Crime Analysis

This week, we looked at three different types of analysis for the purpose of predicting crime and thus enhancing the effectiveness of policing efforts.  We compared Grid Overlay, Kernel Density, and Local Moran's I analysis using 2017 homicide data in Chicago to predict areas of homicide in 2018 and thus determine the best analysis that can be used for a cost-effective solution for future policing efforts.

To complete the analysis I first set the environments so the extent and mask were equal to the boundary of Chicago. Then, for the Grid Overlay method, I used the spatial join tool and entered 2017 homicides to be joined with the Chicago grid. I then selected all grids that were greater than 0 and created a new layer called Homicide Count. I then selected the top 20% from those results. I had 311 total cases and the top 20% resulted in 62 cases selected. I then create another layer from that selection, dissolved it into a single field and the first map below was the result.

For Kernel Density, I used the Kernel Density tool on the 2017 homicides layer, selected an output cell size of 100 and a search radius of 2630. I then split the results into two categories: 0 to 3* the mean, and everything else above that. I then reclassified the data into those two categories and converted the raster to a polygon. After that, I selected all cases that were 3* the mean or greater and created a layer from that selection. The map is also shown below.

For the Moran’s I analysis, I again performed a spatial join between census tracts and 2017 homicides. I then added a new field in the attribute table and calculated the crime rate per 1000 homes. I then used the Cluster and Outlier Analysis tool. From that result, I selected the High-High results and created a new layer from that selection. After that, I dissolved the layer into a single field. The results are also shown below.

Grid Overlay

 Kernel Density

 Local Moran's I

Hotspot Technique	Total area (mi2) in 2017	Number of 2018 homicides within 2017 hotspot	% of all 2018 homicides within 2017 hotspot	Crime density (2018 homicides within 2017 hotspot per mi2)
Grid Overlay	15.46	159	27.00%	10.28
Kernel Density	26.67	262	44.48%	9.82
Local Moran's I	34.05	265	45.00%	7.78

The Kernel Density analysis provides the best model for predicting future homicides. This is because it captured nearly the exact same number of homicides in 2018 as the Local Moran’s I analysis, but did so in an area 7.38 square miles smaller. This would allow the enforcement effort to be much more concentrated and focused while addressing a similar amount of crime. Kernel Density is also better than the Grid Overlay method because it captures a much more significant number of overall homicides, and the density of homicides per square mile is not that much less in the Kernel Density analysis. So you definitely get the most bang for your buck with the Kernel Density analysis.

I also want to highlight what I think is a profound level of short-sightedness in an analysis such as this. While the stated objective and results of such analysis contain useful information, the desire to focus on a single variable as a cure for the problem of homicide creates the huge possibility of mismanagement, misallocation of resources, and even the active continuation and enhancement of foundational issues which ultimately result in situations where criminal activity incubates. These analyses can and should be used, but they must be used in ways that incorporate a fundamental understanding of wealth disparity, educational funding and availability, local economic opportunity, and other factors I am likely not mentioning here.

The issue of homicide, and crime in general, must be taken into consideration along with the functioning of society as a whole. If the police chief is asking for this information, we need to be asking the police chief who they are partnering with in the community in order to understand the underlying causes of such violence and work toward creating partnerships with the Health Departments, Education Departments, and the general governing body within a given community in order to find solutions that really get at the heart of the problem. A proactive and integrated approach can, in turn, create a more prosperous community that has great potential to reduce the burden on law enforcement and increase its overall effectiveness.

I suggest that we begin looking at these types of crime analysis with a much broader view of community interactions and attempt to limit the idea of a single variable solution.