Self-exciting point processes with spatial covariates: modeling the dynamics of crime

TL;DR

This paper introduces a novel spatio-temporal self-exciting point process model that captures complex crime patterns influenced by environmental, economic, and behavioral factors, improving understanding and prediction of criminal activity.

Contribution

It develops a new model integrating spatial features, near-repeat, and retaliation effects, along with inference methods and diagnostic tools for crime data analysis.

Findings

Model accurately captures crime pattern dynamics.

Simulation and real data validate model effectiveness.

Provides insights into crime triggering mechanisms.

Abstract

Crime has both varying patterns in space, related to features of the environment, economy, and policing, and patterns in time arising from criminal behavior, such as retaliation. Serious crimes may also be presaged by minor crimes of disorder. We demonstrate that these spatial and temporal patterns are generally confounded, requiring analyses to take both into account, and propose a spatio-temporal self-exciting point process model which incorporates spatial features, near-repeat and retaliation effects, and triggering. We develop inference methods and diagnostic tools, such as residual maps, for this model, and through extensive simulation and crime data obtained from Pittsburgh, Pennsylvania, demonstrate its properties and usefulness.