IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i6p934-d100343.html
   My bibliography  Save this article

Non-Homogeneous Diffusion of Residential Crime in Urban China

Author

Listed:
  • Yicheng Tang

    (State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing & Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Xinyan Zhu

    (State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing & Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Wei Guo

    (State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing & Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Xinyue Ye

    (Department of Geography, Kent State University, Kent, OH 42240, USA)

  • Tao Hu

    (State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing & Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Yaxin Fan

    (State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing & Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Faming Zhang

    (State Key Lab of Information Engineering in Surveying Mapping and Remote Sensing & Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

Abstract

The relationship between crime and urban environment has always been the focus of crime geography. Like diseases which can transmit and diffuse, crimes may also spread during a certain period of time and to a certain area by the near-repeat effect. Traditional near-repeat analysis focuses on the spatial spread of crimes to adjacent areas, with little regard to the displacement effect. Crime displacement refers to the relocation of criminal events as a result of policing efforts. If this phenomenon is neglected, the near-repeat analysis will tend not to obtain the overall spatial distribution pattern of criminal cases, leading to limited effectiveness of crime control. This paper presents a non-homogeneous diffusion model where crime spreads not only to spatially and temporally adjacent areas, but also to areas with similar environmental characteristics. By virtue of spatial constraints and environmental characteristics, the most vulnerable areas are identified, and this will be helpful for developing policing strategy as well as for sustainable community development.

Suggested Citation

  • Yicheng Tang & Xinyan Zhu & Wei Guo & Xinyue Ye & Tao Hu & Yaxin Fan & Faming Zhang, 2017. "Non-Homogeneous Diffusion of Residential Crime in Urban China," Sustainability, MDPI, vol. 9(6), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:934-:d:100343
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/6/934/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/6/934/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
    2. Ling Wu & Xinyue Ye & David Webb, 2012. "Space-Time Analysis of Auto Burglary Patterns in a Fast-Growing Small City," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 3(4), pages 69-86, October.
    3. Enrico di Bella & Matteo Corsi & Lucia Leporatti & Luca Persico, 2017. "The spatial configuration of urban crime environments and statistical modeling," Environment and Planning B, , vol. 44(4), pages 647-667, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dongping Long & Lin Liu & Jiaxin Feng & Suhong Zhou & Fengrui Jing, 2018. "Assessing the Influence of Prior on Subsequent Street Robbery Location Choices: A Case Study in ZG City, China," Sustainability, MDPI, vol. 10(6), pages 1-16, May.
    2. Fengrui Jing & Lin Liu & Suhong Zhou & Guangwen Song, 2020. "Examining the Relationship between Hukou Status, Perceived Neighborhood Conditions, and Fear of Crime in Guangzhou, China," Sustainability, MDPI, vol. 12(22), pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Inés Barbeito & Ricardo Cao & Stefan Sperlich, 2023. "Bandwidth selection for statistical matching and prediction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 418-446, March.
    2. Christoph Lambio & Tillman Schmitz & Richard Elson & Jeffrey Butler & Alexandra Roth & Silke Feller & Nicolai Savaskan & Tobia Lakes, 2023. "Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln," IJERPH, MDPI, vol. 20(10), pages 1-22, May.
    3. Max Köhler & Anja Schindler & Stefan Sperlich, 2014. "A Review and Comparison of Bandwidth Selection Methods for Kernel Regression," International Statistical Review, International Statistical Institute, vol. 82(2), pages 243-274, August.
    4. Peiyuan Zhang & Jiaming Li & Wenzhong Zhang, 2022. "Characteristics of High-Technology Industry Migration within Metropolitan Areas—A Case Study of Beijing Metropolitan Area," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    5. M. Hiabu & E. Mammen & M. D. Martìnez-Miranda & J. P. Nielsen, 2016. "In-sample forecasting with local linear survival densities," Biometrika, Biometrika Trust, vol. 103(4), pages 843-859.
    6. D.P. Amali Dassanayake & Igor Volobouev & A. Alexandre Trindade, 2017. "Local orthogonal polynomial expansion for density estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(4), pages 806-830, October.
    7. José María Sarabia & Faustino Prieto & Vanesa Jordá & Stefan Sperlich, 2020. "A Note on Combining Machine Learning with Statistical Modeling for Financial Data Analysis," Risks, MDPI, vol. 8(2), pages 1-14, April.
    8. Tingting Cheng & Jiti Gao & Xibin Zhang, 2014. "Semiparametric Localized Bandwidth Selection for Kernel Density Estimation," Monash Econometrics and Business Statistics Working Papers 27/14, Monash University, Department of Econometrics and Business Statistics.
    9. El Heda, Khadijetou & Louani, Djamal, 2018. "Optimal bandwidth selection in kernel density estimation for continuous time dependent processes," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 9-19.
    10. Stefan Sperlich, 2022. "Comments on: hybrid semiparametric Bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 335-339, June.
    11. Tingting Cheng & Jiti Gao & Xibin Zhang, 2019. "Nonparametric localized bandwidth selection for Kernel density estimation," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 733-762, August.
    12. Yu Liu & Chen Zeng & Huatai Cui & Yanhua Song, 2018. "Sustainable Land Urbanization and Ecological Carrying Capacity: A Spatially Explicit Perspective," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    13. Xiang Li & Jiang Zhu & Tao Liu & Xiangdong Yin & Jiangchun Yao & Hao Jiang & Bing Bu & Jianlong Yan & Yixuan Li & Zhangcheng Chen, 2023. "Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China," Land, MDPI, vol. 12(6), pages 1-21, June.
    14. Dai, Xinliang & Qu, Sheng & Sui, Hao & Wu, Pingbo, 2022. "Reliability modelling of wheel wear deterioration using conditional bivariate gamma processes and Bayesian hierarchical models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    15. Joseph Ngatchou-Wandji & Marwa Ltaifa & Didier Alain Njamen Njomen & Jia Shen, 2022. "Nonparametric Estimation of the Density Function of the Distribution of the Noise in CHARN Models," Mathematics, MDPI, vol. 10(4), pages 1-20, February.
    16. Zening Xu & Xiaolu Gao & Zhiqiang Wang & Jie Fan, 2019. "Big Data-Based Evaluation of Urban Parks: A Chinese Case Study," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    17. Liu, Zhi-Feng & Liu, You-Yuan & Chen, Xiao-Rui & Zhang, Shu-Rui & Luo, Xing-Fu & Li, Ling-Ling & Yang, Yi-Zhou & You, Guo-Dong, 2024. "A novel deep learning-based evolutionary model with potential attention and memory decay-enhancement strategy for short-term wind power point-interval forecasting," Applied Energy, Elsevier, vol. 360(C).
    18. Qidi Dong & Jun Cai & Linjia Wu & Di Li & Qibing Chen, 2022. "Quantitative Identification of Rural Functions Based on Big Data: A Case Study of Dujiangyan Irrigation District in Chengdu," Land, MDPI, vol. 11(3), pages 1-17, March.
    19. Gaoyuan Wang & Yixuan Wang & Yangli Li & Tian Chen, 2023. "Identification of Urban Clusters Based on Multisource Data—An Example of Three Major Urban Agglomerations in China," Land, MDPI, vol. 12(5), pages 1-25, May.
    20. Duan, Kun & Ren, Xiaohang & Shi, Yukun & Mishra, Tapas & Yan, Cheng, 2021. "The marginal impacts of energy prices on carbon price variations: Evidence from a quantile-on-quantile approach," Energy Economics, Elsevier, vol. 95(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:934-:d:100343. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.