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1. At the top of the Geoproccessing pane, click the Toolboxes tab.
2. Click the Spatial Statistics Tools toolbox > Analyzing Patterns toolset > Spatial Autocorrelation (Global Moran's I) tool.
3. In the upper right corner of the ‘Merge’ ‘Spatial Autocorrelation’ tool, hover over the Help ? button.
4. (Help)

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As the Geoprocessing pane doesn’t reset after a tool has finished running, it is easy to rerun tools with slightly modified settings. In future versions of ArcGIS Pro, batch processing is also supported, which facilitates multiple runs of the same tool within a single interface. 

1. For 'Input Field', use the drop-down menu to select the  Percent_CarTruckOrVan_carpooled  field.
2. Check Generate Report.
3. Click Run.

Percent Commuting By Public Transportation

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1. For 'Input Field', use the drop-down menu to select the  Percent_PublicTransportation  field.
2. Check Generate Report.
3. Click Run.

Incremental Spatial Autocorrelation

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolset, click the Incremental Spatial Autocorrelation tool.
3. In the upper right corner of the ‘Incremental Spatial Autocorrelation’ tool, hover over the Help ? button.
4. (Help)

Percent Commuting Alone By Car

1. For 'Input Feature Class', use the drop-down menu to select the  CensusTracts_MTTW_2014  layer.
2. For 'Input Field', use the drop-down menu to select the  Percent_CarTruckOrVan_droveAlone  field.
3. Check Generate Report.
4. Click Run.

Percent Commuting By Carpool

1. For 'Input Field', use the drop-down menu to select the  Percent_CarTruckOrVan_carpooled  field.
2. Check

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1.  Generate Report.
2. Click Run.

Percent Commuting By Public Transportation

1. For 'Input Field', use the drop-down menu to select the  Percent_PublicTransportation  field.
2. Check

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1.  Generate Report.
2. Click Run.

High/Low Clustering (Getis_Ord General G)

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1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolset, click the High/Low Clustering (Getis_Ord General G) tool.
3. In the upper right corner of the ‘High/Low Clustering’ tool, hover over the Help ? button.
4. (Help)

Percent Commuting Alone By Car

1. For 'Input Feature Class', use the drop-down menu to select the  CensusTracts_MTTW_2014  layer.
2. For 'Input Field', use the drop-down menu to select the Percent_CarTruckOrVan_droveAlone

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1.  field.
2. Check Generate Report.
3. Click Run.

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

Percent Commuting By Carpool

1. For 'Input Field', use the drop-down menu to select the  Percent_CarTruckOrVan_carpooled  field.
2. Check

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1.  Generate Report.
2. Click Run.

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

Percent Commuting By Public Transportation

1. For 'Input Field', use the drop-down menu to select the  Percent_PublicTransportation  field.
2. Check

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1.  Generate Report.
2. Click Run.

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

For the next set of tools, you will run them each twice: once with a polygon dataset and once with a point dataset.

Optimized Hot Spot Analysis (Getis-Ord Gi*)

P

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

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolset,
3. click the Hot Spot Analysis (Getis-Ord Gi*) tool.
4. In the upper right corner of the ‘Hot Spot Analysis’ tool, hover over the Help ? button.
5. (Help)

Percent White

1. In the Catalog pane on the right, within the Patterns.gdb geodatabase, right-click the Race_CensusTracts feature class and select Add To New Map.

Since hot spot analysis is based on a distance threshold, it is important to get a good understanding of the spatial autocorrelation of your data as it related to distance threshold. One option is to run the incremental spatial autocorrelation on the same variable first, to help determine an appropriate distance threshold, which can then be entered in the Hot Spot Analysis tool. Another option is to use the Optimized Hot Spot Analysis tool, which will automatically run the Incremental Spatial Autocorrelation tool first in the background to select the distance threshold with the most significant?

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolset, click the Incremental Spatial Autocorrelation tool.
3. For 'Input Feature Class', use the drop-down menu to select the Race_CensusTracts  layer.
4. For 'Input Field', use the drop-down menu to select the  Polygon_Race_Percent_of_White  field.
5. Check Generate Report.
6. Click Run.

Crime Points

1. In the Catalog pane on the right, within the Patterns.gdb geodatabase, right-click the Total_Crimes feature class and select Add To New Map.
2. Right-click the Freeways feature class and click Add To Current Map.

Houston crime data is reported at the block level, not the individual address level to protect confidentiality. Therefore, when the addresses are geocoded, all crimes that happened within a single block are all geocoded to the separately to the same point location in the center of the block, resulting in coincident, or overlapping points for all crimes. In order to run a hot spot analysis on this point data, we, instead, need to have a single point at each location and a value field indicating the number of crimes that occurred in that location. In our case, the address data was all run using a consistant geocoder, so we can be sure that crimes on the same block were located at exactly the same point. If, however, you were aggregating crime data points from a variety of data providers who may have used a variety of geocoding methods or if you created a community website, where residents could manually click on a location to report a crime, you would need a way of standardizing the crime locations, otherwise the number of crimes at each location might simply be 1, which would not provide for as interesting of results. Though not necessary for our dataset, you will test out the integrate tool for messier datasets.

The first step is to use Since the crimes are coincident points, we use Integrate with the Collect Events tool to aggregate the data.But before aggregating, but 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 shifting, or standardizing, the locations of the input features.

Step 1 Copy Features

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1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Click the Data Management Tools

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1. toolbox > Features

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1. toolset > Copy Features tool
2. For 'Input Features

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1. ', use the drop-down menu to select the total_Crimes layer.
2. For 'Output Feature Class', rename the output feature class from to total_crimes_original.
3. Ensure and click OK.

Show how to see multiple points at same location and attribute table.

Now you are ready to use the integrate tool to standardize the data

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Data Management Tool toolbox, click the Feature Class toolset > Integrate tool.
3. For 'Input Features', use the drop-down menu to select the total crimes layer.

Step 2 Integrate

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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 , so we could techically will set XY tolerance to 0 for the aggregation. 

Step 3 Run Collect Events

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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

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

• Result

2)    Point_CrimeRate

Using Census Block Groups and total_crimes Datasets

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• 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: