Promoting the accessibility of basic public service facilities is key to safeguarding and improving people’s lives. Effective public service provision is especially important for the sustainable development of less developed regions. Lincang in Yunnan Province is a typical underdeveloped region in China. In parallel, multisource remote sensing data with higher spatial resolution provide more precise results for small-scale regional accessibility assessment. Thus, we use an assessment method to measure and evaluate the accessibility of three types of infrastructure in Lincang based on multisource geospatial data. We further analyze the matching between public service facility accessibility and the socioeconomic attributes of inhabitant clusters and different poverty groups. The results show that the accessibility of educational facilities is currently better than that of health facilities in Lincang and that of sanitation facilities is relatively poor. Public service facility accessibility varies significantly among different types of inhabitant clusters, with better accessibility in inhabitant clusters with high levels of population density, aging, and income. Accessibility to healthcare, education, and sanitation is negatively correlated to varying degrees of poverty levels of poor groups, and the mean values of accessibility to various types of public facilities vary significantly across poor groups. Our findings can help inform policy formulation and provide theoretical support for planning and optimizing the layout of public facilities.

Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda).

For more efficient development planning, food-energy-water (FEW) nexus indicators should be provided with higher spatial and temporal resolutions. This paper takes Zhangye, a typical oasis city in Northwest China’s arid region, as an example, and uses the unweighted, geometric mean method to calculate a standardized, quantitative, and transparent estimation of the FEW nexus for each county. The role of influencing factors is also analyzed. The results showed that (1) the coordination of the FEW nexus in each county gradually increased from 2005 to 2015. Spatially, the distribution of the FEW nexus showed a tendency to be higher in the southwestern region and lower in the northeastern region. (2) Food security and water security were weaker than energy security. Specifically, there were more limitations to food accessibility, water availability, and water accessibility than for other indexes. (3) The FEW indexes are positively associated with per capita GDP (Gross Domestic Product) and negatively correlated with the average evaporation and altitude of each county (district). Decision makers should concentrate on combining industrial advantages, developing water-efficient ecological agriculture, and improving production quality to increase market competitiveness and should actively explore the international market.