Predictive Hotspot Mapping for Data-driven Crime Prediction

TL;DR

This paper introduces a non-parametric spatio-temporal model for crime hotspot mapping that leverages historical data and expert inputs, aiding police in resource allocation and crime prevention.

Contribution

It presents a novel non-parametric approach combining spatio-temporal kernel density estimation with expert input integration for crime prediction.

Findings

Effective in real-world Delhi police deployment

Promising results for crime hotspot identification

Dataset and algorithm released for future research

Abstract

Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving towards predictive approaches relying on historical data. In this paper, we create a non-parametric model using a spatio-temporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.