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.

Conducting research on the spatial cognition of tourists in historical towns helps to balance cultural heritage protection and tourism development. However, the current tourist cognition research is not comprehensive enough in terms of data sources, time dimension, and spatial objects. This research takes Fengjing Ancient Town in Shanghai as an example, and through multi-source data analysis explores how tourists’ perception and cognition of the attractions changes, discusses the impacts of characteristic of spatial system and elements on perception, and then establishes a spatial cognition analysis framework involving time dimension, cognitive depth, and spatial type. On-site aerial photos, Sina Weibo check-in data, tourist memory maps, and photos from tourism websites were used to classify tourists’ spatial cognition through content analysis, theme classification, and GIS spatial analysis. This research finds that tourists have formed three cognitive levels in the travel process, from “initial spatial consciousness” to “place memory” then to “imagery perception”. Meanwhile, space is the most important object of tourists’ cognition, and it is also the carrier of other intangible cultures. In terms of spatial cognition and ancient town tourism, this research finds the tourists’ spatial cognition of Fengjing Ancient Town is related to the main river and main tourist routes that represent the image characteristics of the ancient town. This research shows that clear boundaries of tourism space, richer folk activities, and more sequential tourism routes could help tourists form a more systematic spatial cognition. Based on the findings, this research also establishes an analysis and application framework of tourists’ multilevel spatial cognition to provide optimization suggestions for development of tourism.

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.

The suburbs around Shanghai have a complex river network and a unique Chinese water-town culture. The riparian landscape in the rural Qingxi area has important regional, ecological, and social significance; it serves as an important part of the local bioclimate, but the existing studies on river vegetation did not pay enough attention to the riparian landscape in the countryside around the metropolis. The goal of this study was to examine a comprehensive evaluation model for the river plant landscape in the countryside surrounding a high-density metropolis such as Shanghai in the face of the national policy of rural revitalization and the low-carbon development problem, and to propose optimization strategies accordingly. Therefore, in this study, we selected 91 rivers in the Qingxi area and investigated their plant communities. According to the characteristics of the riparian landscape and its relationship with the river environment and local bioclimate, we classified the 91 riparian landscapes into four types of quadrats: natural landscape, residential recreation, roadside linear landscape, and agricultural landscape. In addition, based on the 13 indicator layers under the categories of ecological carrying capacity, landscape beauty, and social service, we calculated the comprehensive evaluation value (CEV) and comprehensive evaluation index (CEI) of 91 river quadrants using specific formulas to scientifically evaluate the riparian landscape in the rural Qingxi area of Shanghai. Finally, based on the existing problems summarized through data analysis, the researchers proposed five optimization directions: (1) increasing vegetation diversity, (2) choosing native and culturally representative species, (3) improving waterfront planting design, (4) achieving ecological riverbank construction, and (5) building greenway systems and recreational spaces. This study proposed an innovative evaluation model for the riparian vegetation landscape and tested its feasibility by site survey, which provided new visions for future rural landscape research.