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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. For 'Number of Distance Bands', type 25.
4. For 'Output Report File (optional)', click the Browse folder icon. type "ISA_DroveAlone_25.pdf". If you click elsewhere, you will notice that it will automatically locate your PDF file within your project folder.
5. Click Run.

Percent Commuting By Carpool

1. For 'Input Field', use the drop-down menu to select the Percent_CarTruckOrVan_carpooled field.
2. For 'Number of Distance Bands', type 15.
3. For 'Output Report File (optional)', click the Browse folder icon. type  type "ISA_Carpooled_15.pdf".
4. Click Run.

Percent Commuting By Public Transportation

1. For 'Input Field', use the drop-down menu to select the Percent_PublicTransportation field.
2. For 'Number of Distance Bands', type 15.
3. For 'Output Report File (optional)', click the Browse folder icon. type  type "ISA_PublicTransitTransit_15.pdf".
4. Click Run.

High/Low Clustering (Getis_Ord General G)

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

Percent White

Optimized Hot Spot Analysis

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1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Mapping Clusters 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.

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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 Spatial Statistics toolbox, click the Mapping Clusters toolset > Optimized Hot Spot Analysis tool.
3. In the upper right corner of the ‘Hot Spot Analysis’ tool, hover over the Help ? button.
4. (Help)
5. For 'Input Features', use the drop-down menu to select the CensusTracts_Race_2014  layer.
6. For 'Output Features', type "Race_OHSA".
7. For 'Analysis Field', use the drop-down menu to select the Percent_White field.
8. Check Generate Report.
9. Click Run.

Cluster and Outlier Analysis (Anselin Local Morans I)

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Analyzing Patterns toolsetthe Mapping Clusters toolset, click the Incremental Spatial AutocorrelationCluster and Outlier Analysis (Anselin Local Morans I) tool.
3. For 'Input Feature Class', use the drop-down menu to select the  the CensusTracts_Race_CensusTracts2014 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. Percent_White field.
2. For 'Output Features', rename the feature class from asdf to "Race_COA".

Crime Points

Optimized Hot Spot Analysis (Getis-Ord Gi*)

1. In the Catalog pane on the right, within the Patterns.gdb geodatabase, right-click the TotalCrimes_Crimes2014 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.

Copy Features

The first step is to use the Collect Events tool to aggregate the data, but it is important to make a copy of the original input data before proceeding, because the Integrate tool modifies the input dataset by shifting, or standardizing, the locations of the input features.

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Click the Data Management Tools toolbox > Features toolset > Copy Features tool
3. For 'Input Features', use the drop-down menu to select the total_Crimes layer.
4. For 'Output Feature Class', rename the output feature class from to total_crimes_original.Ensure and click OK.

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

Integrate

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

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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, so we could techically will set XY tolerance to 0 for the aggregation.Step 3 Run

Collect Events

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1. In the Geoprocessing pane, within the Spatial Statistics Tools

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1. toolbox,click the Utilities toolset > Collect Events

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1. tool.
2. Click OK

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1. .

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*)

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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.

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Optimized Hot Spot Analysis (Getis-Ord Gi*)

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

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• At the top of the Geoprocessing pane, click the Back arrow button.
• Within the Spatial Statistics toolbox, click the Mapping Clusters toolset > Optimized Hot Spot Analysis tool.
• In the upper right corner of the ‘Hot Spot Analysis’ tool, hover over the Help ? button.
• (Help)
• For 'Input Features', use the drop-down menu to select the total layer.
• For 'Output Features', type "Race_OHSA".
• For 'Analysis Field', use the drop-down menu to select the Percent_White field.
• Check Generate Report.
• Click Run.

Cluster and Outlier Analysis (Anselin Local Morans I)

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Mapping Clusters toolset, click the Cluster and Outlier Analysis (Anselin Local Morans I)

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1.  tool.

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

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