Geospatiality of climate change perceptions on coastal regions: A systematic bibliometric analysis
| Autor: | Everaldo Barreiros de Souza, Melgris José Becerra, Gabriel Ibrahin Tovar Jimenez, Márcia Aparecida da Silva Pimentel |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Geospatial analysis
BIG DATA media_common.quotation_subject CLIMATE CHANGE Geography Planning and Development Big data Scopus lcsh:G1-922 Climate change Context (language use) computer.software_genre Extreme weather Perception Machine learning lcsh:Environmental sciences Nature and Landscape Conservation Earth-Surface Processes media_common lcsh:GE1-350 PERCEPTION purl.org/becyt/ford/5 [https] Ecology business.industry COASTAL Environmental resource management Livelihood Coastal Geography MACHINE LEARNING business computer purl.org/becyt/ford/5.7 [https] lcsh:Geography (General) |
| Zdroj: | Geography and Sustainability, Vol 1, Iss 3, Pp 209-219 (2020) CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET |
| ISSN: | 2666-6839 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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