Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis

Autores
Becerra, Melgris José; Pimentel, Marcia Aparecida; De Souza, Everaldo Barreiros; Tovar Jimenez, Gabriel Ibrahin
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Climate change requires joint actions between government and local actors. Understanding the perception of people and communities is critical for designing climate change adaptation strategies. Those most affected by climate change are populations in coastal regions that face extreme weather events and sea-level increases. In this article, geospatial perception of climate change is identified, and the research parameters are quantified. In addition to investigating the correlations of hotspots on the topic of climate change perception with a focus on coastal communities, Natural Language Processing (NLP) was used to examine the research interactions. A total of 27,138 articles sources from Google Scholar and Scopus were analyzed. A systematic method was used for data processing combining bibliometric analysis and machine learning. Publication trends were analyzed in English, Spanish and Portuguese. Publications in English (87%) were selected for network and data mining analysis. Most of the research was conducted in the USA, followed by India and China. The main research methods were identified through correlation networks. In many cases, social studies of perception are related to climatic methods and vegetation analysis supported by GIS. The analysis of keywords identified ten research topics: adaptation, risk, community, local, impact, livelihood, farmer, household, strategy, and variability. “Adaptation” is in the core of the correlation network of all keywords. The interdisciplinary analysis between social and environmental factors, suggest improvements are needed for research in this field. A single method cannot address understanding of a phenomenon as complicated as the socio-environmental. This study provides valuable information for future research by clarifying the current context of perception work carried out in the coastal regions; and identifying the tools best suited for carrying out this type of research.
Fil: Becerra, Melgris José. Universidade Federal do Pará; Brasil
Fil: Pimentel, Marcia Aparecida. Universidade Federal do Pará; Brasil
Fil: De Souza, Everaldo Barreiros. Universidade Federal do Pará; Brasil
Fil: Tovar Jimenez, Gabriel Ibrahin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Química Analítica y Fisicoquímica; Argentina
Materia
BIG DATA
CLIMATE CHANGE
COASTAL
MACHINE LEARNING
PERCEPTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/129434

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spelling Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysisBecerra, Melgris JoséPimentel, Marcia AparecidaDe Souza, Everaldo BarreirosTovar Jimenez, Gabriel IbrahinBIG DATACLIMATE CHANGECOASTALMACHINE LEARNINGPERCEPTIONhttps://purl.org/becyt/ford/5.7https://purl.org/becyt/ford/5Climate change requires joint actions between government and local actors. Understanding the perception of people and communities is critical for designing climate change adaptation strategies. Those most affected by climate change are populations in coastal regions that face extreme weather events and sea-level increases. In this article, geospatial perception of climate change is identified, and the research parameters are quantified. In addition to investigating the correlations of hotspots on the topic of climate change perception with a focus on coastal communities, Natural Language Processing (NLP) was used to examine the research interactions. A total of 27,138 articles sources from Google Scholar and Scopus were analyzed. A systematic method was used for data processing combining bibliometric analysis and machine learning. Publication trends were analyzed in English, Spanish and Portuguese. Publications in English (87%) were selected for network and data mining analysis. Most of the research was conducted in the USA, followed by India and China. The main research methods were identified through correlation networks. In many cases, social studies of perception are related to climatic methods and vegetation analysis supported by GIS. The analysis of keywords identified ten research topics: adaptation, risk, community, local, impact, livelihood, farmer, household, strategy, and variability. “Adaptation” is in the core of the correlation network of all keywords. The interdisciplinary analysis between social and environmental factors, suggest improvements are needed for research in this field. A single method cannot address understanding of a phenomenon as complicated as the socio-environmental. This study provides valuable information for future research by clarifying the current context of perception work carried out in the coastal regions; and identifying the tools best suited for carrying out this type of research.Fil: Becerra, Melgris José. Universidade Federal do Pará; BrasilFil: Pimentel, Marcia Aparecida. Universidade Federal do Pará; BrasilFil: De Souza, Everaldo Barreiros. Universidade Federal do Pará; BrasilFil: Tovar Jimenez, Gabriel Ibrahin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Química Analítica y Fisicoquímica; ArgentinaElsevier2020-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/129434Becerra, Melgris José; Pimentel, Marcia Aparecida; De Souza, Everaldo Barreiros; Tovar Jimenez, Gabriel Ibrahin; Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis; Elsevier; Geography and Sustainability; 1; 3; 9-2020; 209-2192666-6839CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2666683920300420info:eu-repo/semantics/altIdentifier/doi/10.1016/j.geosus.2020.09.002info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:27:34Zoai:ri.conicet.gov.ar:11336/129434instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:27:34.26CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
title Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
spellingShingle Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
Becerra, Melgris José
BIG DATA
CLIMATE CHANGE
COASTAL
MACHINE LEARNING
PERCEPTION
title_short Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
title_full Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
title_fullStr Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
title_full_unstemmed Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
title_sort Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
dc.creator.none.fl_str_mv Becerra, Melgris José
Pimentel, Marcia Aparecida
De Souza, Everaldo Barreiros
Tovar Jimenez, Gabriel Ibrahin
author Becerra, Melgris José
author_facet Becerra, Melgris José
Pimentel, Marcia Aparecida
De Souza, Everaldo Barreiros
Tovar Jimenez, Gabriel Ibrahin
author_role author
author2 Pimentel, Marcia Aparecida
De Souza, Everaldo Barreiros
Tovar Jimenez, Gabriel Ibrahin
author2_role author
author
author
dc.subject.none.fl_str_mv BIG DATA
CLIMATE CHANGE
COASTAL
MACHINE LEARNING
PERCEPTION
topic BIG DATA
CLIMATE CHANGE
COASTAL
MACHINE LEARNING
PERCEPTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.7
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Climate change requires joint actions between government and local actors. Understanding the perception of people and communities is critical for designing climate change adaptation strategies. Those most affected by climate change are populations in coastal regions that face extreme weather events and sea-level increases. In this article, geospatial perception of climate change is identified, and the research parameters are quantified. In addition to investigating the correlations of hotspots on the topic of climate change perception with a focus on coastal communities, Natural Language Processing (NLP) was used to examine the research interactions. A total of 27,138 articles sources from Google Scholar and Scopus were analyzed. A systematic method was used for data processing combining bibliometric analysis and machine learning. Publication trends were analyzed in English, Spanish and Portuguese. Publications in English (87%) were selected for network and data mining analysis. Most of the research was conducted in the USA, followed by India and China. The main research methods were identified through correlation networks. In many cases, social studies of perception are related to climatic methods and vegetation analysis supported by GIS. The analysis of keywords identified ten research topics: adaptation, risk, community, local, impact, livelihood, farmer, household, strategy, and variability. “Adaptation” is in the core of the correlation network of all keywords. The interdisciplinary analysis between social and environmental factors, suggest improvements are needed for research in this field. A single method cannot address understanding of a phenomenon as complicated as the socio-environmental. This study provides valuable information for future research by clarifying the current context of perception work carried out in the coastal regions; and identifying the tools best suited for carrying out this type of research.
Fil: Becerra, Melgris José. Universidade Federal do Pará; Brasil
Fil: Pimentel, Marcia Aparecida. Universidade Federal do Pará; Brasil
Fil: De Souza, Everaldo Barreiros. Universidade Federal do Pará; Brasil
Fil: Tovar Jimenez, Gabriel Ibrahin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Química Analítica y Fisicoquímica; Argentina
description Climate change requires joint actions between government and local actors. Understanding the perception of people and communities is critical for designing climate change adaptation strategies. Those most affected by climate change are populations in coastal regions that face extreme weather events and sea-level increases. In this article, geospatial perception of climate change is identified, and the research parameters are quantified. In addition to investigating the correlations of hotspots on the topic of climate change perception with a focus on coastal communities, Natural Language Processing (NLP) was used to examine the research interactions. A total of 27,138 articles sources from Google Scholar and Scopus were analyzed. A systematic method was used for data processing combining bibliometric analysis and machine learning. Publication trends were analyzed in English, Spanish and Portuguese. Publications in English (87%) were selected for network and data mining analysis. Most of the research was conducted in the USA, followed by India and China. The main research methods were identified through correlation networks. In many cases, social studies of perception are related to climatic methods and vegetation analysis supported by GIS. The analysis of keywords identified ten research topics: adaptation, risk, community, local, impact, livelihood, farmer, household, strategy, and variability. “Adaptation” is in the core of the correlation network of all keywords. The interdisciplinary analysis between social and environmental factors, suggest improvements are needed for research in this field. A single method cannot address understanding of a phenomenon as complicated as the socio-environmental. This study provides valuable information for future research by clarifying the current context of perception work carried out in the coastal regions; and identifying the tools best suited for carrying out this type of research.
publishDate 2020
dc.date.none.fl_str_mv 2020-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/129434
Becerra, Melgris José; Pimentel, Marcia Aparecida; De Souza, Everaldo Barreiros; Tovar Jimenez, Gabriel Ibrahin; Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis; Elsevier; Geography and Sustainability; 1; 3; 9-2020; 209-219
2666-6839
CONICET Digital
CONICET
url http://hdl.handle.net/11336/129434
identifier_str_mv Becerra, Melgris José; Pimentel, Marcia Aparecida; De Souza, Everaldo Barreiros; Tovar Jimenez, Gabriel Ibrahin; Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis; Elsevier; Geography and Sustainability; 1; 3; 9-2020; 209-219
2666-6839
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2666683920300420
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.geosus.2020.09.002
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
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