| 犯罪防控警务策略及其时空效益评估研究进展 |
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| 引用本文： | 柳林,吴雨菡,宋广文,肖露子.犯罪防控警务策略及其时空效益评估研究进展[J].地球信息科学,2021,23(1):29-42. |
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| 作者姓名： | 柳林 吴雨菡 宋广文 肖露子 |
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| 作者单位： | 1.广州大学地理科学与遥感学院公共安全地理信息分析中心,广州 5100062.辛辛那提大学地理系,辛辛那提 OH45221-0131,美国 |
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| 基金项目： | 国家自然科学基金重点项目;国家重点研发计划项目;广州市科技计划项目;国家自然科学基金项目;广东省自然科学基金研究团队项目;广东省自然科学基金项目 |
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| 摘 要： | 犯罪防控警务策略是犯罪地理研究的重要话题之一,西方国家在此方面展开了大量研究,而我国的相关研究仍较缺乏。本文从不同类型犯罪防控警务策略特点、犯罪防控实验及其防控效益评估3个方面出发,综述国内外研究进展。研究结论包括以下4个方面：① 社区警务、问题导向警务、热点警务和智能警务为4大主要犯罪防控警务策略。社区警务以社区为单位,通过警民合作以减少犯罪;问题导向警务通过SARA（Scanning, Analysis, Response, Assessment）找出并解决社会中存在的问题;热点警务基于犯罪热点制定警务方案,对犯罪热点地区加以干预以降低犯罪率;智能警务利用大数据,人工智能等先进技术开展警务防控;② 国外的犯罪防控实验大多是通过划分实验组和对照组的方式,对比警务策略干预前后的犯罪变化。大部分警务实验以热点警务为主,且在试验区能有效降低犯罪,但同时存在犯罪转移或效益扩散现象;国内犯罪防控实验的研究仍有待开展;③ 传统的警务防控评估主要从破案率、公众安全感和社会经济效益评估3方面出发。鉴于犯罪转移或效益扩散的存在,国内外学者提出了加权位移商（WDQ）、时空加权位移商（STWDQ）、双重差分（DID）、倾向评分匹配（PSM）、双重差分倾向评分匹配（PSM-DID）和基于双重差分（DID）的象限法评估警务时空效益,丰富了警务防控评估体系。整体而言,国外对犯罪防控警务策略与时空效益评估研究得较充分但仍存在不足,而国内尚处于起步阶段。本文在最后探讨了中国警务策略可深入研究的方向。
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| 关 键 词： | 犯罪防控 警务策略 警务实验 热点警务 时空效益评估 犯罪转移 效益扩散 加权位移商 双重差分模型 |
| 收稿时间： | 2020-08-22 |
Research Progress on Policing Strategy of Crime Prevention and the Evaluation of Its Effectiveness in Space and Time |
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| LIU Lin,WU Yuhan,SONG Guangwen,XIAO Luzi.Research Progress on Policing Strategy of Crime Prevention and the Evaluation of Its Effectiveness in Space and Time[J].Geo-information Science,2021,23(1):29-42. |
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| Authors: | LIU Lin WU Yuhan SONG Guangwen XIAO Luzi |
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| Institution: | 1. Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China2. Department of Geography, University of Cincinnati, Cincinnati OH45221-0131, USA |
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| Abstract: | Policing strategy of crime prevention is one of the important topics in crime geography.This paper systematically reviews the development and progress of policing strategy from three aspects:the characteristics of different types of crime prevention policing strategies,crime prevention experiments and the evaluation of crime prevention effectiveness.Our research shows that:①Community policing,problem-oriented policing,hot spots policing and intelligent policing are the four main types of crime prevention policing strategies.Community policing takes the community as a unit and reduces crime through cooperation between the police and the local residents;Problem-oriented policing aims at identifying and solving social problems through a structured approach named SARA(Scanning,Analysis,Response,Assessment).Hot spots policing makes policing plan based on crime hot spots,and intervenes crime hot spots to reduce crime.Intelligent policing uses advanced technologies such as big data and artificial intelligence for more effective policing prevention.②Crime prevention experiments compare the changes of crime before and after the intervention of police strategy between the experimental group and the control group.Most policing experiments are mainly focused on hot spots policing,which typically reduces crime in the experimental area,but may displace crime or diffuse the benefits of crime reduction to the neighboring areas;③Traditional police prevention evaluation mainly considers three aspects:detection rate,public security perception and social and economic benefit.To evaluate the effect of crime displacement or benefits diffusion,scholars put forward Weighted Displacement Quotient(WDQ),Spatio-Temporal Weighted Displacement Quotient(STWDQ),Difference in Differences(DID),Propensity Score Matching(PSM),integration of DID and PSM(PSM-DID)and DID-based quadrant method to measure the space-time benefits of policing strategies.In sum,while there exist abundant international research on policing strategy about crime prevention and evaluation of their space-time benefit such research in China still remain in its infancy.In the end,this paper further discusses the direction of future research on China’s policing strategy. |
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| Keywords: | crime prevention policing strategy policing experiment hot spots policing evaluation of spatiotemporal effectiveness crime displacement benefits diffusion weighted displacement quotient difference-in-differences model |
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