Imperial College London

ProfessorMartaBlangiardo

Faculty of MedicineSchool of Public Health

Chair in Biostatistics
 
 
 
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Contact

 

m.blangiardo Website

 
 
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Location

 

528Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Boulieri:2016:10.1111/rssa.12178,
author = {Boulieri, A and Liverani, S and de, Hoogh K and Blangiardo, M},
doi = {10.1111/rssa.12178},
journal = {Journal of the Royal Statistical Society. Series A. Statistics in Society},
pages = {119--139},
title = {A space-time multivariate Bayesian model to analyse road traffic accidents by severity},
url = {http://dx.doi.org/10.1111/rssa.12178},
volume = {180},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - his paper investigates the dependencies between severity levels ofroad traffic accidents, accounting at the same time for spatial and temporal cor-relations. The study analyses road traffic accidents data at ward level in Englandover the period 2005-2013. We include in our model multivariate spatially struc-tured and unstructured effects to capture the respective dependencies betweenseverities, within a Bayesian hierarchical formulation. We also include a tempo-ral component to capture the time effects and we carry out an extensive modelcomparison. The results show important associations in both spatially structuredand unstructured effects between severities, while a downward temporal trend isobserved for low and high severity levels. Maps of posterior accident rates indi-cate elevated risk within big cities for accidents of low severity and in suburbanareas in the north and on the southern coast of England for accidents of high2Boulieriet al.severity. Posterior probability of extreme rates is used to suggest the presenceof hot spots in a public health perspective.
AU - Boulieri,A
AU - Liverani,S
AU - de,Hoogh K
AU - Blangiardo,M
DO - 10.1111/rssa.12178
EP - 139
PY - 2016///
SN - 0964-1998
SP - 119
TI - A space-time multivariate Bayesian model to analyse road traffic accidents by severity
T2 - Journal of the Royal Statistical Society. Series A. Statistics in Society
UR - http://dx.doi.org/10.1111/rssa.12178
UR - http://hdl.handle.net/10044/1/29135
VL - 180
ER -