Crime Analysis in GIS

Our first topic in GIS Applications is crime analysis and the use of crime mapping for determining crime hotspots. Crime mapping techniques provide insight into the spatial and temporal distributions of crime. This benefits criminologists in the research community and professionals in law enforcement.

Crime mapping factors in the importance of local geography as a reason for crime and considers that it may be as important as criminal motivation. The importance of identifying patterns and hotspots in crime mapping tends to be a precursor for implementing effective crime prevention methods.

Fundamental to crime mapping is spatial autocorrelation, which acknowledges the spatial dependency of values measured within areas. This recognizes that crime in one area can influence the crime rate of a nearby area.

We are tasked this week with quantifying data and generating hotspot maps showing crime density using various methods on the clustering of events. The Lab for Module 1 works with crime data for Washington, DC and Chicago.

Output in this week's lab, a kernel density map showing 2018 crime hotspots for Washington, DC

A relative measure, a crime hotspot represents an area with a greater than average frequency of criminal or disorderly events. An area where people have an above average risk of victimization can also be classified as a crime hotspot. Victimization however cannot always be shown on maps, as the theory refers to multiple incidents on the same individual, regardless of location. This can also represent a street (line) or a neighborhood (polygon) where repeat occurrences take place.

Determining crime hotspots can aid in detecting spatial and temporal patterns and trends of crime. The concept can benefit law enforcement in better allocating resources to target areas. Crime hotspots can also be used to identify underlying causes for crime events.

The concept of local clustering, concentrations of high data values, is the most useful for crime analysis. Methods determine where clusters are located and produce a hotspot map showing concentrations.

Point data can be used directly in this analysis of clusters. A collection of points can produce a hotspot whose bounds are derived from the local density of points. Using point data also has the advantage of not being constrained by a predetermined jurisdictional boundary. Data aggregated into meaningful areas, such as within a jurisdiction where the polygons consists of the highest values, can also result in hot spots. Aggregation can produce crime rates, such as the number of events per number of residents or per households for an area.

Choropleth map with aggregated data determining the crime rate for Washington, DC. 

The Lab for Module 1 focuses on three methods for local clustering. Grid-Based Thematic Mapping overlays a regular grid of polygons above point data of crime events. This produces a count of events for each grid cell. Since all cells are uniform in dimensions, the count is equivalent to a density.

The choropleth map output showing crime density can be further analyzed to determine the crime hotspots. Extracting the crime hotspots involves selecting the highest class of the data. Quintile classification is commonly used to determine this.

The data provided in Lab included point class data of homicides reported in the city of Chicago for 2017. Additionally we were supplied with polygon class data of 1/2 mile grid cells clipped to the Chicago city limits.

The grid cells and point data for Chicago were spatially joined and grid cells where the homicide value was zero were removed from analysis. Using quintile classification, the top 20% of grid cells based on the homicide values was extracted to generate a hotspot map:

Using point values, Kernel Density can also be used to calculate a local density without the use of aggregation. The estimation method utilizes a user-defined grid over the point distribution. A search radius, known as the bandwidth, is applied to each grid cell. Using these two parameters, the method calculates weights for each point within the kernel search radius.

Points closer to the grid cell center are weighted more and therefore contribute more to the total density value of the cell. The final grid cell values are derived by summing the values of all circle surfaces for each location. For the Lab, we used the Spatial Analyst Kernel Density tool in ArcGIS Pro. Input were the grid cell size and bandwidth to run on the 2017 homicides feature class for Chicago. The output was a raster file with ten classes.

Since we were only interested in areas with the highest homicide rate, we reclassified the raster data into two classes. The upper class ranged from a value three times the mean to the maximum value of the raster data. This represented the crime hotspot as estimated with kernel density:

Local Moran's I is the final method implemented on the 2017 Chicago GIS data for Module 1. A global measure of spatial autocorrelation, the Moran's I method addresses the question, are nearby features similar? Features that are closer to each other are more similar to one another than those located farther apart. Moran's I produces a single statistic that reveals if a spatial pattern is clustered by comparing the value at any one location with the value at all other locations.

The result of Moran's I varies between -1.0 and +1.0. Positive values correlate to positive spatial autocorrelation (clustering) and negative values with negative autocorrelation. Where points that are closer together have similar values, the Moran's I result is high. If the point pattern is random, the value will be close to zero.

For the Lab, the homicides feature class and census tract data were spatially joined. A field calculating the number of homicides per 1,000 units was added. This feature in turn was input into the Cluster and Outlier Analysis (Anselin Local Moran's I) Spatial Statistics tool to output a new feature class based upon Local Moran's I. The result includes attribute data revealing two types of clusters: High-High (HH) representing clusters of high values and Low-low (LL) representing clusters of low values.

High-high clusters in the context of the Chicago crime data represent areas with high homicide values in close proximity to other areas with high homicide values. These are the crime hotspots:

Sources:

Ratcliffe, J. (2010). Crime Mapping: Spatial and Temporal Changes. Handbook of Quantitative Criminology,. (pp. 5-8).  Springer New York, NY.

Eck, J.E., Chainey, S., Cameron, J.G, Leitner, M. & Wilson, R.E. (2005) Mapping Crime: Understanding Hot Spots. National Institute of Justice (NIJ) Special Report.