Using risk terrain modeling to predict homeless related crime in Los Angeles, California. (August 2019) - Yoo, Youngmin; Wheeler, Andrew P.
Abstract: We apply Risk Terrain Modeling (RTM) to identify the factors that predict homeless related crime at micro grid cells in Los Angeles, CA. We find that place based factors predicting whether homeless individuals are victimized or the offender being homeless are largely consistent with one another. Out of 26 different crime attractors and generators, prior drug arrests, homeless shelters, and bus stops are the three biggest factors in predicting homeless related crime. We show how the RTM model can effectively forecast future homeless related crimes as well. This suggests that targeted spatial strategies can reduce both homeless offending and victimization risk. Given that the majority of homeless individuals are only intermittently homeless, place based strategies may be a more an effective way to limit risk than strategies that focus on individuals. Highlights: Homelessness is increasing in the United States, and police are often tasked with crime problems related to homelessness. Spatial areas of high homeless crime risk can be generated based on factors of the built environment. Areas with many prior drug arrests and homeless shelters have the highest risk of homeless related crime. The predicted areas of high risk produced by RTM effectively forecasted future homeless crime risk.