Monday, June 19, 2017

Washington D.C. Crime Analysis

This week I went over multiple ways to spatially analyse crime reports, using reports from January 2011 in Washington D.C. There was a lot going on this week so I'm going to toss up a map first and go into details afterward.

There is a lot going on in this map, first off we have the point layer showing the crimes reported that January. This data was separated by offense and graphed by occurrence, as seen by the graph in the corner of the map. This layer is the basis for our spatial analysis, using the point data of police stations in Washington I joined the points spatially to the crimes layer which links the data from each individual crime report to the closest police station. Dividing the crime reports up to their closest station point gives of the percentage of crimes handled by each police station based on location, the station points were symbolized accordingly. The next technique is similar but it begins with a multiple ring buffer from the police station points making a new parcel at half a mile, one mile, and two miles away from a police station. Then I used the same spatial joining to find out what percentage of reported crimes were which distance from a police station, which is explained in the map text. Finally I was tasked with proposing a place for a new police station or stations and I chose two potential areas. The first was chosen to relieve some of the stress from the station with the highest percentage of crime by distance. The second was chosen for a similar reason, the station with the second highest percentage of crime is nearby, also the second location would also pick up a number of crime reports that are currently outside of the two mile area around a police station.

Finally, I did a new type of spatial analysis called Kernel Density Analysis, again let's bring the map out first.

Kernel Density Analysis uses point placement to create a density raster based on how often a point class appears and how close those points are to each other. This maps breaks the crimes point class down into specific offenses and runs a kernel analysis on those points to create a density map which I've overlayed onto the population density map. This style of map allows the user to compare how the different type of crimes and where they occur compare to one another.

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