click OK
• Data symbology for the copied features:

Step 2 Integrate

• Geoprocessing -> ArcToolbox -> Data Management Tools -> Feature Class -> Integrate
• Fill the dialog in as follows:

A good rule of thumb for the XY tolerance is to consider the accuracy of your data. In this case, all crimes were reported by blocks and were geocoded at centers of blocks. Multiple points can be stacked in the same location. Therefore, we will set XY tolerance to 0 for the aggregation.

Step 3 Run Collect Events

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Utilities -> Collect Events
• Fill the dialog in as follows:

• Click OK (Notice that the output from Collect Events is rendered with graduated circles reflecting the number of incidents at each point)

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Mapping Clusters -> Hot Spot Analysis (Getis-Ord Gi*)

• Result:

Note that hot spot doesn’t mean high value. A has a feature with low value, but surrounding neighbors all have high value, which will cause a significantly high mean value than the global mean for this spot. It depends on the scale of the analysis.

Optimized Hot Spot Analysis

1)    Polygon_Race_Percent_of_White

Using Race_CensusTracts Dataset

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Mapping Clusters -> Optimized Hot Spot Analysis

• Result

2)    Point_CrimeRate

Using Census Block Groups and total_crimes Datasets

Version 10.3 and 10.4 have “optimized hot spot analysis tool”, which automatically aggregates data first and does hot spot analysis for points data. For this tool, we will aggregate data by census block groups.

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Mapping Clusters -> Optimized Hot Spot Analysis

• Result:

Cluster and Outlier Analysis (Anselin Local Morans I)

1)    Polygon_Race_Percent_of_White

             Using Race_CensusTracts Dataset

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Mapping Clusters -> Cluster and Outlier Analysis (Anselin Local Morans I)

• Result:

2)    Point_CrimeRate

Using “AggregatedTotalCrimes” dataset, which was generated during the Hot Spot Analysis.

• Geoprocessing -> ArcToolbox -> Spatial Statistics Tools -> Mapping Clusters -> Cluster and Outlier Analysis (Anselin Local Morans I)

• Result: