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dc.contributor.author
Becerra, Melgris José
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Pimentel, Marcia Aparecida
dc.contributor.author
De Souza, Everaldo Barreiros
dc.contributor.author
Tovar Jimenez, Gabriel Ibrahin
dc.date.available
2021-04-06T14:35:53Z
dc.date.issued
2020-09
dc.identifier.citation
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
dc.identifier.issn
2666-6839
dc.identifier.uri
http://hdl.handle.net/11336/129434
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
BIG DATA
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CLIMATE CHANGE
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COASTAL
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MACHINE LEARNING
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PERCEPTION
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Ciencias Medioambientales
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Geografía Económica y Social
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CIENCIAS SOCIALES
dc.title
Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-03-26T19:54:28Z
dc.journal.volume
1
dc.journal.number
3
dc.journal.pagination
209-219
dc.journal.pais
China
dc.journal.ciudad
Beijimg
dc.description.fil
Fil: Becerra, Melgris José. Universidade Federal do Pará; Brasil
dc.description.fil
Fil: Pimentel, Marcia Aparecida. Universidade Federal do Pará; Brasil
dc.description.fil
Fil: De Souza, Everaldo Barreiros. Universidade Federal do Pará; Brasil
dc.description.fil
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
dc.journal.title
Geography and Sustainability
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2666683920300420
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.geosus.2020.09.002
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