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Image
1. Grid Overlay Hotspot Mapping |
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Image 2. Kernel
Density Hotspot Analysis |
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| Image 3. Local Moran's Hotspot Mapping |
To complete
this analysis, I created three different hotspot maps using the 2017 homicide
data for Chicago. I began with the grid‑based hotspot method by joining the
homicide points to the half‑mile grid cells, selecting only the cells with at
least one homicide, and then identifying the top twenty percent with the
highest counts. I dissolved these selected cells into a single polygon to
represent the grid‑based hotspot. Next, I created a kernel density hotspot by
running the Kernel Density tool with the appropriate parameters for Chicago,
adjusting the symbology to isolate values at or above three times the mean,
reclassifying the raster into two classes, converting it to polygons, and
selecting only the highest‑density areas. For the Local Moran’s I hotspot, I
joined the homicide data to the census tracts, calculated homicide rates per
one thousand housing units, and ran the Local Moran’s I tool to identify
statistically significant high‑high clusters. I selected those clusters and
dissolved them into a single boundary. These three hotspot methods provided
different outlines of where homicides were concentrated in 2017, which I later
compared to the 2018 homicide locations to evaluate how well each method
predicted future crime.
The Local Moran’s
hotspot would not be a great future predictor because, although it has a high
number of 2018 homicides, this occurred over a very large area of 52.67 square
miles. This is too broad and unable to pinpoint resource allocation accurately.
The Grid Overlay and
Kernel Density data are relatively similar, with smaller areas and a large
number of 2018 homicides. However, the density calculation is the dealbreaker
here, with a high concentration of crime that is useful for a police chief to
allocate his resources. Due to this, the Kernel Density hotspot analysis
technique is the best to use as a production of future crime and allocation of
resources.



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