Residential sentiments and spatial environments in urban villages: An X-minute community life circle analysis

Abstract

Urban villages, a unique residential typology of China's rapid urbanization, and their residents' sentiments have become a prominent issue in the development and management of Shenzhen. While numerous studies have investigated the impact of the urban environment on residents' sentiments, most have focused on the general population and used administrative units, neglecting environmental impacts across spatial scales based on daily mobility. To address these research gaps, this study employed multi-source big data and a large language model to assess residents' sentiments and evaluate environmental features within 5-, 10-, and 15-min community life circles in the urban villages of Shenzhen. Hierarchical regression and ridge regression were used to explore the key environmental features affecting urban village residents' sentiments. The findings indicated that the sentiment scores of urban village residents were significantly lower than those of the general population, while low sentiment scores were concentrated in suburban areas. Further, the environmental variables within the 10-min community life circle had the greatest influence on sentiments for urban villagers. Specifically, subway stations and green spaces contributed positively to residents' sentiments, while industrial parks and building density/height had a negative impact. These findings advance urban research by integrating activity-space theory with big data analytics, deepening the understanding of how spatial environments shape marginalized groups' sentiments. The results also provide an empirical foundation for optimizing urban renewal policies to improve the quality of life and mental well-being of urban village residents through the development of more livable and healthier urban environments.