Testing a Geospatial Predictive Policing Strategy: Application of ArcGIS 3D Analyst Tools for Forecasting Commission of Residential Burglaries

The purpose of this project is to test the extent to which geo-referenced data and geospatial technologies can help us better forecast commission of residential burglaries. Although new technologies have allowed us to measure the intensity of natural surveillance in new ways, we have not yet scientifically tested whether these technologies can help us better comprehend the existence of a relationship between natural surveillance and the commission of crimes.

This study employees a mixed methods approach. Qualitative data (information from oblique aerial imagery, site surveys and police reports) are embedded and provide a supportive role for the quantitative data (geo-referencing this information in the ArcGIS platform) and quantitative analysis. In a Census block group comprised of 324 parcels and 490 buildings, data on the position, size and height of opening, vegetation and visual barriers have already been collected from geo-referenced oblique aerial imagery. This researcher verified and complemented this data during five additional site survey visits. Spatial data were then geo-referenced and mapped using the ArcGIS platform. 3D models for all features were constructed. The Spokane Police Department permitted the researcher to read residential burglary crime reports for the 5-year period between 2006 and 2010 for the same area.

During this project, ArcGIS 3D analyst tools will be employed to generate sight-lines from each opening to openings in all other buildings on the same street segment. Next, intensity of natural surveillance for each opening will be calculated. This information will be used to create expectations regarding the most likely entry points for burglaries. Based on an analysis of police crime reports, the actual burglary entry points will be compared to the measured intensity of natural surveillance for each opening. This methodology not only analyzes surveillance characteristics of residential burglaries, but can create a model and methodology for studying other crimes with a spatial visibility component.

ca/ncf