This week, we created hotspot analysis maps using three different techniques and compared the outcomes. The maps looked at homicides in Chicago in 2017 and compared the usefulness of their predictions against the 2018 data. The three methods we used included Grid-based Thematic Mapping, Kernel Density, and Local Moran's I.
Grid-based thematic mapping involves classifying the homicides into small gridded areas across the city. This is accomplished through spatial joins of the homicides with the grid layer and filtering only the grids with crimes. We further sorted out only the top quintile of grids for consideration.
Kernel Density involves creating a raster file based on the point data from the homicides. You use symbology to help classify and visualize the data, in this case focusing on areas with crime densities at three times the mean or higher. You can convert the raster into a polygon.
Local Moran's I classifies the homicides by census tracts, using a spatial join of these two layers. The Moran's tool in ArcGIS Pro will provide a statistical analysis of the area and you can out significant clusters of high crime density by choosing those that come back with a high-high rating (areas with high homicide rates near other similar areas).
When crunching the numbers, the Local Moran's I seemed to have predicted the most homicides for 2018 based on the number of incidents as well as the percentage. However, when you take into account the density of the incidents per square mile, this method had the lowest number. I would say the Kernel Density seemed the best map as it had the second most predictions in number, the highest density predictions, and the size of the area fell in the middle - meaning it could be a more manageable area for police to cover.
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| Grid-Based Thematic Mapping |
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| Kernel Density |
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| Local Moran's I |
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