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Artículo

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

Becerra, Melgris José; Pimentel, Marcia Aparecida; De Souza, Everaldo Barreiros; Tovar Jimenez, Gabriel IbrahinIcon
Fecha de publicación: 09/2020
Editorial: Elsevier
Revista: Geography and Sustainability
ISSN: 2666-6839
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias Medioambientales

Resumen

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.
Palabras clave: BIG DATA , CLIMATE CHANGE , COASTAL , MACHINE LEARNING , PERCEPTION
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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Atribución-NoComercial-SinDerivadas 2.5 Argentina (CC BY-NC-ND 2.5 AR)
Identificadores
URI: http://hdl.handle.net/11336/129434
URL: https://linkinghub.elsevier.com/retrieve/pii/S2666683920300420
DOI: http://dx.doi.org/10.1016/j.geosus.2020.09.002
Colecciones
Articulos(IQUIMEFA)
Articulos de INST.QUIMICA Y METABOLISMO DEL FARMACO (I)
Citación
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
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