This week’s lab focused on creating different hotspot maps for crime analysis. Honestly, it felt like I was just trying to survive ArcGIS Pro at 1 AM while everything was crashing or giving me errors.
First, I made three hotspot maps:
1. Grid Overlay Map – showed the grid cells with the highest homicide counts.
2. Kernel Density Map – showed areas where homicides were most concentrated in Chicago.
3. Local Moran’s I Map – showed statistically significant clusters of high homicide rates.
For this lab, I used different tools like Spatial Join, Kernel Density, Reclassify, Select by Attributes, and Dissolve. Each tool helped build out the different hotspot techniques. I spent a lot of time figuring out:
How to calculate crime rates and join data to census tracts.
Setting up the correct symbology breaks for each map so it only showed what was required, like “3 times the mean” for kernel density.
Making sure I saved outputs into the right folders (ArcGIS kept yelling at me when I had spaces in folder names).
Double checking reclassification values to get the maps to look similar to the lab examples.
Keeping track of which step I was on because this lab felt like it never ended. I also had to manage my time better, since I started this late at night and was tired, which made following small details harder. Next time, I’ll start earlier so I don’t feel like I’m fighting both ArcGIS and my sleep schedule.Brief Discussion of Results
Out of the three maps, I think Kernel Density is the most useful for a police chief trying to decide where to allocate patrols. It shows smooth, clear hotspots without being restricted to grid lines or tract boundaries. If a police chief needed a quick visual to know where violent crime is most concentrated, this map would highlight those areas best.
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