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.

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.