顾及案件多维特征的犯罪热点语义挖掘——以北京市入室盗窃案件为例  

Semantic Mining of Crime Hotspot Considering Multi-dimensional Case Characteristics:A Case Study of Burglary in Beijing

作　　者：郭雅琦 陈鹏[1] 朱冠宇 林艳[1] GUO Ya-qi;CHEN Peng;ZHU Guan-yu;LIN Yan(School for Informatics and Cyber Security, People's Public Security University of China, Beijing 100038, China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《科学技术与工程》2022年第9期3620-3628,共9页Science Technology and Engineering

基　　金：北京市自然科学基金(9192022)。

摘　　要：为了从语义结构特征的角度对不同犯罪热点进行挖掘分析,设计了一种顾及案件多维特征的犯罪热点语义挖掘方法,通过构建案件多维特征标签体系,实现犯罪热点内案件的标签匹配,并对案件语义特征标签构建共现网络,以获得犯罪热点内案件更为详细的特征。以北京市2018年入室盗窃案件为例,进行了相应的实证分析实验。结果表明,该方法能够对犯罪热点进行更为深入的挖掘,得到更为精细化的热点特征,对警务部门开展针对性的犯罪打击和防控有一定的实用价值。In order to analyze different crime hotspots from the perspective of semantic structure characteristics,a crime hotspot semantic mining method considering the multi-dimensional characteristics of offence was designed.By constructing multi-dimensional feature label framework of crime,the label matching in the crime hotspots was realized,and the crime semantic feature label was constructed into a co-occurrence network for achieving more detailed characteristics of offence in the crime hot spots.The recording of residential burglary in Beijing in 2018 was collected to carry out the empirical analysis and experiment.It is shown that the proposed method can help understand more about the crime hot spots and demonstrates practical value for the police department to carry out corresponding targeted crime prevention and suppression.

关 键 词：犯罪热点 案件特征 标签体系 语义挖掘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]