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Urban crime prediction based on spatio-temporal Bayesian model

Author

Listed:
  • Tao Hu
  • Xinyan Zhu
  • Lian Duan
  • Wei Guo

Abstract

Spatio-temporal Bayesian modeling, a method based on regional statistics, is widely used in epidemiological studies. Using Bayesian theory, this study builds a spatio-temporal Bayesian model specific to urban crime to analyze its spatio-temporal patterns and determine any developing trends. The associated covariates and their changes are also analyzed. The model is then used to analyze data regarding burglaries that occurred in Wuhan City in China from January to August 2013. Of the diverse socio-economic variables associated with crime rate, including population, the number of local internet bars, hotels, shopping centers, unemployment rate, and residential zones, this study finds that the burglary crime rate is significantly correlated with the average resident population per community and number of local internet bars. This finding provides a scientific reference for urban safety protection.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0206215
    DOI: 10.1371/journal.pone.0206215
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Zhen Yu & Keming Yu & Wolfgang K. Härdle & Xueliang Zhang & Kai Wang & Maozai Tian, 2022. "Bayesian spatio‐temporal modeling for the inpatient hospital costs of alcohol‐related disorders," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 644-667, December.

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