Understanding the Spatiotemporal Pattern of Crimes in Changchun, China: A Bayesian Modeling Approach
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Kingsley U. Ejiogu, 2020. "Block-Level Analysis of the Attractors of Robbery in a Downtown Area," SAGE Open, , vol. 10(4), pages 21582440209, October.
- 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.
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Keywords
spatiotemporal pattern; crime trend; Bayesian hierarchical model; INLA; China;All these keywords.
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