Paper Info
Title
A Spatio-Temporal Method For Crime Prediction Using Historical Crime Data And Transitional Zones Identified From Nightlight Imagery
Abstract
Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.
Year
DOI
Venue
2020
10.1080/13658816.2020.1737701
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
DocType
Volume
Crime prediction, spatio-temporal modeling, Cokriging, VIIRS nightlight
Journal
34
Issue
ISSN
Citations 
9
1365-8816
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Bo Yang119453.08
Lin Liu215026.85
Minxuan Lan312.99
Zengli Wang400.68
Hanlin Zhou502.03
Hongjie Yu600.34