GIS5100 Module 1: Crime Analysis

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Getting the ball rolling again after a nice vacation has been hard, but I'm doing my best to stay on top of things, especially with a few extra days off thanks to the July 4th weekend. To dive right into the swing of things, it's the next course on Applications in GIS, featuring the first Module on Crime Analysis.

The work I do doesn't focus on identifying and analyzing hotspots, so it was a nice change of pace to learn and work through the process of creating different types of hotspot maps and how such data can assist in crime prediction. The three main types of hotspot analyses that were covered were Grid Overlay, Kernel Density, and Local Moran's I.

The two main datasets we worked with were in Washington, DC and Chicago, Illinois. Washington, DC's data helped us get a foundation for learning how to prepare burglary rates in a choropleth map. Then, we created kernel density hotspots in DC as well. From there, we went to Chicago and created 3 hotspot maps: Grid, Kernel Density, and Local Moran's I.

The following are my impressions of each hotspot mapping result.

Grid-Based: Blocky polygon shapes, with a nice balance between clusters and cityscape blocks. Easy to digest and read.

Kernel Density: Highly focused, with irregular polygon shapes. Gives a more defined and honed-in approach to hot spotting. Takes a while to process but feels more precise.

Local Moran’s I: Very broad and blocky/angular polygon shapes. Appears more comprehensive but sacrifices focus.

Overall, I can see the purpose for all 3 types of hotspot mapping depending on what the end goal is. The biggest hurdle -- outside of spelling "burglaries" correctly, funny enough -- I had with this lab was that whenever we had to update the Symbology Upper Values for Kernel Density, I had to be extra careful, updating class by class. If I had entered a value that was higher numerically than the next one, it would ruin the symbol order and my classes entirely. Also, making sure to omit 0 values was crucial to not throw off the mean. Otherwise, I did my best to follow the lab instructions, especially the different tool parameters accordingly.

It was nice to learn that Grid-based hotspotting involved largely tabular data preparation with joining, isolating the top percentile, filtering out 0 values, etc. The main geoprocessing tool ran was the Spatial Join. As for Kernel Density, it had its own tool with the same name "Kernel Density". This is where Symbology updating was crucial, then we had to reclassify and change the raster to a polygon for better representation. Finally, Local Moran's I also relied on Spatial Join and field calculating; however, it also had its own Cluster and Outlier Analysis (Anselin Local Moran's I) tool. Knowing that the geoprocessing analysis tools share similar names to the type of hotspot mapping technique, it's reassuring that I will have an easier time remembering them if I ever need them in the future.

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