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Activity Number: 609
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319982 View Presentation
Title: Point Process Modeling with Spatiotemporal Covariates for Predicting Crime
Author(s): Alex Reinhart* and Xizhen Cai and Joel Greenhouse
Companies: Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
Keywords: spatial data ; crime ; hotspots ; point process

Extensive research has shown that crime tends to be concentrated in hotspots: small pockets with above-average rates of crime. Criminologists and law enforcement agencies want to better predict crime hotspots and understand the factors that cause them, in order to target interventions. Prior research suggests that past crime hotspots, spatial features (like bus stops or bars), and leading indicators (like 911 calls) are all predictive of future crime, but no proposed predictive policing model can account for all of these factors. We have adapted a previous self-exciting point process model to incorporate past crime data, leading indicators, spatial features and spatial covariates (like population density or zoning data), and developed new tools to evaluate the performance of the model and select variables. We show the basic model and demonstrate its application to seven years of Pittsburgh crime data, comparing its fits to previous hotspot models. These results can be used to better guide crime prevention programs and police patrols.

Authors who are presenting talks have a * after their name.

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