基于理性选择理论的社区环境对入室盗窃的影响研究

doi:10.11821/dlyj201712017

地理研究 ›› 2017, Vol. 36 ›› Issue (12): 2479-2491.doi: 10.11821/dlyj201712017

基于理性选择理论的社区环境对入室盗窃的影响研究

肖露子^1,²(), 柳林^3,⁴(), 宋广文^1,², 周素红^1,², 龙冬平^1,², 冯嘉欣^1,²

1. 中山大学地理科学与规划学院综合地理信息研究中心,广州 510275
2. 广东省城市化与地理环境空间模拟重点实验室,广州 510275
3. 广州大学地理科学学院公共安全地理信息分析中心,广州 510006
4. 辛辛那提大学地理系,辛辛那提 OH45221-0131 美国

收稿日期:2017-06-11 修回日期:2017-09-21 出版日期:2017-12-15 发布日期:2017-12-15
作者简介:
作者简介：肖露子（1991- ）,女,江西樟树人,博士研究生,研究方向为犯罪地理与时空行为分析。E-mail:xiaoluzi@mail2.sysu.edu.cn
基金资助:
国家自然科学基金重点项目（41531178）;广东省自然科学基金研究团队项目（2014A030312010）;国家自然科学基金项目（41522104）;广东省科技计划项目（2015A020217003）

Impacts of community environment on residential burglary based on rational choice theory

Luzi XIAO^1,²(), Lin LIU^3,⁴(), Guangwen SONG^1,², Suhong ZHOU^1,², Dongping LONG^1,², Jiaxin FENG^1,²

1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-Sen University, Guangzhou 510275, China
3. Center of Geographic Information Analysis for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, China
4. Department of Geography, University of Cincinnati, Cincinnati OH45221-0131, Ohio, USA

Received:2017-06-11 Revised:2017-09-21 Online:2017-12-15 Published:2017-12-15

摘要/Abstract

摘要：

入室盗窃影响人们的居家安全,已逐渐受到国内学者关注,但能结合中国国情,细化探讨人口特征及住房特征对入室盗窃影响的研究尚未多见。以东南沿海大城市ZG市226个派出所为例,采用2014年入室盗窃警情数据、人口普查数据及路网数据,重点从理性选择理论中的潜在收益和风险两大维度探讨人口特征及住房特征等社区环境对入室盗窃的影响。研究发现：人口特征方面,高学历知识分子因子对入室盗窃率起显著的负向作用,而青壮年外来务工人员因子对入室盗窃率呈现显著的正向作用;住房特征方面,普通商品房容易吸引犯罪者,成为作案目标,而原公有住房因子则能降低入室盗窃发生的风险。分析结果表明,不同住房类型所呈现的风险收益比影响入室盗窃的空间分布;入室盗窃犯罪分子对作案目标的理性选择倾向于遵循“安全第一、收益第二”的原则。研究结论可为犯罪防控和警务实践提供一定的参考。

关键词: 犯罪地理, 社区环境, 入室盗窃, 理性选择理论

Abstract:

Residential burglary is one of the common property crimes in China, which has attracted the attention from many scholars. Domestic literatures of residential burglary mainly focus on its spatial pattern and try to explain why some places suffer more crimes than other places do. Although some of them have explored how social environment and natural environment affect residential burglary, they failed to consider the complex characteristics of population and housing in the context of China's rapid urbanization. To address this, rational choice theory is applied to explain the relationship between community environment and residential burglary in ZG city, one of the biggest cities in the south of China. Using the data of residential burglary, census and road network from 226 police districts, a negative binomial regression model is estimated. Results show that both of the population characteristics and the housing characteristics have significant impacts on burglary. In terms of population characteristics, police districts with more highly educated intellectuals suffer less residential burglaries, while those with more young and middle-aged migrant workers suffer more. As for housing characteristics, police districts with more ordinary commercial residential buildings are easier to attract residential burglars, and those with more public-owned housing can effectively reduce the residential burglary rate. In addition to population and housing characteristics, the results also show that the density of road network has significant impact on residential burglary, while the density of bus lines has no effect on it. In conclusion, the balance of the perceived risk and reward of different environments would affect the spatial distribution of burglaries. Residential burglars roughly follow the rule of "safety first, benefit second" when committing crimes. This study can shed light on how population and housing characteristics influence the spatial pattern of residential burglary in China, and provide suggestions on crime prevention and control to the police.

Key words: criminal geography, community environment, burglary, rational choice theory

肖露子, 柳林, 宋广文, 周素红, 龙冬平, 冯嘉欣. 基于理性选择理论的社区环境对入室盗窃的影响研究[J]. 地理研究, 2017, 36(12): 2479-2491.

Luzi XIAO, Lin LIU, Guangwen SONG, Suhong ZHOU, Dongping LONG, Jiaxin FENG. Impacts of community environment on residential burglary based on rational choice theory[J]. GEOGRAPHICAL RESEARCH, 2017, 36(12): 2479-2491.

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参考文献 38

[1]	严小兵, 焦华富. 犯罪地理国际研究进展. 地理科学进展, 2012, 31(10): 1390-1398. doi: 10.11820/dlkxjz.2012.10.017
	[Yan Xiaobing, Jiao Huafu.A review on the foreign criminal geography research. Progress in Geography, 2012, 31(10): 1390-1398.] doi: 10.11820/dlkxjz.2012.10.017
[2]	Lebeau J L, Leitner M.Introduction: Progress in research on the geography of crime. The Professional Geographer, 2011, 63(PII 9337286922): 161-173. doi: 10.1080/00330124.2010.547147
[3]	姜超, 柳林. 中国犯罪地理与警务GIS研究进展. 地球信息科学学报, 2013, 15(6): 801-808. doi: 10.3724/SP.J.1047.2013.00801
	[Jiang Chao, Liu Lin.Research progress of crime geography studies in China. Journal of Geo-Information Science, 2013, 15(6): 801-808.] doi: 10.3724/SP.J.1047.2013.00801
[4]	姜超, 唐焕丽, 柳林. 中国犯罪地理研究述评. 地理科学进展, 2014, 33(4): 561-573. doi: 10.11820/dlkxjz.2014.04.013
	[Jiang Chao, Tang Huanli, Liu Lin.Review of crime geography in China. Progress in Geography, 2014, 33(4): 561-573.] doi: 10.11820/dlkxjz.2014.04.013
[5]	陈宏胜, 李志刚. 快速城镇化下中国大城市房权分异研究: 对广州的实证. 地理学报, 2014, 69(12): 1821-1832. doi: 10.11821/dlxb201412008
	[Chen Hongsheng, Li Zhigang.Tenure-based housing segregation under rapid urbanization in post-reform urban China: A case study of Guangzhou. Acta Geographica Sinica, 2014, 69(12): 1821-1832.] doi: 10.11821/dlxb201412008
[6]	Logan J R, Bian Y, Bian F.Housing inequality in urban China in the 1990s. International Journal of Urban and Regional Research, 1999, 23(1): 7-25. doi: 10.1111/1468-2427.00176
[7]	Li S M.Housing tenure and residential mobility in urban China a study of commodity housing development in Beijing and Guangzhou. Urban Affairs Review, 2003, 38(4): 510-534. doi: 10.1177/1078087402250360
[8]	Liu J H.Crime patterns during the market transition in China. British Journal of Criminology, 2005, 45(5): 613-633. doi: 10.1093/bjc/azi044
[9]	Ye X, Xu X, Lee J, et al.Space-time interaction of residential burglaries in Wuhan, China. Applied Geography, 2015, 60: 210-216. doi: 10.1016/j.apgeog.2014.11.022
[10]	Wortley R, Mazerolle L.Environmental Criminology and Crime Analysis. Cullompton: Willan Publishing, 2008.
[11]	Jeffery C R.Crime Prevention through Environmental Design. Beverly Hills: Sage Publications, 1971. doi: 10.1007/978-1-4614-5690-2_550
[12]	Shaw C R, Mckay H D.Juvenile Delinquency And Urban Areas. Chicago: University of Chicago Press, 1942.
[13]	Cohen L E, Felson M.Social change and crime rate trends: A routine activity approach. American Sociological Review, 1979, 44(4): 588-608. doi: 10.2307/2094589
[14]	Cornish D B, Clarke R V.The Reasoning Criminal: Rational Choice Perspectives on Offending. New York: Springer-Verlag, 1986. doi: 10.1007/978-1-4613-8625-4
[15]	Loughran T A, Paternoster R, Chalfin A, et al.Can rational choice be considered a general theory of crime? Evidence from individual-level panel data. Criminology, 2016, 54(1): 86-112. doi: 10.1111/1745-9125.12097
[16]	Davies T, Johnson S D.Examining the relationship between road structure and burglary risk via quantitative network analysis. Journal of Quantitative Criminology, 2015, 31(3): 481-507. doi: 10.1007/s10940-014-9235-4
[17]	Wu L, Liu X, Ye X, et al.Permeability, space syntax, and the patterning of residential burglaries in urban China. Applied Geography, 2015, 60: 261-265. doi: 10.1016/j.apgeog.2014.12.001
[18]	Bernasco W, Nieuwbeerta P.How do residential burglars select target areas? A new approach to the analysis of criminal location choice. British Journal of Criminology, 2005, 45(3): 296-315. doi: 10.1093/bjc/azh070
[19]	Nee C, Taylor M.Examining burglars' target selection: Interview, experiment or ethnomethodology?. Psychology, Crime & Law, 2000, 6(1): 45-59. doi: 10.1080/10683160008410831
[20]	Jiang S, Lambert E, Wang J.Correlates of formal and informal social/crime control in China: An exploratory study. Journal of Criminal Justice, 2007, 35(3): 261-271. doi: 10.1016/j.jcrimjus.2007.03.003
[21]	Sampson R J, Groves W B.Community structure and crime: Testing social-disorganization theory. American Journal of Sociology, 1989, 94(4): 774-802. doi: 10.1086/229068
[22]	Vandeviver C, Neutens T, Van Daele S, et al.A discrete spatial choice model of burglary target selection at the house-level. Applied Geography, 2015, 64: 24-34. doi: 10.1016/j.apgeog.2015.08.004
[23]	Bernasco W, Luykx F.Effects of attractiveness, opportunity and accessibility to burglars on residential burglary rates of urban neighborhoods. Criminology, 2003, 41(3): 981-1001. doi: 10.1111/j.1745-9125.2003.tb01011.x
[24]	Wang Y P.Housing reform and its impacts on the urban poor in China. Housing Studies, 2000, 15(6): 845-864. doi: 10.1080/02673030020002573
[25]	陈果. 基于广州实证的后改革时代中国城市住房权问题. 地理学报, 2015, 70(12): 1973-1986. doi: 10.11821/dlxb201512009
	[Chen Guo.Housing rights in post-reform urban China: A case study of housing differentiation and justice in Guangzhou, China. Acta Geographica Sinica, 2015, 70(12): 1973-1986.] doi: 10.11821/dlxb201512009
[26]	Jiang S, Wang J, Lambert E.Correlates of informal social control in Guangzhou, China neighborhoods. Journal of Criminal Justice, 2010, 38(4): 460-469. doi: 10.1016/j.jcrimjus.2010.04.015
[27]	杨刚斌, 柳林, 何深静, 等. 基于多层次模型的广州门禁小区家庭入室盗窃环境影响分析. 热带地理, 2016, 36(4): 610-618. doi: 10.13284/j.cnki.rddl.002860
	[Yang Gangbin, Liu Lin, He Shenjing, et al.Environmental impacts on burglary victimization in gated communities: A multi-level analysis in Guangzhou. Tropical Geography, 2016, 36(4): 610-618.] doi: 10.13284/j.cnki.rddl.002860
[28]	古杰, 周素红, 闫小培. 基于家庭的广州市入室盗窃的特征分析. 人文地理, 2016, 31(5): 87-92.
	[Gu Jie, Zhou Suhong, Yan Xiaopei.Research on characteristics of urban burglary based on family in Guangzhou. Human Geography, 2016, 31(5): 87-92.]
[29]	杨刚斌, 柳林, 何深静, 等.广州门禁小区入室盗窃受害率与内部环境分析. 人文地理. 2016, 31(3): 45-51.
	[Yang Gangbin, Liu Lin, He Shenjing, et al.Community environmental factor analysis of burglary victimization in gated community in Guangzhou. Human Geography, 2016, 31(3): 45-51.]
[30]	陈刚, 李树, 陈屹立. 人口流动对犯罪率的影响研究. 中国人口科学, 2009, (4): 52-61.
	[Chen Gang, Li Shu, Chen Yili.Population mobility and crime: An empirical analysis based on China's observation. Chinese Journal of Population Science, 2009, (4): 52-61.]
[31]	龙冬平, 柳林, 冯嘉欣, 等. 社区环境对入室盗窃和室外盗窃影响的对比分析: 以ZG市ZH半岛为例. 地理学报, 2017, 72(2): 341-355. doi: 10.11821/dlxb201702013
	[Long Dongping, Liu Lin, Feng Jiaxin, et al.Comparisons of the community environment effects on burglary and outdoor-theft: A case study of ZH peninsula in ZG city. Acta Geographica Sinica, 2017, 72(2): 341-355.] doi: 10.11821/dlxb201702013
[32]	宋广文, 肖露子, 周素红, 等.居民日常活动对扒窃警情时空格局的影响. 地理学报. 2017, 72(2): 356-367. doi: 10.11821/dlxb201702014
	[Song Guangwen, Xiao Luzi, Zhou Suhong, et al.Impact of residents' routine activities on the spatial-temporal pattern of theft from person. Acta Geographica Sinica, 2017, 72(2): 356-367.] doi: 10.11821/dlxb201702014
[33]	Boessen A, Hipp J R.Close-ups and the scale of ecology: Land uses and the geography of social context and crime. Criminology, 2015, 53(3): 399-426. doi: 10.1111/1745-9125.12074
[34]	Berk R, Macdonald J M.Overdispersion and Poisson regression. Journal of Quantitative Criminology, 2008, 24(3): 269-284. doi: 10.1007/s10940-008-9048-4
[35]	Land K C, Mccall P L, Nagin D S.A comparison of Poisson, negative binomial, and semiparametric mixed Poisson regression models with empirical applications to criminal careers data. Sociological Methods & Research. 1996, 24(4): 387-442.
[36]	Osgood D W.Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology. 2000, 16(1): 21-43. doi: 10.1023/A:1007521427059
[37]	Hipp J R.Block, tract, and levels of aggregation: Neighborhood structure and crime and disorder as a case in point. American Sociological Review, 2007, 72(5): 659-680. doi: 10.1177/000312240707200501
[38]	王增利, 刘学军, 陆娟.入室盗窃多尺度地理因子分析. 地理学报. 2017, 72(2): 329-340.
	[Wang Zengli, Liu Xuejun, Lu Juan.Multiscale geographic analysis of burglary. Acta Geographica Sinica, 2017, 72(2): 329-340.]

		因子1 高学历知识分子	因子2 青壮年外来务工人员	因子3 农业人员
年龄	19~30岁(%)	0.175	0.858	0.273
学历	初中及以下(%)	-0.821	-0.158	0.489
学历	大学专科及以上(%)	0.922	0.191	-0.205
户口	常住本地人口(%)	0.060	-0.922	0.234
	外来人口户口在本省其他县市(%)	0.328	0.814	-0.080
	外来人口户口登记为省外(%)	-0.456	0.808	-0.064
	农业人口(%)	-0.582	0.151	0.735
职业	国家机关、党群组织、企业、事业单位负责人(%)	0.578	0.062	-0.187
	专业技术人员(%)	0.761	-0.161	-0.494
	办事人员和有关人员(%)	0.482	-0.242	-0.699
	商业、服务业人员(%)	0.253	0.025	-0.741
	农、林、牧、渔、水利业生产人员(%)	-0.141	-0.437	0.842
	生产、运输设备操作人员及有关人员(%)	-0.678	0.578	0.145

		因子1 自建住房	因子2 普通商品房	因子3 高档商品房	因子4 原公有住房	因子5 保障性住房
住房面积	每户50 m²以下(%)	-0.956	-0.124	-0.123	-0.112	0.025
	每户50~80 m²(%)	-0.149	0.340	0.254	0.722	0.007
	每户80~140 m²(%)	0.838	0.285	0.054	-0.085	0.048
	每户平均面积(m²)	0.854	-0.223	-0.032	-0.246	-0.072
	每户平均房间数(间)	0.858	-0.329	-0.092	-0.141	-0.087
建筑层高	3层及以下(%)	0.479	-0.731	-0.393	-0.088	-0.119
	4~9层(%)	-0.583	0.697	0.077	0.131	0.068
	10层及以上楼房(%)	-0.025	0.400	0.778	-0.042	0.150
建筑年代	1979年以前(%)	-0.044	-0.361	0.013	0.728	0.047
建筑年代	2000年以后(%)	-0.170	-0.114	0.074	-0.888	0.018
房屋租金	租金500元以下(%)	-0.023	-0.404	-0.716	-0.365	0.134
	租金1000~2000元(%)	0.014	0.301	0.682	0.395	-0.181
	租金2000元以上(%)	0.062	0.047	0.884	-0.017	0.021
住房来源	自建住房(%)	0.597	-0.664	-0.360	-0.079	-0.117
	购买商品房(%)	0.233	0.812	0.292	-0.127	-0.043
	购买二手房(%)	0.052	0.750	0.257	0.301	-0.043
	购买原公有住房(%)	-0.211	0.262	0.446	0.681	-0.048
	购买经济适用房(%)	0.084	0.085	0.212	0.059	0.804
	租赁廉租房(%)	-0.162	-0.059	-0.274	-0.071	0.647
	租赁其他住房(%)	-0.895	0.055	-0.076	-0.306	-0.037

类别	变量/主成分		变量分析假设
类别	变量/主成分	潜在收益	风险	成本	理论总效应
可达性	道路网密度	-	-	可进入性强	增加
可达性	公交线路密度	-	-	可进入性强	增加
人口特征	高学历知识分子因子	目标价值较高	社会控制较强	-	无影响
人口特征	青壮年外来务工人员因子	目标价值较低	社会控制较弱	-	无影响
住房特征	自建住房因子	目标价值较低	安保设施较差	-	无影响
	普通商品房因子	目标价值较高	安保设施一般	-	增加
	高档商品房因子	目标价值很高	安保设施较好	-	无影响
	原公有住房因子	目标价值较低	安保设施一般,非正式社会控制较强	-	抑制
	保障性住房因子	目标价值较低	非正式社会控制较弱	-	无影响

	变量	平均值	方差	最小值	最大值
因变量	入室盗窃率	39.970	1040.127	0.000	211.719
可达性	道路网密度	8.020	39.753	0.000	30.215
可达性	公交线路密度	28.687	2338.786	0.000	686.456
人口特征	高学历知识分子因子	0	1	-2.127	4.153
人口特征	青壮年外来务工人员因子	0	1	-1.798	3.656
住房特征	自建住房因子	0	1	-2.559	1.685
	普通商品房因子	0	1	-2.038	3.175
	高档商品房因子	0	1	-1.695	7.035
	原公有住房因子	0	1	-3.762	3.068
	保障性住房因子	0	1	-1.565	5.810

	变量名	R	IRR	Robust Std. Err.	VIF
	常量	2.980^***	19.688	0.131
交通可达性	道路网密度	0.060^***	1.062	0.013	2.83
交通可达性	公交线路密度	0.003	1.003	0.004	1.07
人口特征	高学历知识分子因子	-0.419^***	0.658	0.043	1.71
人口特征	青壮年外来务工人员因子	0.283^***	1.327	0.047	2.55
住房特征	自建住房因子	0.107	1.113	0.059	2.20
	普通商品房因子	0.147^**	1.158	0.051	1.78
	高档商品房因子	-0.058	0.944	0.054	1.96
	原公有住房因子	-0.222^***	0.801	0.056	2.15
	保障性住房因子	-0.057	0.945	0.042	1.02
	alpha	0.320^***		0.061
	N	226

基于理性选择理论的社区环境对入室盗窃的影响研究

Impacts of community environment on residential burglary based on rational choice theory

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