不同类型道路密度对公共空间盗窃犯罪率的影响&mdash;&mdash;基于ZG市的实证研究

doi:10.13959/j.issn.1003-2398.2017.06.004

PDF(2639 KB)

人文地理 ›› 2017, Vol. 32 ›› Issue (6) : 32-38,46. DOI: 10.13959/j.issn.1003-2398.2017.06.004

社会

不同类型道路密度对公共空间盗窃犯罪率的影响——基于ZG市的实证研究

柳林^1,2, 杜方叶¹, 肖露子¹, 宋广文¹, 刘凯¹, 姜超¹

作者信息 +

THE DENSITY OF VARIOUS ROAD TYPES AND LARCENY RATE: AN EMPIRICAL ANALYSIS OF ZG CITY

LIU Lin^1,2, DU Fang-ye¹, XIAO Lu-zi¹, SONG Guang-wen¹, LIU Kai¹, JIANG Chao¹

Author information +

文章历史 +

摘要

道路密度对犯罪分布存在影响已得到大多数学者的证实，但忽略了不同类型道路属性的差异对犯罪的影响。不同类型道路在社会—建成环境等各种属性方面存在较大的差异，因此明确不同类型道路密度对公共空间盗窃犯罪率存在的影响有助于犯罪的防控。基于此，本文以派出所为单元构建多元线性回归模型进行研究。研究发现，不同类型道路密度对公共空间盗窃犯罪率影响不同：城市次干道、城市支路和其他可通车道路密度对公共空间盗窃犯罪率有正向影响；不可通车道路密度对公共空间盗窃犯罪率有负向影响；城市主干道密度对公共空间盗窃犯罪率影响不显著。不同类型道路社会—建成环境的差异是公共空间盗窃犯罪率不同的原因。研究结果可为犯罪精准防控提供指导。

Abstract

Spatial crime patterns have found to be related to street network, yet previous studies have not distinguished the heterogeneous influences of different types of road. Different social and physical environments are attached with different types of road, and they may impose different impacts on the occurrence of crime events. Of all the crime types, larceny is one of the most frequent crimes in the ZG city, and is closely associated with citizens' routine activities and their behavioral environments. Based on the routine activity theory, social disorder theory, crime pattern theory, and rational choice theory, this paper examines the impacts of different types of road on the spatial distribution of larceny with social-physical features taken as control variables. For the analysis, 91990 records of larceny are extracted from the call for services data in the ZG city police department, while the sixth census and points of interest from commercial navigation data sets are collected to represent the social-physical environment of the ZG city. Multiple linear regressions are used to quantitatively test the theoretical expectations. The regression results have confirmed the theoretical expectation that the densities of different types of road have diverse influences on the spatial larceny pattern. The densities of the secondary road, branch road, other vehicle-passable road, and pedestrian road are significantly associated with the larceny rate.

导出引用

柳林, 杜方叶, 肖露子, 宋广文, 刘凯, 姜超. 不同类型道路密度对公共空间盗窃犯罪率的影响——基于ZG市的实证研究[J]. 人文地理. 2017, 32(6): 32-38,46 https://doi.org/10.13959/j.issn.1003-2398.2017.06.004

LIU Lin, DU Fang-ye, XIAO Lu-zi, SONG Guang-wen, LIU Kai, JIANG Chao. THE DENSITY OF VARIOUS ROAD TYPES AND LARCENY RATE: AN EMPIRICAL ANALYSIS OF ZG CITY[J]. HUMAN GEOGRAPHY. 2017, 32(6): 32-38,46 https://doi.org/10.13959/j.issn.1003-2398.2017.06.004

中图分类号： C912

参考文献

[1] 颜峻,疏学明,袁宏永.盗窃犯罪空间分布与地理因素的关联[J].清华大学学报(自然科学版),2010,50(2):174-176.[Yan Jun, Shu Xueming, Yuan Hongyong. Relationship between spatial distribution of thief crime and geographical factors[J]. Journal of Tsinghau University(Sci& Tech), 2010,50(2):174-176.]

[2] 刘大千,宋伟,修春亮.长春市"两抢两盗"犯罪的空间分析[J].地理科学,2014,34(11):1344-1352.[Liu Daqian, Song Wei, Xiu Chunliang. Spatial analysis on robbery, forcible seizure, vehicle theft and burglary in Changchun[J]. Scientia Geographica Sinica, 2014,34(11):1344-1352.]

[3] Davies T, Johnson S D. Examining the relationship between road structure and burglary risk via quantitative network analysis[J]. Journal of Quantitative Criminology, 2015,31(3):481-507.

[4] Johnson S D, Bowers K J. Permeability and burglary risk:Are culde-sacs safer[J]. Journal of Quantitative Criminology, 2010,26(1):89-111.

[5] Sohn D. Residential crimes and neighbourhood built environment:Assessing the effectiveness of crime prevention through environmental design(CPTED)[J]. Cities, 2016,52(1):86-93.

[6] Hillier B. Can streets be made safe[J]. Urban Design International, 2004,9(1):31-45.

[7] Brantingham P L, Brantingham P J. Nodes, paths and edges:Considerations on the complexity of crime and the physical environment[J]. Journal of Environmental Psychology, 1993,13(1):3-28.

[8] Cozens P M. New urbanism, crime and the suburbs:A review of the evidence[J]. Urban Policy & Research, 2008,26(4):429-444.

[9] Maha J, Malaika, Hamid H, et al. Achieving the efficient distribution of police stations and rescue police points in Duhok city/Iraq by using (GIS)[J]. Journal of Information Engineering & Applications, 2014,4(4):1-13.

[10] 毛媛媛,戴慎志.犯罪空间分布与环境特征——以上海市为例[J]. 城市规划学刊,2006(3):85-93.[Mao Yuanyuan, Dai Shenzhi. Research on spatial and environment characters of crime:Case study of shanghai[J]. Urban Planning Forum, 2006(3):85-93.]

[11] Armitage R. Predicting and preventing:Developing a risk assessment mechanism for residential housing[J]. Crime Prevention & Community Safety, 2006,8(3):137-149.

[12] Armitage R. The Impact of Connectivity and Through-Movement within Residential Developments on Levels of Crime and Anti-social Behavior[R]. London:University of Huddersfield, 2012:5-10.

[13] Li Q, Fan H, Luan X, et al. Polygon-based approach for extracting multilane roads from OpenStreetMap urban road networks[J]. International Journal of Geographical Information Science, 2014,28(11):2200-2219.

[14] White G. Neighborhood permeability and burglary rates[J]. Justice Quarterly, 1990,7(1):57-67.

[15] Paul J, Brantingham P L. Environmental Criminology[M]. California:Sage Publications, 1981:264.

[16] Shu C F. Housing layout and crime vulnerability[J]. Urban Design International, 2000,5(3):177-188.

[17] Wu L, Liu X,Ye X, et al. Permeability, space syntax, and the patterning of residential burglaries in urban China[J]. Applied Geography, 2015,60(1):261-265.

[18] Matthews S A, Yang T C, Hayslettmccall K L, et al. Built environment and property crime in Seattle, 1998-2000:A bayesian analysis[J]. Environment & Planning A, 2010,42(6):1403-1420.

[19] Browning C R, Byron R A, Calder C A, et al. Commercial density, residential concentration, and crime:Land use patterns and violence in neighborhood context[J]. Journal of Research in Crime and Delinquency, 2010,47(3):329-357.

[20] 简·雅各布斯.美国城市的死与生[M].金衡山,译.南京:译林出版社,2006:26-47.[Jacobs J. The Death and Life of Great American Cities[M]. Jin Hengshan, trans. Nanjing:Yilin Press, 2006:26-46.]

[21] Hipp J R, Wickes R. Violence in urban neighborhoods:A longitudinal study of collective efficacy and violent crime[J]. Journal of Quantitative Criminology, 2016,32(1):1-26.

[22] Armstrong T A, Katz C M, Schnebly S M. The relationship between citizen perceptions of collective efficacy and neighborhood violent crime[J]. Crime & Delinquency, 2010,61(1):121-142.

[23] Cahill M E, Mulligan G F. The determinants of crime in Tucson, Arizona[J]. Urban Geography, 2003,24(7):582-610.

[24] Morenoff J D, Sampson R J, Raudenbush S W. Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence[J]. Criminology, 2001,39(3):517.

[25] Shaw C R, Mckay H D. Juvenile Delinquency and Urban Areas[M]. Chicago:University of Chicago Press, 1942:17-42.

[26] Anderson J M, Macdonald J M, Bluthenthal R, et al. Reducing crime by shaping the built environment with zoning:An empirical study of Los Angeles[J]. University of Pennsylvania Law Review, 2013,161(3):699-756.

[27] Felson M, Cohen L E. Human ecology and crime:A routine activity approach[J]. Human Ecology, 1980,8(4):389-406.

[28] Karstedt S, Clarke R V, Felson M. Advances in criminological theory[J]. Contemporary Sociology, 1994,23(6):1-14.

[29] Cervero R, Sarmiento O L, Jacoby E, et al. Influences of built environments on walking and cycling:Lessons from Bogotá[J]. International Journal of Sustainable Transportation, 2009,3(4):203-226.

[30] Wortley R, Mcfarlane M. The role of territoriality in crime prevention:A field experiment[J]. Security Journal, 2011,24(2):149-156.

[31] 王发曾.城市空间环境对城市犯罪的影响[J].人文地理,2001,16(2):1-6.[Wang Fazeng. The urban spatial environment's influences on urban crime[J]. Human Geography, 2001,16(2):1-6.]

[32] Zhang W Y, Wei Z W, Wang B H, et al. Measuring mixing patterns in complex networks by Spearman rank correlation coefficient[J]. Physica A:Statistical Mechanics and its Applications, 2016,451(1):440-450.

[33] 张景阳,潘光友.多元线性回归与BP神经网络预测模型对比与运用研究[J].昆明理工大学学报(自然科学版),2013,38(6):61-67.[Zhang Jingyang, Pan Guangyou. Comparison and application of multiple regression and BP neural network prediction model[J]. Journal of Kunming University of Science and Technology(Natural Science Edition), 2013,38(6):61-67.]

[34] 高建,周丽萍.基于Box-Cox变换的住宅特征价格理论研究[J].河北科技大学学报,2007,28(3):247-250.[Gao Jian, Zhou Liping. Study on housing hedonic price theory based on Box-Cox transformation[J]. Journal of Hebei University of Science and Technology, 2007,28(3):247-250.]

[35] 左文鼎,夏祥谦,王力宾.Box-Cox变换在Matlab上的实现——以中国汽车特征价格模型为例[J].云南财经大学学报,2007,23(6):116-120.[Zuo Wending, Xia Xiangqian. Box-Cox transformation and its implementation by Matlab-application in Chinas automobile hedonic price model[J]. Journal of Yunnan University of Finance and Economics, 2007,23(6):116-120.]

[36] 王惠文,孟洁.多元线性回归的预测建模方法[J].北京航空航天大学学报,2007,33(4):500-504.[Wang Huiwen, Meng Jie. Predictive modeling on multivariate linear regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2007,33(4):500-504.]

[37] Reid A A, Frank R, Iwanski N, et al. Uncovering the spatial patterning of crimes:A criminal movement model(CriMM)[J]. Journal of Research in Crime and Delinquency, 2014,51(2):230-255.