武田 史朗

We employed publicly available user-generated content (UGC) data from the website Tripadvisor and developed an autoregressive integrated moving average (ARIMA) model using the R language to analyze the seasonality of the use of urban green space (UGS) in Okinawa under normal conditions and during the COVID-19 pandemic. The seasonality of the use of ocean-area UGS is primarily influenced by climatic factors, with the peak season occurring from April to October and the off-peak season from November to March. Conversely, the seasonality of the use of non-ocean-area UGS remains fairly stable throughout the year, with a relatively high number of visitors in January and May. The outbreak of the COVID-19 pandemic greatly impacted visitor enthusiasm for travel, resulting in significantly fewer actual postings compared with predictions. During the outbreak, use of ocean-area UGS was severely restricted, resulting in even fewer postings and a negative correlation with the number of new cases. In contrast, for non-ocean-area UGS, a positive correlation was observed between the change in postings and the number of new cases. We offer several suggestions to develop UGS management in Okinawa, considering the opportunity for a period of recovery for the tourism industry.

Understanding the relationship between environmental features and perceptions of urban green spaces (UGS) is crucial for UGS design and management. However, quantifying park perceptions on a large spatial and temporal scale is challenging, and it remains unclear which environmental features lead to different perceptions in cross-cultural comparisons. This study addressed this issue by collecting 11,782 valid social media comments and photos covering 36 UGSs from 2020 to 2022 using a Python 3.6-based crawler. Natural language processing and image recognition methods from Google were then utilized to quantify UGS perceptions. This study obtained 32 high-frequency feature words through sentiment analysis and quantified 17 environmental feature factors that emerged using object and scene recognition techniques for photos. The results show that users generally perceive Japanese UGSs as more positive than Chinese UGSs. Chinese UGS users prioritize plant green design and UGS user density, whereas Japanese UGS focuses on integrating specific cultural elements. Therefore, when designing and managing urban greenspace systems, local environmental and cultural characteristics must be considered to meet the needs of residents and visitors. This study offers a replicable and systematic approach for researchers investigating the utilization of UGS on a global scale.

Chinese Traditional Gardens (CTGs) are an important part of China’s cultural inheritance from the past. Today’s China has experienced rapid urbanization, raising the need for a new form of contemporary gardens intended to satisfy peoples’ need for traditional culture. Garden paths are important in CTGs; they are designed to show visitors changing views with each step, and to lead them to secluded, quiet places via winding paths. This enhances the ornamental interest of the gardens. Based on plane graphics, this study evaluates the characteristics of three types of garden paths in fourteen traditional gardens and a contemporary antique garden, the Daguan Garden in Beijing. The analysis uses correlation and factor analysis to integrate 28 quantitative path indicators into five aspects of average, scale, network, wide, and aggregation. The 28 indicators can be expressed by six simple indicators: average connection length, number of path sections, alpha index, average width, average tortuous angle, and concentration degree. The results show small variations of garden paths between traditional gardens, but a considerable difference between the contemporary garden and traditional gardens. The research proposes a framework for the quantification and comparison of garden path features that can be applied before and after garden path construction, for both ancient and modern garden styles. This framework generates garden path feature values and theoretical values of six indicators, and is not constrained by the garden scale. Therefore, it provides an accurate and efficient design tool for garden designers.