Page History

...

1. For 'Input Features', 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', type "ISA_DroveAlone_25.pdf". If you click elsewhere, you will be able to see the full file path and notice that the tool will automatically locate your PDF file within your project folder.
5. Click Run.
6. Hover over the Completed with warnings message and click the Output Report File hyperlink.

...

1. For 'Input Field', use the drop-down menu to select the Percent_CarTruckOrVan_carpooled field.
2. For 'Number of Distance Bands', type 10.
3. For 'Output Report File', type "ISA_Carpooled_10.pdf".
4. Click Run.
5. Hover over the Completed with warnings message and click the Output Report File hyperlink.

...

1. For 'Input Field', use the drop-down menu to select the Percent_PublicTransportation field.
2. For 'Number of Distance Bands', type 10.
3. For 'Output Report File', type "ISA_Transit_10.pdf".
4. Click Run.
5. Hover over the Completed with warnings message and click the Output Report File hyperlink.

...

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.
   asdf
   Image Added

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.
5. Hover over the Completed with warnings message and click the Report File hyperlink.

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

Percent Commuting By Carpool

Notice that the z-score is negative, which indicates that areas in which a low percentage of commuters drive alone are clustered. This probably represents an inverse relationship with commuters who are carpooling or using public transit, in which we expect high percentages to be clustered.

Percent Commuting By Carpool

1. For 'Input Field', For 'Input Field', use the drop-down menu to select the Percent_CarTruckOrVan_carpooled field.Check Generate Report.
2. Click Run.
3. Hover over the Completed with warnings message and click the Report File hyperlink.

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

Percent Commuting By Public Transportation

1. For 'Input Field', use the drop-down menu to select the Percent_PublicTransportation field.Check Generate Report.
2. Click Run.
3. Hover over the Completed with warnings message and click the Report File hyperlink.

Pay attention to the “General G Summary” table, which shows this is This variable also displays a “high-clusters” scenariopattern.

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

Percent White

Optimized Hot Spot Analysis

1. At the bottom of the Geoprocessing pane, click the Catalog tab.
2. Within In the Catalog pane on the right, within the Patterns.gdb geodatabase, right-click the CensusTracts_Race_CensusTracts2014 feature class and select Add To Current New Map.

This layer also provides boundaries for all of the census tracts in Harris County, but, this time, with race data.

1. In the Contents pane, right-click the CensusTracts_Race_2014 layer name and select Attribute Table.

The attribute table includes data on the percent of the population within each census tract of each race. For this analysis you will study the distribution of the white population.

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 relates to distance threshold. One option, which you will pursue, is to 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?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 select an optimal distance threshold for you.

Incremental Spatial Autocorrelation

1. At the bottom of the Catalog pane, click the Geoprocessing tab.
2. At the top of the Geoprocessing pane, click the Back arrow button.
3. Within the Spatial Statistics toolbox, click the Mapping Clusters toolset > Optimized Hot Spot Analysis tool.
4. In the upper right corner of the ‘Hot Spot Analysis’ tool, hover over the Help ? button.
5. (Help)

...

1. the Analyzing Patterns toolset, click the Incremental Spatial Autocorrelation tool.
2. For 'Input Features', use the drop-down menu to select theCensusTracts CensuTracts_Race_2014 layer.
3. For 'Output Features', type "Race_OHSA".For 'Analysis Field', use the drop-down menu Input Field', use the drop-down menu to select the Percent_White field.
4. Check Generate Report.
5. ClickRun.

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) tool.
3. For 'Input Feature Class', use the drop-down menu to select the CensusTracts_Race_2014 layer.
4. For 'Input Field', use the drop-down menu to select the Percent_White field.
5. 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 Crimes_2014 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.

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

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.

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.

Collect Events

1. In the Geoprocessing pane, within the Spatial Statistics Tools toolbox,click the Utilities toolset > Collect Events tool.
2. 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 (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.

...

1.  field.
2. For 'Number of Distance Bands', type 15.
3. For 'Beginning Distance', type 10000.
4. For 'Output Report File', type "ISA_White_15.pdf".
5. Click Run.
6. Hover over the Completed with warnings message and click the Output Report File hyperlink.

Notice that the the peak threshold distance is 24,962, which you will use as the distance for your hot spot analysis.

Hot Spot Analysis

1. At the top of the Geoprocessing pane, click the Back arrow button.
2. Within the Spatial Statistics toolbox, click the Mapping Clusters toolset > Hot Spot Analysis (Getis-Ord GI*) tool.
3. In the upper right corner of the ‘Hot Spot Analysis’ tool, hover over the Help ? button.
   
   Image Added
   
   
4. For 'Input Features', use the drop-down menu to select the CensusTracts_Race_2014  layer.
5. For 'Input Field', use the drop-down menu to select the Percent_White field.
6. For 'Output Feature Class', rename the feature class "HSA_White".
7. For 'Distance Band or Threshold Distance', type "24962".
8. Click Run.

Cluster and Outlier Analysis (Anselin Local Moran's 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 Moran's I) tool.
3. For 'Input Feature Class', use the drop-down menu to select

...

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) tool.

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

...

1.  the CensusTracts_Race_2014 layer.
2. For 'Input Field', use the drop-down menu to select the Percent_White field.
3. For 'Output Features', rename the feature class "COA_White".
4. Click Run.

A new layer has been added to your map. Cluster and outlier analysis is often more telling at a smaller geographic unit.