The urban heat island ($UHI$) refers to the land surface temperature (LST) difference between urban areas and their undeveloped or underdeveloped surroundings. It is a measure of the thermal influence of the urban built-up area expansion ($UBAE$), a topic that has been extensively studied. However, the impact of $UBAE$ on the LST differences between urban areas and rural areas ($UH{I}_{U-R}$) and between urban areas and emerging urban areas ($UH{I}_{U-S}$) in different seasons has seldom been investigated. Here, the $UH{I}_{U-S}$ and $UH{I}_{U-R}$ in 34 major metropolitan regions across China, and their spatiotemporal variations based on long-term space-borne observations during the period 2001–2020 were analyzed. The $UBAE$ quantified by the difference in landscape metrics of built-up areas between 2020 and 2000 and their impact on $UHI$ was further analyzed. The $UBAE$ is impacted by the level of economic development and topography. The $UBAE$ of cities located in more developed regions was more significant than that in less developed regions. Coastal cities experienced the most obvious $UBAE$, followed by plain and hilly cities. The $UBAE$ in mountainous regions was the weakest. On an annual basis, $UH{I}_{U-R}$ was larger than $UH{I}_{U-S}$, decreasing more slowly with $UBAE$ than $UH{I}_{U-S}$. In different seasons, the $UH{I}_{U-S}$ and $UH{I}_{U-R}$ were larger, more clearly varying temporally with $UBAE$ in summer than in winter, and their temporal variations were significantly correlated with $UBAE$ in summer but not in winter. The seasonal difference in $UH{I}_{U-R}$ was larger than that of $UH{I}_{U-S}$. Both the $UH{I}_{U-S}$ and $UH{I}_{U-R}$ in coastal cities were the lowest in summer, decreasing the fastest with $UBAE$, while those in mountain cities decreased the slowest. The change in the density of built-up lands was the primary driver affecting the temporal variations in $UH{I}_{U-S}$ and $UH{I}_{U-R}$ during $UBAE$, followed by changes in proportion and shape, while the impact of the speed of expansion was the smallest, all of which were more obvious in summer than in winter. The decreased density of built-up lands can reduce $UHI$. These findings provide a new perspective for a deeper understanding of the effect of urban expansion on LST in different seasons.

Accessibility to healthcare services is crucial for residents’ wellbeing. Numerous studies have revealed significant spatial inequality in healthcare accessibility across various contexts. However, it still remains unclear whether the inequality is caused by the unbalanced spatial distribution of healthcare facilities or by unequal transport access to them. This study decomposes inequality in healthcare accessibility into facility- and transport-driven inequality by comparing scenarios of healthcare accessibility, which consider various combinations of multidimensional components of accessibility using different distance measures. Using a case study in Shenzhen, this study reveals that both facility distribution and transport access substantially contribute to spatial inequality in healthcare accessibility. Facility distribution accounts for 61.3% and 50.8% of the overall accessibility inequality for driving and transit modes, respectively. The remaining inequality is induced by imbalanced mobility provided by transport networks. Furthermore, the impact of transport component on healthcare accessibility is unevenly distributed. This study highlights that both facility- and transport-related countermeasures should be considered to improve the accessibility and equality of healthcare services. It provides transferable methods for quantitatively decomposing facility- and transport-driven inequality in accessibility to healthcare or other facilities.

Equal accessibility to healthcare services is essential to the achievement of health equality. Recent studies have made important progresses in leveraging GIS-based location–allocation models to optimize the equality of healthcare accessibility, but have overlooked the hierarchical nature of facilities. This study developed a hierarchical maximal accessibility equality model for optimizing hierarchical healthcare facilities. The model aims to maximize the equality of healthcare facilities, which is quantified as the variance of the accessibility to facilities at each level. It also accounts for different catchment area sizes of, and distance friction effects for hierarchical facilities. To make the optimization more realistic, it can also simultaneously consider both existing and new facilities that can be located anywhere. The model was operationalized in a case study of Shenzhen, China. Empirical results indicate that the optimal healthcare facility allocation based on the model provided more equal accessibility than the status quo. Compared to the current distribution, the accessibility equality of tertiary and secondary healthcare facilities in optimal solutions can be improved by 40% and 38%, respectively. Both newly added facilities and adjustments of existing facilities are needed to achieve equal healthcare accessibility. Furthermore, the optimization results are quite different for facilities at different levels, which highlights the feasibility and value of the proposed hierarchical maximal accessibility equality model. This study provides transferable methods for the equality-oriented optimization and planning of hierarchical facilities.