Spatial modeling of trends in crime over time in Philadelphia
| Autor: | Cecilia Balocchi, Shane T. Jensen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
0301 basic medicine Statistics and Probability Computer science Association (object-oriented programming) Bayesian probability Bayesian inference Statistics - Applications 01 natural sciences Methodology (stat.ME) 010104 statistics & probability 03 medical and health sciences Urban planning Econometrics Applications (stat.AP) Urbanism 0101 mathematics Spatial analysis Statistics - Methodology Built environment crime time trends Law enforcement spatial 030104 developmental biology Modeling and Simulation Statistics Probability and Uncertainty |
| Zdroj: | Ann. Appl. Stat. 13, no. 4 (2019), 2235-2259 |
| ISSN: | 1932-6157 |
| DOI: | 10.1214/19-aoas1280 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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