Balocchi, Cecilia; Jensen, Shane T. Spatial modeling of trends in crime over time in Philadelphia. (English) Zbl 1435.62430 Ann. Appl. Stat. 13, No. 4, 2235-2259 (2019). Summary: Understanding the relationship between change in crime over time and the geography of urban areas is an important problem for urban planning. Accurate estimation of changing crime rates throughout a city would aid law enforcement as well as enable studies of the association between crime and the built environment. Bayesian modeling is a promising direction since areal data require principled sharing of information to address spatial autocorrelation between proximal neighborhoods. We develop several Bayesian approaches to spatial sharing of information between neighborhoods while modeling trends in crime counts over time. We apply our methodology to estimate changes in crime throughout Philadelphia over the 2006–15 period while also incorporating spatially-varying economic and demographic predictors. We find that the local shrinkage imposed by a conditional autoregressive model has substantial benefits in terms of out-of-sample predictive accuracy of crime. We also explore the possibility of spatial discontinuities between neighborhoods that could represent natural barriers or aspects of the built environment. Cited in 4 Documents MSC: 62P25 Applications of statistics to social sciences 62M30 Inference from spatial processes 62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH) Keywords:urbanism; crime; spatial; time trends Software:ggplot2; spBayes; ggmap × Cite Format Result Cite Review PDF Full Text: DOI arXiv Euclid References: [1] Aldor-Noiman, S., Brown, L. D., Fox, E. B. and Stine, R. A. (2016). Spatio-temporal low count processes with application to violent crime events. Statist. Sinica 26 1587-1610. · Zbl 1356.62131 [2] Anderson, C. and Ryan, L. M. (2017). 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