Multidimensional Evaluation of Crowd Activity Performance in Leisure Urban Spaces Using Network Big Data: A Case Study from Nanjing, China

Evaluating the performance of crowd activity in urban spaces is essential for effectively understanding residents’ leisure needs and preferences. Although existing research has provided some insights, it has not fully revealed the commonalities and differences in crowd activity in leisure urban spaces (LUSs). Addressing this gap, the study developed a multidimensional evaluation framework to deeply analyze various LUSs’ behavioral characteristics and patterns. This study used 11 sample spaces in the central urban area of Nanjing, China, including four parks, three squares, two waterfront spaces, and two leisure blocks. By integrating Baidu Heat Map data and social media reviews and employing methods including ArcGIS (version 10.8.2) spatial analysis, SPSS descriptive statistics (version 28), word frequency, and sentiment analysis, the study conducted a comprehensive evaluation across indicators including spatial clustering, temporal persistence, group diversity, and feedback positivity. The research reveals the performance of LUSs across different crowd activity dimensions, highlighting that leisure blocks excel in multiple dimensions, attracting many young people and families. Spatial distribution analysis reveals a gradual decrease from the city center to the outskirts in terms of spatial clustering and group diversity indicators. Temporal distribution analysis shows that squares are busiest during the day and leisure blocks are busiest at night, especially on weekends. The results of group diversity analysis indicate that parks and squares mainly attract families, while waterfront spaces balance the needs of families, friends, and individuals. Social media review analysis reveals users’ preferences for environmental atmosphere, convenience, and social factors. These findings provide new perspectives for understanding and assessing the use of urban spaces and offer crucial guidance for the optimization and development strategies of future LUSs.