Leilei Sun

The limited capacity of urban green spaces to equitably satisfy the well-being needs of populations in urbanized areas is a global challenge. However, research on the spatial mismatch between green space equity and residents’ subjective well-being (SWB) remains inadequate. Using Shanghai as a case study, this research integrates social media data with an improved GA2SFCA method to evaluate SWB and UPGS accessibility and analyzes and compares the geographical spatial distribution differences of UPGS accessibility across different travel modes. This study employs machine learning to reveal the potential drivers of the mismatch between SWB and UPGS accessibility (note that this study does not explore causal relationships). The results indicate that: (1) UPGS accessibility in Shanghai exhibits pronounced spatial heterogeneity, the equity results derived from the Lorenz curve and Gini coefficient indicate that public transit (Gini = 0.579) < walking (0.427) < driving (0.149), and community parks effectively mitigating disparities among other urban park types; (2) UPGS accessibility and SWB are spatially correlated (r = 0.013, p < 0.01, z > 2.58), with a distinct High-High clustering pattern identified in the inner-ring region; (3) Road network accessibility (SHAP = 0.9478), housing prices (0.7025), and company agglomeration (0.5695) are the three most influential factors contributing to the spatial mismatch where SWB is higher than accessibility, and they exhibit clear threshold effects. These findings link urban green space equity with residents’ SWB, providing a basis for targeted interventions to enhance social welfare and promote urban sustainability.

Town development, a crucial stage of urbanization, has been increasingly prioritized in recent sustainable socio-economic growth strategies. Vitality, especially the one measured by aggregate crowd behaviors, is widely recognized as a crucial development element. Conducting comprehensive assessments of the drivers of town vitality, particularly crowd vitality, is thus essential for addressing challenges and monitoring progress. This study examines representative towns in China and employs multiple datasets along with XGBoost-SHAP to investigate the mechanisms of development environment factors on aggregate crowd vitality. Key findings highlight the study’s novelty and broader implications: (1) The degree of industrial agglomeration is the most significant factor impacting the dependent measures, providing new data-driven insights into the role of economic clustering in town development. (2) Other indicators, such as the minimum distance to the town center, the enclosure, and car and pedestrian friendliness, can effectively predict town vitality, offering practical considerations for town planning. (3) Industrial innovation and diversification, rational planning of living circles, and enhancement of town conditions emerge as three crucial strategies for promoting urbanization. This study enhances empirical insights with strategies for addressing urbanization challenges, emphasizing how crowd data can be used to inform urbanization policies and planning practices, aiding urban planners in building more sustainable systems.