Measurement error models for spatial network lattice data: Analysis of car crashes in Leeds
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1093/jrsssa/qnad057 |
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Title
Measurement error models for spatial network lattice data: Analysis of car crashes in LeedsDate
2023-04-28Publisher
Royal Statistical Society; Oxford University PressISSN
0964-1998; 1467-985XBibliographic citation
Andrea Gilardi and others, Measurement error models for spatial network lattice data: Analysis of car crashes in Leeds, Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 186, Issue 3, July 2023, Pages 313–334, https://doi.org/10.1093/jrsssa/qnad057Type
info:eu-repo/semantics/articlePublisher version
https://academic.oup.com/jrsssa/article-abstract/186/3/313/7146735?redirectedFro ...Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed ... [+]
Road casualties represent an alarming concern for modern societies. During the last years, several authors proposed sophisticated approaches to help authorities implement new policies. These models were usually developed considering a set of socio-economic variables and ignoring the measurement error, which can bias the statistical inference. This paper presents a Bayesian model to analyse car crash occurrences at the network-lattice level, taking into account measurement error in the spatial covariate. The suggested methodology is exemplified by considering the collisions in the road network of Leeds (UK) during 2011–2019. Traffic volumes are approximated using an extensive set of counts obtained from mobile devices and the estimates are adjusted using a spatial measurement error correction. [-]
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Journal of the Royal Statistical Society Series A: Statistics in Society, Volume 186, Issue 3, July 2023, Pages 313–334, https://doi.org/10.1093/jrsssa/qnad057Rights
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- MAT_Articles [763]