Public transportation serving urban tourism has a positive impact on sustainable urban development. With the rapid rise of “subway tourism” in China and the emergence of numerous popular attractions, the pedestrian spaces connecting subway stations and attractions are important public spaces for tourists’ perception. Identifying, cognizing, and perceiving the pedestrian spaces around subway stations near popular tourist attractions from the tourists’ perspective holds significant value for promoting station–city coordination and enhancing spatial quality. This paper establishes an optimization design framework for the pedestrian spaces around subway stations near urban popular tourist attractions, moving from identification to cognition and perception. Taking Tianjin, China as an example, we collected 11,405 travelogue data entries and street network data of the subway stations around popular attractions. (1) We constructed an LDA topic model to identify popular tourist attractions; (2) we applied space syntax to understand the features and forms of the pedestrian spaces around subway stations; (3) we utilized the ROST-CM network text analysis method to analyze tourists’ overall perception of the pedestrian spaces around subway stations; (4) we proposed suggestions for optimization. The contribution of this study lies in constructing a vertical analytical framework that transitions from “identification” to “cognition” to “perception”. The cognitive and perceptual dimensions can mutually corroborate some of their findings but are not interchangeable. Future urban spatial optimization research should consider both cognitive and perceptual dimensions, enhancing the comprehensiveness of the human-centered perspective. The research results provide empirical references and guidance for the management and practice of urban space renewal around subway stations near major cities’ tourist attractions.

This study aims to investigate the spatial distribution and structural characteristics of urban greening in Beijing, focusing on three typologies: Single Tree (S-T), Tree–ush (T-B), and Tree–Bush–Grass (T-B-G). The analysis examines how socio-economic factors and the COVID-19 pandemic have influenced these structures across three time periods: pre-pandemic, during the pandemic, and post-pandemic recovery. To achieve this, a deep learning-based approach utilizing the DeepLabV3+ neural network was applied to analyze the features extracted from Baidu Street View (BSV) images. This method enabled the precise quantification of the structural characteristics of urban greening. The findings indicate that greening structures are significantly influenced by commercial activity, population mobility, and economic conditions. During the pandemic, simpler forms like S-T proved more resilient due to their lower maintenance requirements, while complex systems such as T-B-G experienced reduced support. These results underscore the vulnerability of green infrastructure during economic strain and highlight the need for urban greening strategies that incorporate flexibility and resilience to adapt to changing socio-economic contexts while maintaining ecological and social benefits.

The mountainous environment of Nanjing, China, has historically held significant importance. However, many of its mountains today face the challenges of being covered by urban development and the loss of their historical names. To protect these mountains and their names, it is essential to understand their history. However, most historical mountain names found in local gazetteers cannot be directly established to have corresponding relationships with contemporary mountains. This paper used a variety of historical materials and examined the geographical locations of 108 mountains in the core area of Nanjing in the Southern Song Dynasty (1127–1279), categorized into five situations. It created a map of mountain names and distinguished the deviations between ‘cognitive geography’ and ‘real geography’. This paper revealed that in the Southern Song, the perceived mountains formed six distinct distribution belts, forming an inner and an outer layer. Three mountain belts in the inner layer were found to form a Y-shaped structure with the city, creating three types of city-mountain relationships. It also uncovered the reasons for the deviations between the local gazetteer records and the actual geographic situation. These deviations were mainly caused by the scholars’ method of measuring distances along routes, which also contributed to the misplacement of place names in historical maps.