A Spatio-Temporal Statistical Model of Crime Hotspots

• In Event: Methodological Advancements to Studying Crime and Development

Abstract

The concentration of crime into small "hotspots" has been widely observed across many different cities and types of crimes. Current tools to understand the causes and dynamics of crime hotspots are limited. A variety of mapping tools, for example, have been proposed to detect hotspots in crime data, but these tools cannot correlate clusters to events or covariates which may have caused them. Separately, methods such as Risk Terrain Modeling attempt to identify spatial features that predict crime rates, such as gang territories, bars, or poverty, but consider only chronic hotspots, not accounting for temporary flare-ups.

We propose a statistical model which accounts for spatial and temporal variation in crime by modeling both spatial features and near-repeat and retaliatory crimes, allowing it to model the birth and death of crime hotspots and the reasons they appear, and to statistically test hypotheses about each predictive variable. We demonstrate the model on a large dataset of crimes in Pittsburgh, Pennsylvania, showing its utility in understanding the dynamics of crime.

Authors

• Alex Reinhart, Carnegie Mellon University

• Daniel Nagin, Carnegie Mellon University