Coupling mechanisms and optimization of urban green space equity and income distribution based on multi-source big data: A case study of beijing

Urban green spaces are essential components of green infrastructure and ecosystems, playing a vital role in environmental sustainability and residents’ well-being. With socioeconomic development and rising living standards, public demand for better-quality and more equitably distributed green space has increased. However, most studies focus on large and medium-sized parks, overlooking the role of small green spaces in spatial equity. Using Beijing as a case study, this research integrates multi-source big data to evaluate green space equity through the Gaussian Two-Step Floating Catchment Area (G2SFCA) method. It combines income distribution modeling, Lorenz curves, and Gini coefficients, and applies the Coupling Coordination Degree Model (CCDM) to explore the spatial coupling between green space equity and residents’ income distribution. Results show that: (1) Green space distribution in Beijing is notably uneven, with a population-weighted Gini coefficient of 0.3388. The top 10% of the population by township has access to only about 0.6% of green space, while the top 10% of green space–rich areas accommodate 25–45% of residents. In outer suburbs and ecological zones, a small population occupies most green resources. (2) Income distribution is also highly unequal, with a Gini coefficient of 0.7883. The lowest 10% of the population holds merely 0.01–0.6% of total income, while the highest-income 10% captures 25–45%. Income is relatively balanced in central districts but highly concentrated in the periphery. (3) Coupling between green space equity and income distribution varies spatially. Among 331 townships, 19.9% show high coordination—mainly in the southeast and northwest—while 40.1% are poorly coordinated, mostly in northern, central, and edge areas. Coupling coordination correlates positively with green space equity and less strongly with income. Quadrant analysis indicates that about 70% of townships exhibit a “high–low mismatch,” reflecting social–ecological imbalance. The study suggests optimizing green space layout in outer suburbs and low-coordination areas, enhancing public green space provision, and promoting more balanced income distribution to support sustainable urban development and equitable access to green infrastructure.