Urban green space has significant social, ecological, cultural and economic value. This study uses social media data to examine the spatiotemporal utilization of major parks in Shanghai and explore the determinants of their recreational attraction. Methods: Based on microblog check-in data between 2012 and 2018 across 17 parks in Shanghai, we investigated the patterns at different temporal scales (weekly, seasonal and annual) and across workdays and weekends by using log-linear regression models. Results: Our findings indicate that both internal and external factors affect park utilization. In particular, the presence of sports facilities significantly contributes to higher visit frequency. Factors such as the number of subway stations nearby, scenic quality and popularity have a positive impact on check-in numbers, while negative factors affecting park use are number of roads, ticket price and average surrounding housing price. Across different temporal scales, the use patterns of visitors have obvious seasonal and monthly tendencies, and the differences of workday and weekend models lie in external factors’ impacts. Conclusions: In order to achieve the goal of better serving the visitors, renewal of urban green spaces in megacities should consider these influential factors, increase sports facilities, subway stations nearby and improve scenic quality, popularity and water quality. This study on spatiotemporal utilization of urban parks can help enhance comprehensive functions of urban parks and be helpful for urban renewal strategies.

Walking and running activities (W&RAs), encompassing strolling, slow walking, brisk walking, jogging, and running, hold significant importance as popular forms of exercise within urban parks. Recognized for their efficacy in promoting public health and preventing chronic diseases, understanding the nuanced impact of pathway features on W&RAs is crucial for advancing health-centric urban park planning. Based on extensive, high-frequency field observation data, we utilize multiple OLS regression models and univariate OLS regression models to investigate the relationship between urban park pathway features and variations in W&RAs, specifically examining activity density and intensity. Subsequently, we propose corresponding pathway optimization strategies. Our findings highlight the primary determinants, with vegetation coverage ratio, path type, and security facility density influencing activity density; and control value, time required to reach the nearest entrance, and pavement type influencing activity intensity. Significantly, increased vegetation coverage enhances density, while interconnected spaces and improved accessibility elevate intensity. In conclusion, our study delineates key features that merit prioritization, specifies their optimal ranges and proposes optimization design strategies for urban park pathways. By shedding light on these considerations, our research contributes valuable insights to the realm of health-oriented urban park planning and design.

With the continuous expansion of urban construction land, the green belts aiming for ecological protection have ensured a sustainable and effective function of regional ecosystem services. At the same time, these ecological green belts are expected to develop their compound service potentials with the development of cities. In order to meet the increasing demand of urban residents for the recreational utilization of urban green space, the primary function of the ecological green belts has transformed from being purely ecological to a combination of being ecological and recreational. Based on social media data, which has the characteristics of a large amount of accessible geographic information, this study used multiple regression models to analyze the recreational utilization intensity of ecological protection green belts with a case study in the green belt of Shanghai, China. The research results showed that the internal elements (total external area, water area, etc.) of the Shanghai green belt have positive correlations with its recreational utilization. The impact of external factors was inconclusive on the recreational utilization of the outer forest belt (the number of subway stations in accessibility factors was negatively correlated; the number of cultural facilities and the number of restaurants in the surrounding service facilities were positively related). Combined with the “Shanghai City Master Plan (2017–2035)”, this study suggests potential zones for the recreational transformation of the Shanghai green belt, provides a theoretical and practical basis for improving the recreational utilization of an urban ecological protection green belt and contributes to the sustainable development of ecological protection green belts in high-density cities.

Ancient canal towns in Jiangnan have become important tourist destinations due to their unique water town scenery and historical value. Creating a unique tourist image boosts these ancient towns’ competitive edge in tourism and contributes significantly to their preservation and growth. The vast amount of data from social media has become an essential source for uncovering tourism perceptions. This study takes two ancient towns in Shanghai, Zhaojialou and Fengjing, as case study areas. In order to explore and compare the destination images of the towns, in the perception of tourists and in official publicity, machine learning approaches like word embedding and K-means clustering are adopted to process the comments on Sina Weibo and publicity articles, and statistical analysis and correspondence analysis are used for comparative study. The results reveal the following: (1) Using k-means clustering, destination perceptions were categorized into 16 groups spanning three dimensions, “space, activity, and sentiment”, with the most keywords in “activity” and the fewest in “sentiment”. (2) The perception of tourists often differs significantly from the official promotional materials. Official promotions place a strong emphasis on shaping the image of ancient towns based on their historical resources, presenting a more general picture. Tourist perception, which is fragmented, highlights emerging elements and the experiential activities, along with the corresponding emotional experiences. (3) Comparing the two towns, Fengjing Ancient Town stands out, with more diverse tourist perceptions and richer emotional experiences. This underscores the effectiveness of tourism activities that use space as a media to evoke emotions, surpassing the impact of the spaces themselves.

Urban parks play a pivotal role in shaping physical activity (PA), yet our comprehension of the specific influence of park greenery remains limited. In this study, we focused on three Shanghai urban parks, conducting a comprehensive year-long on-site observation to delve into greenery’s impact on PA. By employing the hierarchical linear model analysis, we uncovered a robust correlation between internal and surrounding park greenery and PA density and diversity. Within a 400 m radius, indicators such as normalized difference vegetation index, green view ratio, Shannon’s diversity index, and color-leaf plant ratio demonstrated a negative relationship with PA density. Conversely, the green coverage ratio and green view ratio within the 400 m radius exhibited a positive link with PA density. In terms of PA diversity, factors such as green visibility rate, color-leaf plants, and green coverage ratio displayed negative associations, while the 400 m green view ratio showcased a positive impact. Notably, our study revealed a dual interplay between internal and surrounding greenery, manifesting as both collaborative and competitive dynamics that collectively shape PA. Additionally, we subjected greenery indicators to seasonal assessments, yielding insights that illuminate how urban park greenery shapes PA. These findings offer valuable guidance for future park planning and fostering environments conducive to PA.