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An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago
Zhu, Qing1,2; Zhang, Fan1; Liu, Shan2; Li, Yuze3
2022-07-01
Source PublicationINFORMATION & MANAGEMENT
ISSN0378-7206
Volume59Issue:5Pages:13
AbstractThe sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.
KeywordCrime inference Public security Spatial heterogeneity Time-lag effect Machine learning
DOI10.1016/j.im.2019.103247
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation (NSFC) Programs of China[91646113] ; National Natural Science Foundation (NSFC) Programs of China[71722014] ; National Natural Science Foundation (NSFC) Programs of China[71471141] ; National Natural Science Foundation (NSFC) Programs of China[71350007] ; Youth Innovation Team of Shaanxi Universities ?
WOS Research AreaComputer Science ; Information Science & Library Science ; Business & Economics
WOS SubjectComputer Science, Information Systems ; Information Science & Library Science ; Management
WOS IDWOS:000814203000001
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/61206
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLiu, Shan
Affiliation1.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.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. An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago[J]. INFORMATION & MANAGEMENT,2022,59(5):13.
APA Zhu, Qing,Zhang, Fan,Liu, Shan,&Li, Yuze.(2022).An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago.INFORMATION & MANAGEMENT,59(5),13.
MLA Zhu, Qing,et al."An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago".INFORMATION & MANAGEMENT 59.5(2022):13.
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