A Nonparametric Test-Based Approach for Mining Spatio-Temporal Co-Occurrence Patterns of Urban Crimes

被引：0
作者：
Chen Y. [1 ]
Cai J. [1 ]
Liu Q. [1 ]
Deng M. [1 ]
Zhang X. [2 ,3 ]
机构：
[1] Department of Geo-Informatics, Central South University, Changsha
[2] Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing
[3] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2019年 / 44卷 / 12期
基金：
中国国家自然科学基金;
关键词：
Nonparametric statistics; Scale dependence; Significance test; Spatio-temporal co-occurrence patterns; Urban crimes;
D O I：
10.13203/j.whugis20180112
中图分类号：
学科分类号：
摘要：
Scientific suggestions for crime prevention and control can be provided by analyzing the association relationship among multi-types of crimes based on spatio-temporal co-occurrence pattern discovery method. User-specified thresholds of prevalence measures are usually required by existing methods to filter mining results. Wrong decisions may be made by application departments without enough prior knowledge. Thus, a significance test method is proposed for mining spatio-temporal co-occurrence patterns among urban crimes. Firstly, a spatio-temporal pattern reconstruction method is developed to construct the null model of independence by fitting the observed distribution characteristics of each feature. Then, the significance of candidate spatio-temporal co-occurrence patterns are tested based on the empirical distributions of co-occurrence prevalence of candidate patterns under the null model. Simulated datasets with predefined patterns are further used to verify the effectiveness of this method. In addition, the spatio-temporal co-occurrence patterns among 13 types of crimes of the city S in 2016 are identified at multiple analysis scales (i.e. spatio-temporal radius). Taking the pattern {disorderly conduct, motor vehicle theft, pickpocketing} as an example, the formation mechanisms of that pattern are deeply analyzed by combining with the spatial distributions of communal facilities. The result shows that: (1) statistically significant spatio-temporal co-occurrence patterns can be effectively detected by fully considering the effect of autocorrelation of each type of crime; (2) spatio-temporal co-occurrence patterns among crimes vary with the scales of analysis; and (3) spatio-temporal co-occurrence patterns usually happen among different crimes with similar artificial and social environment. © 2019, Research and Development Office of Wuhan University. All right reserved.
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页码：1883 / 1892
页数：9
相关论文
共 29 条
  • [1] Mccollister K.E., French M.T., Fang H., The Cost of Crime to Society: New Crime-Specific Estimates for Policy and Program Evaluation, Drug and Alcohol Dependence, 108, 1, pp. 98-109, (2010)
  • [2] Wang Z., Liu X., Lu J., Et al., Construction and Spatial-Temporal Analysis of Crime Network: A Case Study on Burglary, Geomatics and Information Science of Wuhan University, 43, 5, pp. 759-765, (2018)
  • [3] Wilcox P., Eck J.E., Criminology of the Unpopular, Criminology and Public Policy, 10, 2, pp. 473-482, (2011)
  • [4] Li W., Wen L., Chen Y., Property Crime Forecast Based on Improved GA-BP Neural Network Model, Geomatics and Information Science of Wuhan University, 42, 8, (2017)
  • [5] Yang Z., On Related Crime, Criminal Science, 46, 4, pp. 13-22, (2000)
  • [6] Crime Pattern Definitions for Tactical Analysis Standards, Methods, & Technology (SMT) Committee White Paper 2011-01, (2011)
  • [7] Chen K., Based on the Grey Related Analytical Method Doing Positive Research into the Group of Drug Crimes-Set the Judgment of Xiangtan City Yuhu District as a Sample, (2010)
  • [8] Li D., Wang S., Li D., Et al., Theories and Technologies of Spatial Data Mining and Knowledge Discovery, Geomatics and Information Science of Wuhan University, 27, 3, pp. 221-233, (2002)
  • [9] Li D., Wang S., Shi W., Et al., On Spatial Data Mining and Knowledge Discovery (SDMKD), Geomatics and Information Science of Wuhan University, 26, 6, pp. 491-499, (2001)
  • [10] Liu D., Chen H., Qi H., Et al., Advance in Spatiotemporal Data Mining, Journal of Computer Research and Development, 50, 2, pp. 225-239, (2013)
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