A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation

TL;DR

This paper introduces a novel spatio-temporal kernel density estimation framework for more accurate predictive crime hotspot mapping, incorporating temporal data, statistical significance testing, and a new evaluation metric.

Contribution

It presents a comprehensive framework that integrates spatio-temporal KDE, data-driven bandwidth selection, significance testing, and a novel evaluation metric for predictive hotspot mapping.

Findings

The framework effectively identifies crime hotspots with improved accuracy.

The likelihood cross-validation optimizes bandwidth selection.

The PAI curve provides a robust evaluation across scales.

Abstract

Predictive hotspot mapping plays a critical role in hotspot policing. Existing methods such as the popular kernel density estimation (KDE) do not consider the temporal dimension of crime. Building upon recent works in related fields, this article proposes a spatio-temporal framework for predictive hotspot mapping and evaluation. Comparing to existing work in this scope, the proposed framework has four major features: (1) a spatio-temporal kernel density estimation (STKDE) method is applied to include the temporal component in predictive hotspot mapping, (2) a data-driven optimization technique, the likelihood cross-validation, is used to select the most appropriate bandwidths, (3) a statistical significance test is designed to filter out false positives in the density estimates, and (4) a new metric, the predictive accuracy index (PAI) curve, is proposed to evaluate predictive hotspots at multiple areal scales. The framework is illustrated in a case study of residential burglaries in Baton Rouge, Louisiana in 2011, and the results validate its utility.