Module 1: Crime Analysis

 

This week’s assignment focused on using spatial analysis techniques to create and evaluate crime hotspot maps. Our objective was to explore how different methods—Kernel Density, Grid Overlay, and Local Moran’s I—can be used to analyze 2017 homicide data in Chicago and assess how well those methods could predict the locations of homicides that occurred in 2018. The goal was not only to map past crime, but to consider how GIS can support proactive decision-making in public safety.

In this analysis, I applied the three hotspot techniques using 2017 homicide data. For the grid and Moran’s I methods, I performed spatial joins to aggregate homicide counts by census tract or grid cell. I then normalized the data where needed, such as calculating homicide rates per 1,000 households, to make the results more meaningful. Each method produced a different pattern of “hotspot” areas across the city.

After creating the hotspot maps, I evaluated their predictive power by comparing them to 2018 homicide locations. Specifically, I looked at how many of the 2018 homicides occurred within the 2017 hotspot areas, as well as how large each hotspot map was. The idea was to understand which method best balanced accuracy with efficiency—since covering the whole city would technically catch all crimes, but wouldn't help a police department allocate resources effectively.

Each method brought a different perspective. Kernel Density highlighted very concentrated areas of risk; useful for focused enforcement. Grid Overlay offered a middle-ground approach, capturing a moderate number of crimes in a reasonably sized area. Local Moran’s I, while the broadest in coverage, predicted the most crimes and could be helpful for long-term planning rather than day-to-day response.

This assignment was a great example of how GIS can go beyond mapping to actually support critical decisions. By comparing the effectiveness of different techniques, we can better understand which tools are most useful depending on the context and objectives; whether for strategic planning or targeted intervention.

Grid-based Thematic Hotspot Mapping:

Kernel Density Hotspot Mapping:

Local Moran's I Hotspot Mapping: 

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