Using the Mapping Clusters tool in vehicle theft data

In the last section, our analysis revealed that vehicle theft in Seattle is clustered. Now, we'll expand our analysis to include the use of several tools found in the Mapping Clusters toolset, including Hot Spot Analysis, Grouping Analysis, and Cluster and Outlier Analysis:

1. Let's start the clustering analysis by running the Hot Spot Analysis tool. Define Seattle_NHood_VehicleTheft as the input feature class and NormVT as the analysis field. You can define the name and location of the output feature class. For the Conceptualization of Spatial Relationships parameter, use your knowledge of the neighborhood boundaries and the dataset to select an appropriate value. The following screenshot shows the output using CONTIGUITY_EDGES_CORNERS. You may also want to run this tool multiple times with different values for the spatial relationship parameter to see...