基于案事件流数据的犯罪行为时空分析研究

项目名称： 基于案事件流数据的犯罪行为时空分析研究

项目编号： No.41471372

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 天文学、地球科学

项目作者： 张宏

作者单位： 南京师范大学

项目金额： 80万元

中文摘要： 在犯罪动态化、智能化日趋明显，新型犯罪行为层出不穷的背景下，促使警务模式由被动的关注具体罪犯行为向主动的犯罪情报分析、犯罪预测与防控转变，迫切需要解决有数据，无情报的问题，构建数据-信息-知识-情报一体的信息链。本项目以警情监测和社会面防控系统中案事件流数据的实时分析为切入点，拟通过时空分析流计算模型的研究，建立对案事件流数据的实时分析计算框架。为有针对性的压降警情，研究基于流计算的案事件流数据聚类分析，优化流计算中的时空自相关算法，通过警情与防控的时空结构比对，实现警力跟着警情走。针对犯罪侦破，研究基于案事件流数据的多轨关联分析模型, 优化流计算中的轨迹串并,频繁项集等的计算,通过轨迹历时和共时特征的分析，实现人案关联。本项目面向案事件流数据的分析挖掘，所构建的流计算模型和多轨关联分析方法，可望在犯罪地理与时间地理研究的理论与方法上有所创新。

中文关键词： 犯罪时空分析；流计算；事件流；轨迹分析；时空聚类

英文摘要： In the context of dynamic and intelligent crime becoming increasingly prevalent, new types of crime emerges continuously. Consequently, policing mode are prompted from passive focused on specific criminal behavior to proactive criminal information analysis, crime prediction, crime prevention and control to get precautions. This challenge urgently needs to address the problem of 'mass data, no intelligence' by constructing integrated information chain of data-information-knowledge-intelligence. This research aims to establish the Real-time analysis framework for real time analysis of Case and Event Stream Data (CESD) based on spatio-temporal analysis of stream computing models in the alert-situation monitoring and social prevention and control system. This project focuses on two specific contents. One is to reduce the occurrence of criminal activities. The proposal project focuses on make different clustering analysis of CESD based on stream computing, optimize spatio-temporal autocorrelation algorithms in the stream computing, and compare space-time structures between alter-situation and police strength for reasonably deploying police. The other is to crime detective, the research intends to build multi-trajectory correlation analysis models based on CESD, optimize algorithms of trajectories connection and frequent item set computation, and analyze serial and synchronic features of trajectories to find the relevance between the person and a case. What the research propose here are to extend and develop these approaches on CESD analytical mining, stream computing models construction and multi-trajectory correlation analysis algorithms. The research findings are expected to bring theory and methodology innovations in both criminal geography and time geography.

英文关键词： Crime Spatio-temporal Analysis;Stream Computing;Event Stream;Trajectory Analysis;Spatio-temporal Clustering