Classification-Based Greenspace Exposure Assessment Reveals Urban Cropland's Potential for Mitigating Spatial Inequality

We hypothesized that different green space types, particularly agricultural areas within urban environments, play varying roles in addressing green space access inequality, with seasonal variations significantly influencing exposure patterns. Our dataset examines green space exposure across multiple Chinese cities (2010-2020) using satellite-derived vegetation indices combined with population data to create exposure metrics. Data was gathered through monthly vegetation monitoring, land cover classification, and demographic mapping, then processed using population-weighted models to quantify how different communities access various green space types throughout seasons. This dataset can be interpreted to understand urban sustainability dynamics, guide green infrastructure planning, and assess environmental justice outcomes. Users can analyze city-level exposure patterns, examine grid-scale spatial distributions, evaluate inequality trends using provided coefficients, and explore optimization scenarios through included algorithms, making it valuable for urban planners, environmental researchers, and policy makers seeking evidence-based approaches to equitable green space distribution and sustainable city development.