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Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach

Author

Listed:
  • Daqian Liu

    (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China)

  • Wei Song

    (Department of Geographic and Environmental Sciences, University of Louisville, Louisville, KY 40292, USA)

  • Chunliang Xiu

    (Jangho Architecture College, Northeastern University, Shenyang 110169, China)

  • Jun Xu

    (Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun Institute of Technology, Changchun 130012, China)

Abstract

Chinese cities have been undergoing extraordinary changes in many respects during the process of urbanization, which has caused crime patterns to evolve accordingly. This research applies a Bayesian spatiotemporal model to explore and understand the spatiotemporal patterns of crime risk from 2008 to 2017 in Changchun, China. The overall temporal trend of crime risk, the effects of land use covariates, spatial random effects, and area-specific differential trends are estimated through a Bayesian spatiotemporal model fitted using the Integrated Nested Laplace Approximation (INLA). The analytical results show that the regression coefficient for the overall temporal trend of crime risk changed from significantly positive to negative after the land use variables are incorporated into the Bayesian spatiotemporal model. The covariates of road density, commercial and recreational land per capita, residential land per capita, and industrial land per capita are found to be significantly associated with crime risk, which relates to classic theories in environmental criminology. In addition, some areas still exhibit significantly increasing crime risks compared with the general trend even after controlling for the land use covariates and the spatial random effects, which may provide insights for law enforcement and researchers regarding where more attention is required since there may be some unmeasured factors causing higher crime trend in these areas.

Suggested Citation

  • Daqian Liu & Wei Song & Chunliang Xiu & Jun Xu, 2021. "Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach," Sustainability, MDPI, vol. 13(19), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:10500-:d:640432
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    References listed on IDEAS

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    1. Minxuan Lan & Lin Liu & Andres Hernandez & Weiyi Liu & Hanlin Zhou & Zengli Wang, 2019. "The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    2. Malleson, Nick & Andresen, Martin A., 2016. "Exploring the impact of ambient population measures on London crime hotspots," Journal of Criminal Justice, Elsevier, vol. 46(C), pages 52-63.
    3. Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
    4. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    5. Hodgkinson, Tarah & Andresen, Martin A. & Farrell, Graham, 2016. "The decline and locational shift of automotive theft: A local level analysis," Journal of Criminal Justice, Elsevier, vol. 44(C), pages 49-57.
    6. Kingsley U. Ejiogu, 2020. "Block-Level Analysis of the Attractors of Robbery in a Downtown Area," SAGE Open, , vol. 10(4), pages 21582440209, October.
    7. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    8. Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.
    9. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    10. Tao Hu & Xinyan Zhu & Lian Duan & Wei Guo, 2018. "Urban crime prediction based on spatio-temporal Bayesian model," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-18, October.
    11. Linning, Shannon J., 2015. "Crime seasonality and the micro-spatial patterns of property crime in Vancouver, BC and Ottawa, ON," Journal of Criminal Justice, Elsevier, vol. 43(6), pages 544-555.
    12. Guangwen Song & Lin Liu & Wim Bernasco & Luzi Xiao & Suhong Zhou & Weiwei Liao, 2018. "Testing Indicators of Risk Populations for Theft from the Person across Space and Time: The Significance of Mobility and Outdoor Activity," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 108(5), pages 1370-1388, September.
    13. Wei Song & Daqian Liu, 2013. "Exploring Spatial Patterns of Property Crime Risks in Changchun, China," International Journal of Applied Geospatial Research (IJAGR), IGI Global, vol. 4(3), pages 80-100, July.
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