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

• NCJ Number: 254389
• Journal: Applied Geography Volume: 99 Dated: 2018 Pages: 89-97
• Author(s): Yujie J. Hu; Fahui Wang; Cecile Guinc; Haojie Zhub
• Date Published: 2018
• Length: 9 pages
• Grant Number(s): 2012-PB-FX-K001
• Sponsoring Agency: Office of Juvenile Justice and Delinquency Prevention (OJJDP)
• Publication Format: Article
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Language: English
• Country: United States of America

Annotation

Building upon recent works in related fields, this article proposes a spatio-temporal framework for predictive hotspot mapping and evaluation.

Abstract

Predictive hotspot mapping plays a critical role in hotspot policing; however, existing methods, such as the popular kernel density estimation (KDE), do not consider the temporal dimension of crime. The current article argues that compared 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. (publisher abstract modified)