Crime Analysis

      For this assignment, I created three maps, each using a different hotspot technique. To achieve this I first calculated the area in square miles for each hotspot map by creating a new field called “Shape_Area_sqmi.” I then used the Calculate Geometry option by right-clicking the new field in each attribute table. Since the kernel density hotspot map consisted of multiple separate polygons, I had to use the “summary statistics” geoprocessing tool to add all of their areas together to get the total coverage. I then selected the 2018 homicides within the 2017 hotspot areas by location. After that, I exported the selected features to create a shapefile containing those selections. Next, I used spatial join for each hotspot map to get the count of 2018 homicides within each map. I, again, had to use the “summary statistics” geoprocessing tool to add all of the 2018 homicides within the kernel density map. For each of the new shapefiles I created (hom2018_In2017GO, hom2018_In2017KD, hom2018_In2017LM), I added a new field within their attribute tables called “hom_per_sqmi” and then used the calculate field tool using the expression [!Join_Count! / !Shape_Area_sqmi!] for each map to get their crime densities. For the kernel density map, I first used the “summary statistics” geoprocessing tool to find the minimum and maximum of homicides per square mile. Then I also got the median since the sum of them seemed skewed. There may have been a better way to represent the data for the kernel density map, but for the time being, this was the best way I knew how.







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