KMS Of Academy of mathematics and systems sciences, CAS
| Static or dynamic? Characterize and forecast the evolution of urban crime distribution | |
| Zhu, Qing1,2; Zhang, Fan1,2; Liu, Shan2; Wang, Lin3; Wang, Shouyang4 | |
| 2022-03-15 | |
| Source Publication | EXPERT SYSTEMS WITH APPLICATIONS
 |
| ISSN | 0957-4174 |
| Volume | 190Pages:16 |
| Abstract | 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. |
| Keyword | Urban crime Spatiotemporal framework Crime distribution Graph neural network |
| DOI | 10.1016/j.eswa.2021.116115 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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 Research Area | Computer Science ; Engineering ; Operations Research & Management Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science |
| WOS ID | WOS:000720552900005 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/59581 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Liu, Shan |
| Affiliation | 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 |
| Recommended Citation 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. |
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