Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices

A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is...

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Authors: Barber, Xavier, Conesa Ortega, David|||0000-0001-5917-2673, Lopez-Quílez, Antonio, Mayoral, Asuncion, Morales, Javier, Barber, Antoni
Format: article
Publication Date:2017
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/178491
Online Access:https://hdl.handle.net/2117/178491
Access Level:Open access
Keyword:Bioclimatology
geostatistics
parallel computation
spatial prediction
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::62 Statistics::62P Applications
Classificació AMS::86 Geophysics
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
id ES_7fdcb9d6d629772bfc9d475db15dfc84
oai_identifier_str oai:upcommons.upc.edu:2117/178491
network_acronym_str ES
network_name_str España
repository_id_str
spelling Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indicesBarber, XavierConesa Ortega, David|||0000-0001-5917-2673Lopez-Quílez, AntonioMayoral, AsuncionMorales, JavierBarber, AntoniBioclimatologygeostatisticsparallel computationspatial predictionClassificació AMS::62 Statistics::62M Inference from stochastic processesClassificació AMS::62 Statistics::62P ApplicationsClassificació AMS::86 GeophysicsClassificació AMS::62 Statistics::62F Parametric inferenceÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàticaA methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.Peer ReviewedInstitut d'Estadística de Catalunya20172017-12-1920202020-02-24journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/178491reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1784912026-05-27T15:37:01Z
dc.title.none.fl_str_mv Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
title Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
spellingShingle Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
Barber, Xavier
Bioclimatology
geostatistics
parallel computation
spatial prediction
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::62 Statistics::62P Applications
Classificació AMS::86 Geophysics
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
title_short Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
title_full Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
title_fullStr Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
title_full_unstemmed Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
title_sort Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices
dc.creator.none.fl_str_mv Barber, Xavier
Conesa Ortega, David|||0000-0001-5917-2673
Lopez-Quílez, Antonio
Mayoral, Asuncion
Morales, Javier
Barber, Antoni
author Barber, Xavier
author_facet Barber, Xavier
Conesa Ortega, David|||0000-0001-5917-2673
Lopez-Quílez, Antonio
Mayoral, Asuncion
Morales, Javier
Barber, Antoni
author_role author
author2 Conesa Ortega, David|||0000-0001-5917-2673
Lopez-Quílez, Antonio
Mayoral, Asuncion
Morales, Javier
Barber, Antoni
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Bioclimatology
geostatistics
parallel computation
spatial prediction
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::62 Statistics::62P Applications
Classificació AMS::86 Geophysics
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
topic Bioclimatology
geostatistics
parallel computation
spatial prediction
Classificació AMS::62 Statistics::62M Inference from stochastic processes
Classificació AMS::62 Statistics::62P Applications
Classificació AMS::86 Geophysics
Classificació AMS::62 Statistics::62F Parametric inference
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
description A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-12-19
2020
2020-02-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/178491
url https://hdl.handle.net/2117/178491
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institut d'Estadística de Catalunya
publisher.none.fl_str_mv Institut d'Estadística de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411855975317504
score 15,300719

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