Background: The recent global COVID-19 pandemic serves as another reminder that people in different urban neighborhoods need equal access to basic medical services. This study aims to improve the spatial accessibility of healthcare services toward the ‘15-minute city’ goal. Methods: We chose Zhengzhou, China, as a case study. To improve spatial accessibility, two optimization models of optimal supply-demand allocation (OSD) and the capacitated p-medina problem (CPMP) were used. Spatial accessibility in this study is defined as the walking time from the communities to healthcare centers. Results: For the current status of healthcare services at the community level, the mean travel time is 18.3 min, and 39.6% of residents can access healthcare services within a 15-minute travel time. Population coverage within a 15-minute walking time is significantly lower than the national target of 80%. After redefining the service areas through OSD allocation, the mean travel time was reduced to 16.5 min, and 45.1% of the population could reach services. Furthermore, the 60 newly proposed healthcare centers selected by the CPMP model could potentially increase by 35.0% additional population coverage. The average travel time was reduced to 10 min. Conclusions: Both the redefinition of the service areas and the opening of new service centers are effective ways to improve the spatial accessibility of healthcare services. Two methods of this study have implications for urban planning practices towards the 15-minute city.

Green space exposure is considered an important aspect of a livable environment and human well-being. It is often regarded as an indicator of social justice. However, due to the difficulties in obtaining green space exposure data from a ground-based view, an effective evaluation of the green space exposure inequity at the community level remains challenging. In this study, we presented a green space exposure inequity assessment framework, integrating the Green View Index (GVI), deep learning, spatial statistical analysis methods, and urban rental price big data to analyze green space exposure inequity at the community level toward a “15-minute city” in Zhengzhou, China. The results showed that green space exposure inequality is evident among residential communities. The areas in the old city were with relatively high GVI and the new city districts were with relatively low GVI. Moreover, a spatially uneven association was observed between the degree of green space exposure and housing prices. Especially, the wealthier communities in the new city districts benefit from low green space, compared to disadvantaged communities in the old city. The findings provide valuable insights for policy and planning to effectively implement greening strategies and eliminate environmental inequality in urban areas.

To better understand the possible role of projected land use and cover change (LUCC) in future regional climate projections, we explored the regional climate change response from land use/cover change under different climate scenarios. To do so, we propose a research framework based on different SSP-RCPs to simulate and explore the impacts of future land use/cover changes on the future climate of Zhengzhou City, China, using the Weather Research and Forecasting (WRF) model with land use/cover and meteorological data under different SSP-RCP scenarios based on CMIP6. Two scenarios, SSP2-4.5 and SSP5-8.5, were compared and analyzed by simulating changes in future climate factors of temperature at 2 m height above ground(T2) and precipitation. The results show that T2 is higher for all 4 months by the year 2060 compared to that in the year 2030. Furthermore, a comparison of the abovementioned years showed that the mean temperatures of January and July were higher than those of SSP2-4.5 under the SSP5-8.5 scenario in both years, but in 2030, the mean T2 of April and October were lower than those of SSP2-4.5 under the SSP5-8.5 scenario. In terms of precipitation, both scenarios have no significant precipitation in July in 2030 and 2060, but there is an unusual increase in January and October.

Mental health is one of the main factors that significantly affect one’s life. Previous studies suggest that streets are the main activity space for urban residents and have important impacts on human mental health. Existing studies, however, have not fully examined the relationships between streetscape characteristics and people’s mental health on a street level. This study thus aims to explore the spatial patterns of urban streetscape features and their associations with residents’ mental health by age and sex in Zhanjiang, China. Using Baidu Street View (BSV) images and deep learning, we extracted the Green View Index (GVI) and the street enclosure to represent two physical features of the streetscapes. Global Moran’s I and hotspot analysis methods were used to examine the spatial distributions of streetscape features. We find that both GVI and street enclosure tend to cluster, but show almost opposite spatial distributions. The Results of Pearson’s correlation analysis show that residents’ mental health does not correlate with GVI, but it has a significant positive correlation with the street enclosure, especially for men aged 31 to 70 and women over 70-year-old. These findings emphasize the important effects of streetscapes on human health and provide useful information for urban planning.