Road casualties represent the leading cause of death among young people worldwide, especially in poor and developing countries. This paper introduces a Bayesian hierarchical model to analyse car accidents on a network lattice that takes into account measurement error in spatial covariates. We exemplified the proposed approach analysing all car crashes that occurred in the road network of Leeds (UK) from 2011 to 2019. Our results show that omitting measurement error considerably worsens the fit of the model and attenuates the effects of spatial covariates.
Gilardi, A., Borgoni, R., Presicce, L., Mateu, J. (2021). Measurement error models on spatial network lattices: car crashes in Leeds. In CLADAG 2021 Book of abstracts and short papers (pp.348-351). Firenze [10.36253/978-88-5518-340-6].
Measurement error models on spatial network lattices: car crashes in Leeds
Gilardi, A.
;Borgoni, R.;Presicce, L.;
2021
Abstract
Road casualties represent the leading cause of death among young people worldwide, especially in poor and developing countries. This paper introduces a Bayesian hierarchical model to analyse car accidents on a network lattice that takes into account measurement error in spatial covariates. We exemplified the proposed approach analysing all car crashes that occurred in the road network of Leeds (UK) from 2011 to 2019. Our results show that omitting measurement error considerably worsens the fit of the model and attenuates the effects of spatial covariates.
| File | Dimensione | Formato | |
|---|---|---|---|
|
main.pdf
accesso aperto
Descrizione: Articolo principale
Tipologia di allegato:
Publisher’s Version (Version of Record, VoR)
Dimensione
370.62 kB
Formato
Adobe PDF
|
370.62 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
