A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery. Issue 9 (1st September 2020)

• Record Type: Journal Article
• Authors: Yang, Bo; Liu, Lin; Lan, Minxuan; Wang, Zengli; Zhou, Hanlin; Yu, Hongjie
• Is Part Of: International journal of geographical information science. Volume 34:Issue 9(2020)
• Page Start: 1740
• Page End: 1764
• Publication Date: 2020-09-01
• Subjects: Crime prediction -- spatio-temporal modeling -- Cokriging -- VIIRS nightlight; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1737701
• Languages: English
• ISSNs: 1365-8816

Abstract

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.