Hourly and 0.5-Meter Green Space Exposure Mapping and Its Impacts on the Urban Built Environment

Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks. More than 68% of residents in high-priced blocks experienced over 8 h of green space exposure during weekend nighttime (especially around 19:00), which was much higher than low-price blocks. Moreover, spatial inequality in residents’ green space exposure was more pronounced on weekends than on workdays, with lower-priced blocks exhibiting greater inequality (Palma ratio: 0.445 vs. 0.385). Furthermore, green space morphology, quantity, and population density were identified as the critical factors affecting DRGE. The optimal threshold for Percent of Landscape (PLAND) was 25–70%, while building density, height, and Sky View Factor (SVF) were negatively correlated with DRGE. These findings address current research gaps by considering population mobility, capturing green space supply and demand inequities, and providing scientific decision-making support for future urban green space equality and planning.