Module 1: Crime Analysis

 During this week's module, I had the opportunity to work on various forms of mapping crime analysis. The three hotspot maps I was tasked with creating were a kernel density map, grid-based thematic map, and a local moran's 1 high-high cluster map. One of the important things I had to keep in mind throughout the crime analysis module was to update the environmental parameters to reflect the geographic location that I was working in. A simple, yet hindering mistake if not updated. While completing the grid-based thematic map, it was simple to spatially join Chicago grid cells and 2017 homicide points in order to place a number of homicides per cell. In order to narrow down the data, only the top 20% of grid cells with the highest amount of homicides were taken into account. This could potentially be a problem when analyzing the map when trying to find areas where crime is on the rise. The finished map was obtained by dissolving the newly created feature class with the top 20% into a single polygon. For the kernel density map, the kernel density tool was utilized to convert the 2017 homicide data into a raster that would become a polygon. This was done by reclassifying the raster on break values; three times the mean and the max value from the homicide data. For the local moran's 1 map, a different approach was taken. I conducted a field calculation to determine the homicide count per 1,000 households. The cluster and outlier analysis tool was then used to determine the clusters of high crime density using the crime rate calculated during the previous step. The high-high clusters were the desired. This allows someone to analyze the clusters for areas of high crime relative to other nearby high crime areas. 

I believe that the kernel density map would be most beneficial to someone, such as a police chief, due to how the values are distributed. The kernel density map puts more weight towards crimes being committed near the center of the hotspot. Ideally, the police chief would want to send patrol cars to an area with the heaviest concentration of crime at the center of a hotspot. The kernel density map displays a larger volume of crime at the center of hotspots, as opposed to the local moran where you might have to decide whether to send units to a low-high outlier area with crime. The police chief would be less inclined to send officers to that area, but could regret it in the future when the crimes in high areas start to seep into the nearby low areas. It would also be wise to continue patrolling the same hotspots in the future, given that the 2018 hotspots are similar to the 2017 ones. There also seems to be a trend of homicides taking place alongside major highways and inner city, where impoverished areas are more common.