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

arXiv: https://arxiv.org/abs/2006.00272 · HTML: https://mlsanity.com/paper/2006.00272/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 3 of 3)

- [A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City](https://mlsanity.com/paper/2602.02566/) (2026)
- [Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey](https://mlsanity.com/paper/2403.00780/) (2024)
- [Predictive Hotspot Mapping for Data-driven Crime Prediction](https://mlsanity.com/paper/2602.23750/) (2026)

## Abstract (arXiv)

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.