Module 1: Crime Analysis

For this lab the task was to show hotspot analysis using 3 methods (Grid-Based Thematic Mapping, Kernel Density, and Local Moran's I) The below screenshots show the hotspots of homicides from 2017 in Chicago, IL.

First up is Grid-Based: Based on a grid of 1/2 mile cells clipped to the Chicago city boundary, the end result is the grids with the top 20% overall highest count of homicides. Utilizing Spatial Join, Select by Attributes, and Dissolve tools.

Second is Kernel Density: This map shows the density of homicides that are three times the mean of the data. Utilizing Kernel Density, Reclassify, and Select by Attributes tools.

Last is Local Moran's I: This map uses crime counts and number of homicides per 1,000 housing units for each census block group. The result is spatial clusters of the high-high clusters that are in close proximity to other areas with a high homicide rate. Utilizing Spatial Join, Cluster and Outlier Analysis, and SQL Query tools.

Comments: Other than input and output data any remaining parameters in the tools used were left as default per lab instructions.