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

• Published in: Geography and sustainability Vol. 1; no. 3; pp. 209 - 219
• Main Authors: Becerra, Melgris José, Pimentel, Marcia Aparecida, De Souza, Everaldo Barreiros, Tovar, Gabriel Ibrahin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-09-2020; Elsevier
• Subjects: Big data; Climate change; Coastal; Machine learning; Perception; Climate change; Perception; Coastal; Big data; Machine learning
• ISSN: 2666-6839; 2666-6839
• DOI: 10.1016/j.geosus.2020.09.002

•A geospatiality analysis approach is used to study the climate change perception.•Scientific literature on climate change perception from big data database.•Analysis of topic trends for language, countries, and research.•A network of most frequent keywords to identify hotspot topics of research.•Highlighted of stronger research designs and methods for use in future studies.
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.
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