Urban crime prediction based on spatio-temporal Bayesian model

Tao Hu; Xinyan Zhu; Lian Duan; Wei Guo

doi:10.1371/journal.pone.0206215

Urban crime prediction based on spatio-temporal Bayesian model

PLoS One. 2018 Oct 31;13(10):e0206215. doi: 10.1371/journal.pone.0206215. eCollection 2018.

Authors

Tao Hu^{1

2

3

4}, Xinyan Zhu^{1

2}, Lian Duan^{4

5}, Wei Guo^{1

2}

Affiliations

¹ State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China.
² Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China.
³ Department of Geography, Kent State University, Kent, Ohio, United States of America.
⁴ Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Guangxi Teachers Education University, Guangxi, China.
⁵ Geography Science and Planning School, Guangxi Teachers Education University, Guangxi, China.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
China / epidemiology
Crime / statistics & numerical data*
Humans
Socioeconomic Factors
Spatio-Temporal Analysis
Urban Population*

Grants and funding

This research was supported by the the National Key Research and Development Program of China, 2017YFB0503704, to WG; the National Natural Science Foundation of China, 41401524, to LD; the Natural Science Foundation of Guangxi Province, 2015GXNSFBA139191, to LD; the Funds for the Central Universities, 413000010, to TH; the Open Found of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 16(03), to TH; the Open Research Program of Key Laboratory of Police Geographic Information Technology, Ministry of Public Security, 2016LPGIT03, to LD; Scientific Project of Guangxi Education Department, KY2015YB189 to LD; the Open Research Program of Key Laboratory of Environment Change and Resources Use in Beibu Gulf, 2014BGERLXT14 to LD; and the Open Research Program of Key Laboratory of Mine Spatial Information Technologies of National Administration of Surveying, Mapping and Geoinformation, KLM201409 to LD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.