Big-Data-Based Text Mining and Social Network Analysis of Landscape Response to Future Environmental Change

Abstract

Climate change impacts the urban environment and landscape changes worldwide. To understand how South Korean citizens perceive these changes and what they expect for the future landscape, this study analyzed urban residents’ perceptions through text mining. Data related to the keywords “future landscape”, “future environment”, “well-being”, and “climate change” were collected from July 2020 to July 2021 from the Korean search engines Naver, Daum, and Google using the tool TEXTOM. Keywords, importance, and related words were derived through word frequency, TF-IDF, and N-gram analysis. CONCOR analysis was used to derive the meaning and relevance of the words. In “future landscape”, results showed a high frequency of the words “complex”, “apartment”, “future value”, and “sale”, and the connection strength was higher between “complex”, “landscape”, and “future value”. In “future environment”, “eco-friendly” showed the highest word frequency, and the words “New Deal”, “hydrogen” and “mobility” showed a high frequency and correlation. For “well-being”, “Green Cross” (a well-being-related company) showed the highest frequency, and the connection strength between satisfaction indexes was high. For “climate change”, “response” showed the highest frequency, and the connection strength between “carbon-neutral”, “UN-convention”, and “plan-establishment” was high. These results showed that South Koreans associate landscaping with the value of apartment complexes, that they expect solutions to mitigate climate change impacts with green and eco-friendly strategies, and lastly that well-being-related companies are receiving a great deal of public attention. Thus, it is expected that the results will help plan effective landscaping approaches to respond to environmental changes.