The impact of park environmental characteristics and visitor perceptions on visitor emotions from a cross-cultural perspective

Assessing the perception of urban parks and understanding the relationship between environmental characteristics and conflicts in park usage are crucial for the design and management of urban parks. However, quantifying perceptions of parks in large spaces poses challenges, and different regional cultures also exhibit variations in park usage. Therefore, this study aims to explore from a cross-cultural perspective how the environmental characteristics and perceptions of parks affect visitors' emotions. This research collected online comment data from Weibo check-ins in Shanghai and tweets in London between 2022 and 2023. It applied natural language processing (NLP) technology using SnowNLP for sentiment semantic keyword extraction, identifying the sentiment index of each data entry to quantify visitors' sentiments towards urban parks in Shanghai and London. Simultaneously, street view panoramas were obtained for semantic segmentation using the SegNet model and ADE20K to quantify spatial perceptions as independent variables. On this basis, we used nonlinear regression models and MGWR models to explore the relationship between visitors' sentiment values in parks and the park environment. The results showed that walkability in Shanghai was found to more easily bring positive sentiments to visitors, while visitors in London paid more attention to imageability. Walkability, accessibility, and imageability are of the same importance in Shanghai and London, but the correlation and emphasis of visitor perceptions differ, indicating potential conflicts in park usage among different user groups in the two cities' urban parks. Overall, our study offers differentiated planning suggestions that can provide information for urban design decisions, ultimately contributing to the improvement of human well-being. • Our study Introduces a quantitative method for perceiving urban parks. • The results used natural language processing technology to reveal park visitors' emotions. • Applies street view semantic segmentation for assessment of spatial quality. • Environmental factors have a greater impact on park emotions compared to perceptions.