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

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...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 9; no. 12; p. 732
Main Authors Yu, Hongjie, Liu, Lin, Yang, Bo, Lan, Minxuan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2020
Subjects
Accuracy
Algorithms
Crime
crime prediction
Crime prevention
Criminal statistics
Criminology
Data
Data integration
Datasets
Decision trees
historical crime
Learning algorithms
Machine learning
Model accuracy
Police
potential offenders
Prediction models
Recidivism
Robbery
Semantics
Social networks
ST-Cokriging algorithm
Statistical methods
Weekly
United States > US
Illinois
Chicago Illinois
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi9120732