The spatial distribution of Leisure Urban Spaces (LUSs) is closely linked to urban sustainability and residents’ quality of life. This study uses the Central Urban Area of Nanjing as the study area. Using POI and AOI data, the locations of LUS were precisely identified and categorized, including parks, squares, waterfront spaces, and leisure blocks. GIS spatial analysis methods, the nearest neighbor index, standard deviation ellipse, and kernel density estimation were used to analyze these spaces’ form, directivity, and density. Population activity intensity (PAI) data at various time points, collected by Baidu heat map, are correlated with LUS distribution through multiple linear regression analysis. (1) Parks and squares exhibit significant clustering tendencies, whereas waterfront spaces show weaker clustering, and leisure blocks are randomly distributed; (2) The central points of all types of LUS are located in the city center, extending from southeast to northwest, with parks and squares offering a broader range of services; (3) The overall LUS layout shows a ‘core and multiple points’ structure, with varying density patterns across different spaces, indicating concentrated and dispersed leisure areas; (4) The correlation between LUS distribution and PAI strengthens throughout the day and is greater on weekends than weekdays. Leisure blocks significantly enhance activity intensity, while parks have a limited effect, and waterfront spaces often show a negative correlation due to their remote locations. These results provide insights for future urban planning in Nanjing and underscore patterns in residents’ leisure activities.

Transitional streets serve as intermediary spaces between the Central Business Districts (CBDs) and surrounding residential areas, offering diverse functions and activities within urban interiors. However, a practical methodology for accurately classifying these streets has been lacking, due primarily to transitional areas’ spatial constraints and functional complexities. This study leverages Point of Interest (PoI) data from 2023 to develop an innovative methodological framework that addresses these challenges. This framework analyses transitional streets’ functional distribution and typology, employing PoI frequency density and functional type ratios to identify and classify functional zones. It generally delineates eight main types of transitional streets in the CBD of Chongqing, a prototypical hillside city. Utilising advanced data technology from internet maps, this research pioneers new approaches for identifying and analysing the functionality of transitional streets. The findings underscore the effectiveness of PoI data in precisely recognising the functional types of transitional streets, thereby providing a robust theoretical and practical foundation for the in-depth study of transitional streets. Moreover, the results enhance urban spatial planning in hillside cities of China, effectively demonstrating the advantages of PoI data in defining street typology compared to traditional methods. This approach provides a more detailed understanding of urban functional dynamics by allowing for a more nuanced data analysis of street functions.

This empirical study examines how artificial intelligence-enabled heritage intelligence (AIHI) and green technology integration (GTI) influence sustainable heritage development performance (SHDP) through participatory heritage governance (PHG) in Kaifeng. A quantitative design was adopted using a structured questionnaire. Anonymized data with a sample size of 360 were collected via five-point Likert-scale responses from heritage-governance stakeholders in Kaifeng. Measurement properties and structural relationships were examined using partial least squares structural equation modeling with bootstrapping procedures. AIHI and GTI both exert significant positive effects on PHG. PHG significantly improves SHDP and partially mediates the relationship between AIHI and sustainability outcomes. The interaction between PHG and GTI is positive, indicating that green technologies reinforce the link between governance processes and sustainability performance. This study connects smart-city governance literature with emerging research on heritage governance and green transition by identifying PHG as the governance mechanism through which AI and green technologies contribute to sustainable outcomes. The findings provide guidance for coordinating AI applications, green-retrofitting initiatives, and stakeholder participation within a heritage-sensitive governance structure.

In rapid urbanization, the socio-spatial environment between inner-city functional areas faces numerous challenges. Assessing and enhancing the environmental quality of these areas has become an urgent research issue. This study quantitatively evaluates the social-spatial environment of inner-city transitional edges, selecting Chongqing’s Yuzhong District as the case study area. It explores the relationship between spatial environmental factors and social activities. Integrating spatial data, internet “big” data, and field survey data, a multidimensional evaluation of the quality of the social-spatial environment framework is constructed, encompassing four dimensions: connectivity, social function, comfort, and conviviality. Subsequently, a multiple linear regression model is used to explore the main environmental factors influencing social activities on transitional edges. The results show that the density of street trees, lighting facilities, functional density, and functional diversity significantly impact social activities, demonstrating the correlation between the spatial environment of inner-city transitional edges and social activities. Corresponding optimization strategies for each dimension in transitional edges are then summarized. This study provides references for coordinating inner-city functional areas, optimizing urban environments, and promoting sustainability. It can also be applied to a broader range of transitional edge evaluation studies.