Week one of Applications in GIS introduces us to crime hotspot mapping and the different methods used to identify and analyze areas with elevated crime rates, enabling law enforcement and community planners to allocate resources effectively and implement targeted interventions.
In the first part of this week’s lab, we conducted an exploratory crime analysis focused on burglaries in Washington, DC. Using ArcGIS Pro, we isolated burglary reports and analyzed their distribution across census tracts. By counting the number of incidents per tract and normalizing this data against the number of housing units, we calculated a burglary rate, which provides a clearer understanding of crime patterns. The map pictured above illustrates the burglary rates across the census tracts.
In part two of this week's lab, we explored various methods of crime hotspot mapping, focusing on techniques like Grid Overlay, Kernel Density, and Local Moran's I. Each method offers unique insights into crime patterns, allowing us to assess their effectiveness in identifying high-crime areas.
The graphic above illustrates the results of our analysis, highlighting the different hotspot areas identified by each method, which can aid law enforcement in making informed decisions regarding resource allocation and crime prevention strategies.
After completing the three different methods of hotspot analysis, we were tasked with deciding which method we believe would be the most useful for a police chief working to predict and prepare for future crime patterns.
From the reading materials we have covered this week, as well as seeing and working with these data in the real world, I believe the Kernel Density method of hotspot mapping would be the most ideal for a police chief when making decisions on how to police their region.
While the Grid Overlay method captured a much larger area, the crime density doesn't seem as high as I would have thought, which leads me to believe it includes some areas with lower crime rates. This large area could strain department resources if they attempted to police it in its entirety.
The Local Moran's I method returned a more balanced area compared to the other two methods; however, its crime density is the lowest of the three, suggesting it may not be as helpful for targeting high-crime areas.
The Kernel Density method returned the smallest area but the highest crime density. This indicates it is more accurate in pinpointing high-crime areas. The department can focus on a smaller area, utilizing its resources more efficiently and effectively.
This insight into crime analysis and mapping techniques was fascinating, and I could definitely see myself enjoying working in this application of GIS. I believe that more education in criminal justice would be necessary to truly be of help to law enforcement, but I'm always open to learning.
I am excited for lab two, which covers LiDAR (Light Detection and Ranging). LiDAR is a remote sensing technology that uses laser light to measure distances and create precise, three-dimensional information about the Earth's surface and other objects.
