Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/88954 https://doi.org/10.1016/j.apgeog.2014.04.002 |
Resumo: | The spatial patterns of fire occurrence were analyzed in two regions of Southern Europe, focusing on the long-term factors that influence fire distribution. The relationship between fire occurrence and the physical and anthropogenic variables collected was investigated with Geographically Weighted Regression (GWR) and the results were compared with Ordinary Least Squares (OLS). Local patterns of the significant variables were explored and a strong spatial variability of their explanatory power was revealed. Climate (precipitation), livestock and land cover (shrubland) were found to be significant in both regions, although in particular areas and to different extents. Regarding model performance, GWR showed an improvement over OLS in both regions. The investigation of the spatial variation in the importance of the main drivers over a broad study area, gives a valuable contribution to the improvement of fire management and prevention strategies, adjusted to the particular conditions of different areas. |
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Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted RegressionFire densitySpatial patternsDriving factorsSouth European regionsGeographically Weighted RegressionThe spatial patterns of fire occurrence were analyzed in two regions of Southern Europe, focusing on the long-term factors that influence fire distribution. The relationship between fire occurrence and the physical and anthropogenic variables collected was investigated with Geographically Weighted Regression (GWR) and the results were compared with Ordinary Least Squares (OLS). Local patterns of the significant variables were explored and a strong spatial variability of their explanatory power was revealed. Climate (precipitation), livestock and land cover (shrubland) were found to be significant in both regions, although in particular areas and to different extents. Regarding model performance, GWR showed an improvement over OLS in both regions. The investigation of the spatial variation in the importance of the main drivers over a broad study area, gives a valuable contribution to the improvement of fire management and prevention strategies, adjusted to the particular conditions of different areas.Elsevier2014-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/88954http://hdl.handle.net/10316/88954https://doi.org/10.1016/j.apgeog.2014.04.002eng01436228https://www.sciencedirect.com/science/article/pii/S0143622814000630?via%3DihubOliveira, SandraPereira, José M.C.San-Miguel-Ayanz, JesúsLourenço, Luciano Fernandesinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-13T10:02:53Zoai:estudogeral.uc.pt:10316/88954Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:09:25.004914Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
title |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
spellingShingle |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression Oliveira, Sandra Fire density Spatial patterns Driving factors South European regions Geographically Weighted Regression |
title_short |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
title_full |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
title_fullStr |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
title_full_unstemmed |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
title_sort |
Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression |
author |
Oliveira, Sandra |
author_facet |
Oliveira, Sandra Pereira, José M.C. San-Miguel-Ayanz, Jesús Lourenço, Luciano Fernandes |
author_role |
author |
author2 |
Pereira, José M.C. San-Miguel-Ayanz, Jesús Lourenço, Luciano Fernandes |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Oliveira, Sandra Pereira, José M.C. San-Miguel-Ayanz, Jesús Lourenço, Luciano Fernandes |
dc.subject.por.fl_str_mv |
Fire density Spatial patterns Driving factors South European regions Geographically Weighted Regression |
topic |
Fire density Spatial patterns Driving factors South European regions Geographically Weighted Regression |
description |
The spatial patterns of fire occurrence were analyzed in two regions of Southern Europe, focusing on the long-term factors that influence fire distribution. The relationship between fire occurrence and the physical and anthropogenic variables collected was investigated with Geographically Weighted Regression (GWR) and the results were compared with Ordinary Least Squares (OLS). Local patterns of the significant variables were explored and a strong spatial variability of their explanatory power was revealed. Climate (precipitation), livestock and land cover (shrubland) were found to be significant in both regions, although in particular areas and to different extents. Regarding model performance, GWR showed an improvement over OLS in both regions. The investigation of the spatial variation in the importance of the main drivers over a broad study area, gives a valuable contribution to the improvement of fire management and prevention strategies, adjusted to the particular conditions of different areas. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-07 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10316/88954 http://hdl.handle.net/10316/88954 https://doi.org/10.1016/j.apgeog.2014.04.002 |
url |
http://hdl.handle.net/10316/88954 https://doi.org/10.1016/j.apgeog.2014.04.002 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
01436228 https://www.sciencedirect.com/science/article/pii/S0143622814000630?via%3Dihub |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799133988104699904 |