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| THE DENSITY OF VARIOUS ROAD TYPES AND LARCENY RATE: AN EMPIRICAL ANALYSIS OF ZG CITY |
| LIU Lin1,2, DU Fang-ye1, XIAO Lu-zi1, SONG Guang-wen1, LIU Kai1, JIANG Chao1 |
1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
2. Department of Geography, University of Cincinnati, Cincinnati OH 45221-0131, USA |
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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.
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Received: 05 September 2016
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|
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|
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|
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|
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|
| [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.
|
| [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.]
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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.
|
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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.]
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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.]
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2017, Vol. 32 
