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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)

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

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

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

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Hot Spot Analysis (Mapping Clusters)

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Polygon_Race_Percent_of_White

Using Race_CensusTracts Dataset

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

Check the  report and threshold distance is 24962.

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

• Result:

2)    Point_CrimeRate

Using Freeways and total_crimes Datasets

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• Aggregate crime data prior to analysis

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• 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:

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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.

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

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

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

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• 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. 

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Optimized Hot Spot Analysis

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1)    Polygon_Race_Percent_of_White

Using Race_CensusTracts Dataset

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

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• Result

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2)    Point_CrimeRate

Using Census Block Groups and total_crimes Datasets

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

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• Result:

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Cluster and Outlier Analysis (Anselin Local Morans I)

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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)

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• Result:

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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)

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• Result:

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