Predictive Hotspot Mapping for Data-driven Crime Prediction
Karthik Sriram, Ankur Sinha, Suvashis Choudhary

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…
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Taxonomy
TopicsCrime Patterns and Interventions · Anomaly Detection Techniques and Applications · Human Mobility and Location-Based Analysis
