Wanting Zhao

With rural revitalization as China’s national policy priority, rural tourism has been promoted as a pathway to stimulate local economies and advance revitalization objectives. National and local governments have introduced policy measures to support rural tourism development. However, the spatial correspondence between documented policy attention and tourism popularity remains uneven and insufficiently understood. To address this gap, this study develops an integrated evaluation framework combining a text-based measure of documented policy attention, denoted PII, and a Tourism Popularity Index (TPI) using multi-source data from 920 peri-urban villages in Shanghai with rural tourism destination POIs. Spatial analysis reveals disparities between tourism popularity and documented policy attention, and further examines the contextual characteristics associated with this misalignment. The findings indicate that: (1) documented policy attention and rural tourism popularity exhibit pronounced spatial disparities, with high-popularity clusters emerging where favorable resource conditions, accessibility, and higher levels of policy attention coexist; (2) widespread policy–popularity mismatches indicate limited correspondence between the two measures; (3) the selected cases illustrate differences in spatial planning, operational capacity, and market-oriented strategies, cautioning against uniform policy expansion. The study offers a spatially explicit framework for assessing policy–popularity alignment and informing context-sensitive rural tourism governance in highly urbanized regions.

The COVID-19 pandemic highlighted the role of urban parks as green spaces in mitigating social isolation and supporting public mental health. Research in this area is limited due to the lack of large-scale datasets. Moreover, timely studies are indeed necessary under pandemic conditions. This study employs quantitative methods to analyze the temporal and spatial changes in social interaction in 160 urban parks before, during, and after the COVID-19 pandemic, and assesses their correlation with the built environment. Social media data from the Dianping platform were collected for this purpose. A two-step analytical approach was employed: first, machine learning-based keyword analysis identified review data related to social interaction, leading to the construction of two indicators: social interaction intensity and social interaction recovery rate. Second, we applied regression models to explore the correlation between the two indicators in urban parks and 18 characteristics of the built environment. The built environment characteristics associated with social interaction intensity varied across different periods, with seven factors, including natural landscapes, perceptual experience, building density, and road intersections, showing significant correlations with the recovery of social interaction capabilities in the post-pandemic era. Based on these findings, it is recommended that urban planners consider integrating more flexible design element, such as adding greenery and enriching the audio-visual experience for visitors. Furthermore, enhancing the quality and accessibility of park amenities can foster social interaction, thereby contributing to public health resilience in future crises. This research recommends that urban park design should not only support communities’ immediate needs but also prepare for unforeseen challenges.