A Hot Spot Detection Method of Criminal Events Under Data Uncertainty

被引：2

作者：

Liu, Enbo ^{[1
]}

Chen, Kaiqi ^{[1
]}

Shi, Yan ^{[1
]}

Deng, Min ^{[1
]}

机构：

[1] School of Geosciences and Info-physic, Central South University, Changsha

来源：

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2024年 / 49卷 / 12期

关键词：

crime hot spot; incomplete data; uncertainty;

D O I：

10.13203/j.whugis20220100

中图分类号：

学科分类号：

摘要：

Objectives: Accurate crime hot spot 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 and they may get unreliable hot spot detection results when applied to practical crime datasets. 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. In terms of space, 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 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 proposed method can detect stable and reasonable hot spot results in the face of criminal events with different uncertainty strengths. Conclusions: The hot spots detected by the proposed 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. © 2024 Editorial Department of Geomatics and Information Science of Wuhan University. All rights reserved.

引用

页码：2342 / 2354

页数：12

共 36 条

[1]

Fazeng Wang, Urban Crime Analysis and Space Prevention and Control［M］, (2003)

[2]

Weixiu Li, Research on Crime Trend and Its Influencing Factors After China - s Reform and Opening up ［J］, Modern Business Trade Industry, 41, 9, pp. 155-156, (2020)

[3]

Hu Y J, Wang F H，, Guin C，, Et al., A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation［J］, Applied Geography, 99, pp. 89-97, (2018)

[4]

Zhang L N，, Messner S F，, Zhang S., Neighborhood Social Control and Perceptions of Crime and Disorder in Contemporary Urban China［J］, Criminology, 55, 3, pp. 631-663, (2017)

[5]

Eck J E，, Chainey S，, Cameron J G，, Et al., Mapping Crime：Understanding Hot Spots［R］, (2005)

[6]

Juan Lu, Guoan Tang, Hong Zhang, Et al., A Review of Research Methods for Spatiotemporal Distribution of the Crime Hot Spots［J］, Progress in Geography, 31, 4, pp. 419-425, (2012)

[7]

Pezzuchi G., A Brief Commentary on“The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime”［J］, SecurityJournal, 21, 4, pp. 291-292, (2008)

[8]

Chong Xu, Lin Liu, Suhong Zhou, Et al., The Spatio-Temporal Patterns of Street Robbery in DP Peninsula［J］, Acta Geographica Sinica, 68, 12, pp. 1714-1723, (2013)

[9]

Ruijun Zhu, Research of Spatial-Temporal Clustering for Case Data Based on the Public Security Big-Data ［D］, (2017)

[10]

Catlett C，, Cesario E，, Talia D，, Et al., Spatio-Temporal Crime Predictions in Smart Cities：A Data-Driven Approach and Experiments［J］, Pervasive and Mobile Computing, 53, pp. 62-74, (2019)

← 1 2 3 4 →