Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
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...
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doaj-968006036e7c417dbd66f4f4a5b053292020-12-30T04:24:04ZengElsevierGeography and Sustainability2666-68392020-09-0113209219Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysisMelgris José Becerra0Marcia Aparecida Pimentel1Everaldo Barreiros De Souza2Gabriel Ibrahin Tovar3Universidade Federal do Pará (UFPA), Instituto de Geocincias, Belém CEP 60440-554, BrazilUniversidade Federal do Pará (UFPA), Programa de Pós-graduao em Geografia, Belém CEP 60440-554, BrazilUniversidade Federal do Pará (UFPA), Instituto de Geocincias, Belém CEP 60440-554, Brazil; Corresponding author.Universidad de Buenos Aires (UBA), Facultad de Farmacia y Bioquímica, Departamento de Química Analítica y Fisicoquímica, Buenos Aires C1113AAD, Argentina; CONICET − Universidad de Buenos Aires (UBA). Instituto de Química y Metabolismo del Fármaco (IQUIMEFA), Buenos Aires C1111AAI, Argentina; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2666683920300420Climate changePerceptionCoastalMachine learningBig data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Melgris José Becerra Marcia Aparecida Pimentel Everaldo Barreiros De Souza Gabriel Ibrahin Tovar |
spellingShingle |
Melgris José Becerra Marcia Aparecida Pimentel Everaldo Barreiros De Souza Gabriel Ibrahin Tovar Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis Geography and Sustainability Climate change Perception Coastal Machine learning Big data |
author_facet |
Melgris José Becerra Marcia Aparecida Pimentel Everaldo Barreiros De Souza Gabriel Ibrahin Tovar |
author_sort |
Melgris José Becerra |
title |
Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis |
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 |
publisher |
Elsevier |
series |
Geography and Sustainability |
issn |
2666-6839 |
publishDate |
2020-09-01 |
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. |
topic |
Climate change Perception Coastal Machine learning Big data |
url |
http://www.sciencedirect.com/science/article/pii/S2666683920300420 |
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