The first professional night-time light remote sensing satellite in China, Luojia1-01, has raised the resolution of night-time light data to 130 m, which provides a possibility for the study of small-scale night-time light. This paper is the first research on spatial analysis and quantitative modeling between night-time light intensity (NTLI) and community housing price (CHP) on a small scale by using the Luojia1-01 night-time light imagery. This paper takes Wuhan as the research area, CHP data obtained by web-crawler technology as the research object, combines Luojia1-01 data, and carries out spatial correlation analysis and quantitative modeling on a small scale for them. The experimental results show that there is a strong linear positive correlation between the NTLI and CHP based on geographically weighted regression (GWR), and the CHP data in Wuhan have obvious spatial non-stationarity. Moreover, the coupling mechanism between the NTLI and CHP is also revealed. We can conclude that there is potential for estimating the CHP by using Luojia1-01 night-time light imagery.

The development of ecotourism in protected areas faces the challenge of balancing conservation and ecotourism. Ecotourism suitability assessments are essential tools for managing tourism in these areas. However, current assessments often overlook biological factors, leading to adverse effects on wildlife. This study uses the Northeast China Tiger and Leopard National Park as a case study to establish a comprehensive assessment system that integrates ecotourism suitability with tiger and leopard habitat suitability, thereby linking ecotourism with wildlife conservation. The primary research methods include ecotourism suitability analysis based on the entropy weight method and habitat suitability analysis using the MaxEnt model. Based on the zoning results of ecotourism and habitat suitability, a comprehensive ecotourism suitability zoning map was produced. This map indicates that areas of very high suitability account for 45.62% of the total area, covering approximately 6152.563 km², and are primarily located on the edges of village clusters. These areas can be prioritized for developing tourism infrastructure. The comprehensive ecotourism assessment system can balance the development of ecotourism with wildlife conservation, contributing significantly to the coordinated development of economic, social, and environmental objectives.

The impact of building the Belt and Road on the ecological environment and the health of the related cities along this belt deserves more attention. Currently, there are few relevant pieces of research in this area, and the problem of a time lag between the ecological environment and health (e.g., life expectancy, LE) has not been explored. This paper investigates the aforementioned problem based on five ecological indicators, i.e., normalized difference vegetation index, leaf area index, gross primary production (GPP), land surface temperature (LST), and wet, which were obtained from MODIS satellite remote-sensing products in 2010, 2015, and 2020. The research steps are as follows: firstly, a comprehensive ecological index (CEI) of the areas along the Belt and Road was calculated based on the principle of component analysis; secondly, the changes in the trends of the five ecological indicators and the CEI in the research area in the past 11 years were calculated by using the trend degree analysis method; then, the distributions of the cold and hot spots of each index in the research area were calculated via cold and hot spot analysis; finally, the time lag relationship between LE and the ecological environment was explored by using the proposed spatiotemporal lag spatial crosscorrelation analysis. The experimental results show that ① there is a positive correlation between LE and ecological environment quality in the study area; ② the ecological environment has a lagging impact on LE, and the impact of ecological indicators in 2010 on LE in 2020 is greater than that in 2015; ③ among the ecological indicators, GPP has the highest impact on LE, while LST and Wet have a negative correlation with LE.

With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.

China’s accelerating pace of urbanization has placed severe pressure on its ecosystems. Hence, the monitoring and assessment of eco-environmental quality has significant implications for sustainable urban development. By introducing a pollution index, a modified remote sensing ecological index (MRSEI) was constructed to more comprehensively evaluate the spatiotemporal distribution of the eco-environment quality in the middle reaches of the Yangtze River where urbanization has been developing rapidly. Future trends in eco-environmental quality were analyzed using Theil–Sen trend analysis, the Mann–Kendall test, and the Hurst exponent. Environmental influencing factors were also analyzed. Our results show that: (1) The impact of pollution factors on urban agglomerations cannot be overlooked. The MRSEI model introduces a pollution indicator to better assess the eco-environmental quality of urban agglomeration areas. (2) The eco-environmental quality is high in the south and east and low in the north and west, with overall levels ranging between moderate and good. (3) The eco-environmental quality remained stable, improved, and degraded in 86.3%, 3.1%, and 10.7% of the study area, respectively. (4) The land use and land cover type are directly related to the eco-environment. Climate factors indirectly affect the eco-environment. Human activities in cities and urban peripheries lead to land use changes and industrial pollution, which significantly affect environmental quality.