The equitable distribution of public health facilities is a major concern of urban planners. Previous studies have explored the balance and fairness of various medical resource distributions using the accessibility of in-demand public medical service facilities while ignoring the differences in the supply of public medical service facilities. First aid data with location information and patient preference information can reflect the ability of each hospital and the health inequities in cities. Determining which factors affect the measured differences in public medical service facilities and how to alter these factors will help researchers formulate targeted policies to solve the current resource-balance situation of the Ministry of Public Health. In this study, we propose a method to measure the differences in influence among hospitals based on actual medical behavior and use geographically weighted regression (GWR) to analyze the spatial correlations among the location, medical equipment, medical ability, and influencing factors of each hospital. The results show that Wuhan presents obvious health inequality, with the high-grade hospitals having spatial agglomeration in the city-center area, while the number and quality of hospitals in the peripheral areas are lower than those in the central area; thus, the hospitals in these peripheral areas need to be further improved. The method used in this study can measure differences in the influence of public medical service facilities, and the results are consistent with the measured differences at hospital level. Hospital influence is not only related to the equipment and medical ability of each hospital but is also affected by location factors. This method illustrates the necessity of conducting more empirical research on the public medical service supply to provide a scientific basis for formulating targeted policies from a new perspective.

Given the challenges of rapid urbanization and ecological degradation, construction land reclamation (CLR) has become one of the critical strategies for optimizing land use and balancing urban development with ecological preservation. However, the long-term impacts and patterns of CLR remain underexplored, necessitating a comprehensive analysis to inform sustainable land management practices. This study investigates the patterns and impacts of CLR in Hangzhou over a fifteen-year period (2006–2020). Using an improved K-means clustering algorithm, the study analyzes the land reclamation status of 199 streets, identifying nine distinct categories that reflect varying stages of urbanization, ecological protection, and land-use management. The results show a clear transition from rapid, large-scale reclamation during the early years to more targeted and sustainable reclamation efforts in later periods. The study’s innovation lies in its use of a long-term, time-series analysis at the street level, enhancing understanding of CLR dynamics and improving prediction accuracy by 11%. It emphasizes the importance of adaptive land management in balancing urban growth with ecological preservation and agricultural sustainability. The clustering analysis provides insights into the evolving urbanization process, with the highest concentration of construction land reclamation occurring along urban–rural boundaries. These findings offer valuable lessons for urban planning and sustainable urbanization strategies.

With the sustainable and coordinated development of cities, the formulation of urban street policies requires multiangle analysis. In regard to the existing street research, a large number of studies have focused on specific landscapes or accessibility of streets, and there is a lack of research on the multiple functions of streets. Recent advances in sensor technology and digitization have produced a wealth of data and methods. Thus, we may comprehensively understand streets in a less labor-intensive way, not just single street functions. This paper defines an index system of the multiple functions of urban streets and proposes a framework for multifunctional street measurement. Via the application of deep learning to Baidu Street View (BSV) imagery, we generate three functions, namely, landscape, traffic, and economic functions. The results indicate that street facilities and features are suitably identified. According to the multifunctional perspective, this paper further classifies urban streets into multifunctional categories and provides targeted policy recommendations for urban street planning. There exist correlations among the various street functions, and the correlation between the street landscape and economic functions is highly significant. This framework can be widely applied in other countries and cities to better understand street differences in various cities.