GIS 4048 M1 Lab, Hotspot Mapping

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The analysis followed a lab guide to explore burglary and homicide patterns in Washington, DC. First, I filtered the original crime dataset using a SQL query to isolate burglary incidents and exported this subset as a new feature class. I then spatially joined burglary points to census tracts to calculate burglary counts per tract, and used a Python formula to calculate burglary rates per 1,000 housing units.

For the second part we created three hotspot maps of murders in Chicago. I created a grid based map of murders, isolating the top 20%. This involved dividing areas with homicides by 5 and selecting the top fifth from the total list, which seemed imprecise, because a lot of values equal to values in the top 20% were cut off, solely by their list rank despite identical values.

I also created a kernel density map and used symbology to isolate values 3x the mean, and used geoprocessing tools to create a new polygon.

The third hotspot map I created used local Moran’s I, again joining census tracts with point crime data, and python to determine a homicide rate (this is slightly different than the earlier exercise, because the table listed households instead of housing units!). Then I used the Moran’s I tool to generate a map cluster map, and a SQL selection to isolate the high-high clusters, and dissolve and export to create a new feature class of just the high high areas.

The final parts of the lab asked us to quantify the area of each hotspot map, and look at how many murders from the next year occurred inside each style

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