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Bayesian hierarchical spatial models: Implementing the Besag York MolliƩ model in stan.
- Source :
- Spatial & Spatio-temporal Epidemiology; Nov2019, Vol. 31, pN.PAG-N.PAG, 1p
- Publication Year :
- 2019
-
Abstract
- This report presents a new implementation of the Besag-York-Mollié (BYM) model in Stan, a probabilistic programming platform which does full Bayesian inference using Hamiltonian Monte Carlo (HMC). We review the spatial auto-correlation models used for areal data and disease risk mapping, and describe the corresponding Stan implementations. We also present a case study using Stan to fit a BYM model for motor vehicle crashes injuring school-age pedestrians in New York City from 2005 to 2014 localized to census tracts. Stan efficiently fit our multivariable BYM model having a large number of observations (n=2095 census tracts) with small outcome counts < 10 in the majority of tracts. Our findings reinforced that neighborhood income and social fragmentation are significant correlates of school-age pedestrian injuries. We also observed that nationally-available census tract estimates of commuting methods may serve as a useful indicator of underlying pedestrian densities. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 18775845
- Volume :
- 31
- Database :
- Supplemental Index
- Journal :
- Spatial & Spatio-temporal Epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 139387044
- Full Text :
- https://doi.org/10.1016/j.sste.2019.100301