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Exploring Spatial Patterns of Property Crime Risks in Changchun, China

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  • Wei Song

    (Department of Geography and Geosciences, University of Louisville, Lousville, KY, USA)

  • Daqian Liu

    (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchung, P.R. China)

Abstract

Urban crime has increasingly become a major issue for Chinese cities. Using crime data collected at police precincts in 2008, the main aim of this research is to examine the spatial distribution of property crime which accounted for almost 82% of all crimes in the city of Changchun, and analyze the relationship between the spatial patterns of property crime and neighborhood characteristics. Standardized property crime rates (SCR) were applied to assess the relative risk of property crime across the city. Statistically significant clusters of high-risk areas or hot-spots were detected. A global ordinary least squares (OLS) regression model and a geographically weighted regression (GWR) model were calibrated to explore the risk of property crime as a function of contextual neighborhood characteristics. The analytical results show that significant local variations exist in the relationship between the risk of property crime and several neighborhood socioeconomic variables.

Suggested Citation

  • 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.
    • Handle: RePEc:igg:jagr00:v:4:y:2013:i:3:p:80-100
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    Cited by:

    1. Wuxue Cheng & Yajun Rao & Yixin Tang & Jiajia Yang & Yuxin Chen & Li Peng & Jiangcheng Hao, 2022. "Identifying the Spatio-Temporal Characteristics of Crime in Liangshan Prefecture, China," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
    2. 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.

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