Progress in Research and Practice of Spatial-temporal Crime Prediction over the Past Decade

被引：0
|
作者：
He R. [1 ,2 ]
Lu Y. [1 ,2 ]
Jiang C. [3 ,4 ]
Deng Y. [1 ,2 ]
Li X. [1 ,2 ]
Shi D. [1 ,2 ]
机构：
[1] College of Resources Environment and Tourism, Capital Normal University, Beijing
[2] Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing
[3] College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing
[4] Beijing Key Laboratory of Megaregions Sustainable Development Modeling, Beijing
来源：
Journal of Geo-Information Science | 2023年 / 25卷 / 04期
基金：
中国国家自然科学基金;
关键词：
crime analysis; crime prediction; ethical issues; geography of crime; predictive policing; proactive policing; smart policing; spatial-temporal scale;
D O I：
10.12082/dqxxkx.2023.220808
中图分类号：
学科分类号：
摘要：
As a forward-looking and proactive policing mode, predictive policing has been a major innovation of modern policing reforms across the USA and European countries since it was proposed in 2008. As it does not involve the use of personal privacy data and can be integrated with police patrolling and precise crime prevention strategies, place - based spatial - temporal crime prediction has been a hot research topic and main component of policing practices. This research presents a systematic review of the progress of spatial-temporal crime prediction across the world since 2013 when the RAND Corporation released its special report on predictive policing. It contributes to the literature with the following five aspects: (1) summarizing the new trends in the field of spatiotemporal crime prediction studies in terms of the number of papers, research topics, leading scholars, and academic journals. The studies on spatial- temporal crime prediction have received extensive attention from various countries in recent years, and the research themes have shown a diversified trend. The most productive scholars are mainly from China and the USA, with the main focus on spatial-temporal crime prediction model development; (2) describing the new dynamics and progress of six basic components involved in the spatial-temporal crime prediction research, which are the prediction target, temporal scale, spatial scale, prediction method, performance evaluation measure, and practical evaluation. The four most widely studied types of crimes are theft, robbery, burglary, and motor vehicle theft. For burglary crime, the typical temporal unit for spatial-temporal prediction is 1-month; For the other three types of crime, the typical temporal unit is 1-day. For these four types of crime, the typical spatial unit is 200-meter grid. The top three models with the best prediction performance are random forest model, spatial-temporal neural network model, and Hawkes process model; (3) introducing several main commercial softwares for spatial- temporal crime prediction and global predictive policing practices; (4) investigating the relevant ethical issues and potential challenges that are embedded in each stage of practical applications, including data & algorithm biases, lack of transparency and countability mechanism; (5) prospecting future research directions in spatial-temporal crime prediction areas. This research provides a brief and panoramic image of the field of spatial- temporal crime prediction and can act as a reference for researchers and practitioners in relevant fields including crime geography, smart policing, and Policing Geographic Information System (PGIS). © 2023 Journal of Geo-Information Science
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页码：866 / 882
页数：16
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