M1 Crime Analysis

    In this crime analysis module, I explored how GIS can help law enforcement identify crime hotspots using real data from Washington DC and Chicago. I started by mapping burglary rates in DC, normalizing the data by housing units to create a fair comparison across neighborhoods, and used kernel density analysis to visualize assault patterns as smooth "heat maps" showing crime concentration. The kernel density technique was particularly effective at revealing clustering patterns that weren't obvious when just looking at individual crime points.

    The most interesting part involved comparing three different hotspot mapping techniques using Chicago homicide data: grid-based mapping, kernel density analysis, and Local Moran's I statistical clustering. I tested each method's effectiveness by using 2017 crime data to predict where 2018 homicides would occur. The analysis revealed important trade-offs between accuracy and area coverage - methods that captured more future crimes often required monitoring larger areas, which has real implications for how police departments allocate their limited patrol resources. 


Grid Overlay
Kernel Density
Local Moran's I







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