Criminal incident prediction using a point-pattern-based density model

Hua Liu

doi:10.1016/S0169-2070(03)00094-3

Return

Criminal incident prediction using a point-pattern-based density model

2003-10-01

PRE

Hua Liu

Donald E. Brown

DOI：10.1016/S0169-2070(03)00094-3

Original

Original request for help

Save

Abstract

En 中文

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or hot spots observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space-time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA. (C) 2003 International Institute of Forecasters. Published by Elsevier B. V. All rights reserved.

Keywords:

point-pattern methods

crime forecasting

spatial transition density model

hot spot prediction model

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.