刘恩博, 谌恺祺, 石岩, 邓敏. 数据不确定性下的犯罪事件热点探测方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220100
引用本文: 刘恩博, 谌恺祺, 石岩, 邓敏. 数据不确定性下的犯罪事件热点探测方法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220100
LIU Enbo, CHEN Kaiqi, SHI Yan, DENG Min. A Hot Spot Detection Method of Criminal Events Under Data Uncertainty[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220100
Citation: LIU Enbo, CHEN Kaiqi, SHI Yan, DENG Min. A Hot Spot Detection Method of Criminal Events Under Data Uncertainty[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220100

数据不确定性下的犯罪事件热点探测方法

A Hot Spot Detection Method of Criminal Events Under Data Uncertainty

  • 摘要: 精确的犯罪热点探测技术对于城市公共安全管理与警力部署有着重要的决策支持作用,但由于城市犯罪的偶发性与隐蔽性,犯罪行为通常无法被直接、准确的观测,导致记录的犯罪信息于时间、空间上存在不确定性。现有热点探测方法大多基于数据完备的假设,应用于真实犯罪数据集时,易产生不可信的热点探测结果。基于此,本文提出一种数据不确定性下的犯罪事件热点探测方法。时间上,基于概率表达思想与Aoristic分析,建模犯罪时序规律;空间上基于理性选择理论与日常活动理论,采用地理加权回归进行不完备先验概率的矫正;最终通过Expectation-Maximization(EM)算法提取犯罪热点,绘制出可信的热点分布图。基于中国某地级市实际警情数据进行实验验证,结果显示,本文方法在面对相异不确定性强度的犯罪事件时,均能探测出稳定且合理的热点结果。本文方法所探测的热点充分剔除了犯罪事件不确定性的影响,贴合犯罪学中的日常活动理论,可为警务防控部署提供精准的决策支持。

     

    Abstract: Objectives: Accurate crime hotspot detection technology plays an important role in decision-making support for urban public security management and police force deployment. However, due to the contingency and concealment of urban crime, criminal behavior is usually unable to be observed directly and accurately, resulting in the uncertainty of recorded crime information in time and space. Most of the existing hot spot detection methods are based on the assumption of complete data. When applied to real crime data sets, it is easy to produce unreliable hot spot detection results. Methods: Based on this, this paper proposes a crime hot spot detection method under data uncertainty. In terms of time, based on the idea of probability expression and Aoristic analysis, the time sequence law of crime is modeled; Spatially, based on rational choice theory and daily activity theory, geographic weighted regression is used to correct the incomplete prior probability. Finally, the crime hot spots are extracted by Expectation-Maximization (EM) algorithm to draw a credible hot spot distribution map. Results: The experimental verification is based on the policing alert data of a prefecture level city in China. The results show that the method in this paper can detect stable and reasonable hotspot results in the face of criminal events with different uncertainty strengths. Conclusions: The hot spots detected by this method sufficiently eliminate the impact of the uncertainty of criminal events, fit with the daily activity theory in criminology, and can provide accurate decision support for police prevention and control deployment.

     

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