Static or dynamic? Characterize and forecast the evolution of urban crime distribution

doi:10.1016/j.eswa.2021.116115

CSpace

	Static or dynamic? Characterize and forecast the evolution of urban crime distribution
	Zhu, Qing 1,2; Zhang, Fan 1,2; Liu, Shan 2; Wang, Lin 3; Wang, Shouyang 4
	2022-03-15
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	190 页码:16
摘要	Despite the considerable deployed resources, current policing efforts are failing to stop crimes before they start, and therefore, also failing to adequately protect lives and property. To promote the intelligent transformation from reactive to proactive policing, this study proposed a hierarchical crime prediction framework. First, the temporal dependency in the frequency domain was decomposed and a network constructed to capture the spatial relationships within the sub-frequencies. Human mobility in a city was then utilized to characterize the dynamic relationships within the network. Using the proposed framework, this study examined the crime distribution evolution in Chicago to holistically predict the short-term crimes in the different communities. The framework was found to have high predictive accuracy and significant potential in promoting proactive policing. It was concluded that: (1) as the crime distribution evolution comes from the spatial relationship changes, these dynamic relationships are critical in explaining and characterizing the evolution; and (2) the social interactions constructed using the human activity data can characterize the dynamic crime distribution relationships.
关键词	Urban crime Spatiotemporal framework Crime distribution Graph neural network
DOI	10.1016/j.eswa.2021.116115
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation (NSFC) Programs of China[72011540408] ; National Natural Science Foundation (NSFC) Programs of China[71722014] ; National Natural Science Foundation (NSFC) Programs of China[71731009] ; National Research Foundation of Korea[NRF-2020K2A9A2A06069972] ; Youth Innovation Team of Shaanxi Universities Big data and Business Intelligent Innovation Team''
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000720552900005
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59581
专题	中国科学院数学与系统科学研究院
通讯作者	Liu, Shan
作者单位	1.Shaanxi Normal Univ, Int Business Sch, Xian 710061, Peoples R China 2.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China 3.Huazhong Univ Sci & Technol, Sch Management, Wuhan 430074, Peoples R China 4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Qing,Zhang, Fan,Liu, Shan,et al. Static or dynamic? Characterize and forecast the evolution of urban crime distribution[J]. EXPERT SYSTEMS WITH APPLICATIONS,2022,190:16.
APA	Zhu, Qing,Zhang, Fan,Liu, Shan,Wang, Lin,&Wang, Shouyang.(2022).Static or dynamic? Characterize and forecast the evolution of urban crime distribution.EXPERT SYSTEMS WITH APPLICATIONS,190,16.
MLA	Zhu, Qing,et al."Static or dynamic? Characterize and forecast the evolution of urban crime distribution".EXPERT SYSTEMS WITH APPLICATIONS 190(2022):16.