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
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TP391.75

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Semantic mining of crime hotspot considering multi-dimensional case characteristics: A case study of burglary in Beijing
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    摘要:

    犯罪热点能够表征犯罪活动在空间上的分布特征模式与聚集规律。目前关于犯罪热点已开展有大量研究,但大多关注于犯罪热点的分布模式及特定热点的成因解释,尚缺少从语义结构特征的角度对不同犯罪热点进行挖掘分析。对此,本文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。本文以北京市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。

    Abstract:

    Crime hotspot can represent the characteristic and aggregation pattern of crime in spatial distribution. At present, a lot of researches have been carried out on crime hotspot, but most of them focused on the clusters identification and formation analysis to the hotspot, lacking of the analysis of different hotspots from the perspective of semantic. Therefore, we proposes a semantic mining method of crime hotspots, which takes into account the multi-dimensional characteristics of the case. By constructing the multi-dimensional label system and matching the labels and hotspots, the hotspots are mined in different dimensions from the semantic point of view. Using this method, we take the burglary cases in Beijing for example to conduct an empirical analysis experiment. From the result, the method can conduct a more in-depth exploration of crime hotspots and obtain more refined hotspot features.

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郭雅琦,陈鹏,朱冠宇,等. 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[J]. 科学技术与工程, 2022, 22(9): 3620-3628.
Guo Yaqi, Chen Peng, Zhu Guanyu, et al. Semantic mining of crime hotspot considering multi-dimensional case characteristics: A case study of burglary in Beijing[J]. Science Technology and Engineering,2022,22(9):3620-3628.

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历史
  • 收稿日期:2021-08-17
  • 最后修改日期:2022-01-03
  • 录用日期:2021-11-10
  • 在线发布日期: 2022-03-25
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