Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method

Hongjie Yu,Bo Yang,Minxuan Lan,Lin Liu

doi:10.3390/ijgi9120732

Abstract

Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.

Highlights

Since criminal activities are closely related to the social and built environment [1,2,3], the rapid change of the latter two may alter the spatial and temporal crime pattern, which in turn, brings new challenges to the city management
The results revealed the effectiveness of the high temporal resolution “potential offender” covariate on the crime prediction
The new ST-Cokriging algorithm extends the spatial structure to a spatio-temporal domain; especially, the temporal independences are modeled by the temporal semi-variogram

Summary

Introduction

Since criminal activities are closely related to the social and built environment [1,2,3], the rapid change of the latter two may alter the spatial and temporal crime pattern, which in turn, brings new challenges to the city management. Benefited from its general applicability and predictive ability, machine learning has been used in various disciplines, including criminology Both scholars and practitioners have been trying to take advantage of various machine-learning algorithms to predict crime patterns and tailor situational crime prevention strategies [4,5,6,7,8]. Some of them solely use historical crime data [7,9,10,11], while many consider additional factors for the sake of improving the accuracy of crime prediction [12] The latter approach is theoretically sound because the distribution of crimes often has complex relationships with the social/built environment (e.g., nearby buildings, facilities, residents and activities, the perception of crime) [13,14,15,16,17,18,19]

Objectives

Methods

Results

Conclusion