Restorative Environment Characteristics of an Urban Forest Based on Big Data Analytics

Abstract

Since the COVID-19 pandemic, urban forests have become important restorative environmental spaces for which demand-customized management based on users’ experiences is needed. We collected 21,557 data points from blogs from January 2020 to December 2021. For data analysis, keyword frequency, term frequency–inverse document frequency, and sentiment analyses were conducted using TEXTOM 4.0, and a semantic linkage network was established and analyzed using Gephi 0.92. In the analyses, the restorative environment components of “being away”, “fascination”, “extent”, and “compatibility” were derived from users’ experiences. Fascination, which stems from natural objects such as rocks, valleys, and trails, was derived the most frequently, and being away and compatibility, representing leisure activities such as climbing and walking, formed the largest cluster in cluster analysis. Sentiment analysis revealed a high positive word rate of 91.6%, with favorable feelings accounting for 87.5%, whereas the proportion of joy and interest (12.5%) was relatively low. In addition, this study showed that hard fascinations such as sports, entertainment, and education are required to improve the experience quality in urban forests as restorative environments. Hence, the necessity of local government policies and projects is emphasized.