The issue of how to realize the coordinated development of various elements in human–land systems, or, in other words, how to achieve the coordinated development of population-economy-society-resource-environment (PESRE) systems, has become an important topic, which has received global attention. This study takes 31 provinces in China as the research objects, and carries out the research on the spatial–temporal synthetic measurement of the coordinated development of PESRE systems. The conclusions are as follows. From 1995 to 2015, the process of change of coupling coordination degree of China’s PESRE systems can be divided into two types: Rising first and then declining, and fluctuant continuously. The number of provinces of the first type was higher, and most provinces were on the verge of uncoordinated development status or in a weakly coordinated development status. The coupling degree of PESRE systems at the provincial level in China generally shows some positive spatial correlations, and the level of coordinated development displays some obvious spatial aggregation patterns. Moreover, the degree of such aggregation first increases and then weakens. The eastern parts of China represent the main “high-high” type aggregation regions. The central and western parts of China represent the main “low-low” types, account for the largest proportion, and display obvious aggregation characteristics.

Since the 1990s, the notion of a circular economy has been developing globally; countries all over the world have been considering the development of a circular economy as an important means of achieving sustainable development. As the development of an industrial circular economy can help promote the efficient recycling of resources, it is an important starting point for industrial transformation and upgrading, and represents a key factor that will lead to the development of a circular economy in China. China’s varying provinces (municipalities and autonomous regions) have successively implemented circular economy practices in the industrial field. The research object of the present study is 30 provinces, autonomous regions, and municipalities directly under the control of central government (Hong Kong, Macao, Taiwan, and Tibet were not included owing to lack of data). Through the integration of geographic information systems (GIS) technology and the spatial analysis model, data envelopment analysis (DEA) model, and Tobit regression model, a measure model and index system are constructed, in order to carry out a multi-angle comprehensive study integrating the efficiency evaluation, spatial analysis, and influencing factors analysis of China’s industrial circular economy. It is an important innovation, and an important contribution to the existing research system. The conclusions are as follows: (1) In general, the overall level of China’s industrial circular economy’s efficiency was not high, and there was still a lot of room for improvement. The integrated efficiency of the industrial circular economy in the eastern region was relatively high, followed by that in the western region, and the lowest level in the middle region. (2) The efficiency of China’s industrial circular economy displayed obvious spatial aggregation characteristics at the provincial level, including clear spatial dependence and spatial heterogeneity. High-value aggregation areas were mainly distributed in the eastern coastal areas, and low-value aggregation areas were concentrated and contiguously distributed in the middle and western inland areas. (3) The four elements of economic level, openness to the outside, government regulation, and industrialization aggregation each impose a significant positive impact on the efficiency of China’s industrial circular economy, which can promote its efficiency. The level of industrialization exerts a significant negative impact on the efficiency of the industrial circular economy, which hampers its improvement. The impact of technological innovation on the efficiency of the industrial circular economy is not statistically significant.

Health is the basis of a good life and a guarantee of a high quality of life. Furthermore, it is a symbol of social development and progress. How to further improve the health levels of citizens and reduce regional differences in citizens’ health status has become a research topic of great interest that is attracting attention globally. This study takes 31 provinces (municipalities and autonomous regions) of China as the research object. Through using GIS (Geographic Information System) technology, the entropy method, spatial autocorrelation, stepwise regression, and other quantitative analysis methods, measurement models and index systems are developed in order to perform an analysis of the spatio-temporal comprehensive measurements of Chinese citizens’ health levels. Furthermore, the associated influencing factors are analyzed. It has important theoretical and practical significance. The conclusions are as follows: (1) Between 2002 and 2018, the overall health levels of Chinese citizens have generally exhibited an upward trend. Moreover, for most provinces, the health levels of their citizens have improved dramatically, although some provinces, such as Tianjin and Henan, showed a fluctuating downward trend, suggesting that the health levels of citizens in these regions displayed a tendency to deteriorate. (2) The health levels of citizens from China’s various provinces showed clear spatial distribution characteristics of clustering, as well as an obvious spatial dependence and spatial heterogeneity. As time goes by, the degree of spatial clustering with regard to citizens’ health levels tends to weaken. The health levels of Chinese citizens have developed a certain temporal stability, the overall health status of Chinese citizens shows a spatial differentiation of a northeast–southwest distribution pattern. (3) The average years of education and urbanization rate have a significant positive effect on the improvement of citizens’ health levels. The increase of average years of education and urbanization rate can promote the per capita income, which certainly could help improve citizens’ health status. The Engel coefficient, urban–rural income ratio, and amount of wastewater discharge all pose a significant negative effect on the improvement of citizens’ health levels, these three factors have played important roles in hindering the improvements of citizen health.

Health is the basis of human survival and development. It is not only related to quality of life but also guarantees national security and social stability. Under the combined influence of various factors, large regional differences exist with regard to the health levels of residents in the Yellow River Basin (YRB). Here, we took 73 prefecture-level cities (leagues and prefectures) in the YRB as our research object. We constructed an index system and a measurement model and applied geographical information system (GIS) technology and quantitative analysis methods to make comprehensive spatial and temporal measurements of the health index of residents in the YRB and further analyzed the influencing factors. Overall, the health index of residents in the YRB showed a steady upward trend. However, some differences exist across various regions with regard to residents’ health index. The YRB resident health index displayed positive spatial autocorrelation; spatial clustering showed an initial decrease, followed by an increase, suggesting notable fluctuations. With the increase in per capita GDP, urbanization rate, and household size, the health index of residents in the YRB has improved. However, increased wastewater and waste gas discharge has led to a decrease in the health index of residents in the YRB.