摘要
采用时空同现模式分析方法挖掘多元犯罪事件之间的关联关系,可为犯罪事件防控问题提供科学指导。现有方法依赖人为设置的频繁度阈值,应用部门若缺乏先验知识则可能导致决策错误。因此,基于非参数统计思想,提出一种面向城市犯罪的时空同现模式显著性检验方法。首先通过重建每类犯罪事件的时空分布,构建多元犯罪事件分布独立的零模型;然后根据零模型下多元犯罪事件同现频率的试验分布,判别候选时空同现模式的显著性。最后设计具有预设模式的模拟数据实验验证该方法的有效性;在多个分析尺度(时空半径)下识别S市2016年13种犯罪事件间时空同现模式,并以时空同现模式{扰乱治安,盗窃电动自行车,扒窃}为例,结合公共设施空间分布,对该模式形成机理进行深入分析。结果表明:①该方法充分顾及了单元犯罪事件自相关特征的影响,能够有效识别具有统计特性的时空同现模式;②犯罪事件时空同现模式随分析尺度的变化而存在差异;③具有相似建成环境和社会环境的犯罪事件容易形成时空同现模式。
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 cooccurrence 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 cooccurrence 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.
作者
陈袁芳
蔡建南
刘启亮
邓敏
张雪英
CHEN Yuanfang;CAI Jiannan;LIU Qiliang;DENG Min;ZHANG Xueying(Department of Geo-informatics,Central South University,Changsha 410083,China;Key Laboratory of Virtual Geographic Environment,Nanjing Normal University,Ministry of Education,Nanjing 210023,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2019年第12期1883-1892,共10页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金（41730105）
国家重点研发计划（2016YFB0502303）
中南大学研究生自主探索创新项目（2017zzts346）
公安部科技强警基础工作专项（2016GABJC43）
江苏省高校自然科学研究重大项目（15KJA420002）~~
关键词
城市犯罪
时空同现模式
非参数统计
显著性检验
尺度依赖
urban crimes
spatio-temporal co-occurrence patterns
nonparametric statistics
significance test
scale dependence
