Analysis of the Spatiotemporal Distribution and Influencing Factors of Victims with Different Characteristics in Theft Crime: A Case Study of Baoan District of Shenzhen City
DOI:
https://doi.org/10.54691/q9485s88Keywords:
Victim Characteristics; Theft Crime; Poisson Regression; Influencing Factors.Abstract
This study takes Bao'an District, Shenzhen as the study area and analyzes the environmental factors affecting the distribution of theft crime victims among residents with different characteristics from the perspectives of built environment and social environment variables. The research results indicate that variables such as the proportion of juveniles, the proportion of the floating population, scientific and cultural facilities, and road network density all have a certain impact on the victimization of residents with different characteristics. Based on these findings, the paper proposes targeted prevention and control measures, including strengthening security patrols in areas frequently visited by women, paying attention to areas where non-local residents congregate, enhancing education and management for juveniles, improving community policing, increasing surveillance in key areas, and conducting safety awareness campaigns, to reduce the risk of theft crimes and protect the lives and property of the public.
Downloads
References
[1] Cornish D B and Clarke R V G.1986. The Reasoning Criminal: Rational Choice Perspectives on Offending. New York: Springer-Verlag.
[2] Xiao Luzi, Liu Lin and Song Guangwen. 2017. Impacts of Community Environment on Residential Burglary Based on Rational Choice Theory[J]. Geography Research, 36 (12): 2479-2491.
[3] Chandan Harish C. 2023.Implications of Marginalization and Critical Race Theory on Social Justice. IGI Global.
[4] Jiang Mingjun, Zhang Xinzhi and Hu Junmei. 2014. Research on the Psychology of Victims of Telecom Fraud[J]. China Judicial Appraisal, (04): 45-47.
[5] Zhang Yaowen and Luo Wenhua. 2023. On Characteristics of Victims in Telecommunication Network Frauds:Comparative Analysis Based on Data from Different Regions[J]. Journal of Shanxi Police College, 31 (01): 80-87.
[6] Qu Jia.2017.Research on the Characteristics and Laws of Street Picking Crime - From the Three Dimensions of Space, Time, and Victims [J]. Journal of Public Security (Journal of Zhejiang Police College), (06): 93-98+102.
[7] Powers R A and Socia K M. (2019). Racial Animosity, Adversary Effect, and Hate Crime: Parsing Out Injuries in Intraracial, Interracial, and Race-Based Offenses. Crime & Delinquency, 65(4):447-473.
[8] Caines Matthew and Wyatt Brown. 2023.Victim and Offender Race and the Likelihood of Weapon Use: a Test of Racial Animosity and Racial Threat Theories. Criminal Justice Studies ,36.2 : 184-201.
[9] Rosenblatt M.Remarks on Some Nonparametric Estimates of a Density Function [J]. The annals of mathematical statistics, 1956:27 (3): 832 - 837.
[10] Zhang Xinyu and Chen Peng .2023.The Impact of COVID-19 Prevention and Control Measures on Residential Burglary Hotspots:A Case Study of The Core Urban Areas of Beijing[J]. Progress in Geographic Science, 42 (02): 328-40.
[11] Liu Lin, Sun Qiuyuan and Xiao Luzi. 2021. Time Differences in the Impact of Daily Activities of Drug-Related Personnel on The Spatial Pattern of Theft Incidents [J]. Journal of Earth Information Science, 23 (12): 2187-2000.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Scientific Journal Of Humanities and Social Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.





