Land suitability assessment is fundamental in space control planning and land development because of its effects on land use and urban layout. Rainstorms and waterlogging have become one of the most common natural disasters in the coastal areas of China. As a result, the concept of an ecological sponge city was incorporated into the construction of cities in the future. Taking Shenzhen–Shantou special cooperation zone (SSCZ), we constructed a storm flooding model based on the SCS flow generation model and GIS to explore the spatial distribution characteristics of the flooding risk in a rainstorm of 100-year lasting 1 h. Combined with population and economic indicators, a radial basis function (RBF) network was utilized to evaluate the environmental risk, the vulnerability of disaster-bearing bodies, and the rain–flood resilience of sponge cities. The self-organizing feature mapping (SOFM) model was used for cluster analysis. Spatial differences were found in the construction suitability of the study area. A suitable construction area (73.59% of the entire area) was located downtown. The construction of the artificial spongy body in the highest vulnerable area (3.25%) needs to be strengthened. The control construction area (3.3%) is located along the banks of the river, with relatively high risk and low resilience of flood control engineering. Ecological construction (19.85%) serves as the sponge body of ecological buffer. The factors of waterlogging, ecology, population, and economy could be integrated comprehensively by applying neural network methods for urban planning and construction.

Previous Studies, such as the evaluation of the supply of and demand for regional medical resources and carrying capacity assessments, require further development. This paper aims to evaluate the carrying capacity and spatial distribution of medical resources in Shenzhen from the perspective of supply and demand, and to conduct a time-series variation of the coupling coordination degree from 1986 to 2019. The two-step floating catchment area method was employed to quantify the carrying capacity and coupling coordination degree method and spatial autocorrelation analysis were applied to analyze spatial distribution between supply and demand. The results were as follows. (1) The carrying capacity index in more than 50% of the districts was classified as low-grade. The percentage of regions with good grades was 8.27%. The regions with a high carrying capacity were distributed in the central and southeastern areas. (2) The coupling coordination continued to rise, increasing from 0.03397 in 1986 to 0.33627 in 2019. (3) The level of supply and demand for medical resources in Shenzhen increased from 1986 to 2019, and the highest degree of compatibility between the supply and the population size was largely concentrated in the western and eastern regions. This research can provide a theoretical reference for Shenzhen to rationally plan medical resources and improve the carrying capacity of medical resources.

Sustainable Development Goals propose to build inclusive, safe, resilient, and sustainable cities and human settlements, which requires us to scientifically evaluate the carrying capacity of current urban public service facilities, but there is still a lack of in-depth exploration of urban public medical service facilities. Therefore, this paper, within the mobile phone signaling data, improved the potential model and carrying capacity evaluation model of public medical facilities, explored the spatial pattern distribution of public medical resources in Shenzhen, and analyzed the current situation of carrying capacity of public medical resources. The study showed that: (1) the overall spatial distribution of public medical resources in Shenzhen is uneven, showing a pattern of multicenter aggregation and multilevel development; (2) the service potential of public medical facilities has obvious spatial variations, with Futian District, Dapeng New District, and Nanshan District showing more obvious high-gravitational-value aggregation centers; (3) medical facilities in Shenzhen are never empty, but the problems of medical underloading and overloading are severe, and spatial allocation and utilization efficiency need to be further optimized. The research results can provide a scientific basis for the research on the allocation and sustainable construction of medical resources in megacities.

Ecosystem services play a vital role in human well-being, with land-use changes exerting substantial influence on ecosystem service value (ESV) and land-use carbon emissions (LUCEs). Understanding the spatio-temporal relationship and transition dynamics between ESV and LUCEs is essential for promoting high-quality ecological development aligned with the “dual carbon” objective. This study takes the Loess Plateau as the research object. Based on five-phase land-use data from 2000 to 2020, the ESV and LUCEs are calculated. Exploratory spatio-temporal data analysis is used to explore their spatio-temporal relationship and transition paths, and the quadrant model is introduced to analyze the transition patterns from the perspective of ecological quality. The results indicate the following: (1) From 2000 to 2020, the ESV of the Loess Plateau increased from CNY 579.032 billion to CNY 582.470 billion, with an overall increase of only 0.15%. Among the changes in land use, changes in forest and grassland significantly affected the ESV. (2) The LUCEs from land use on the Loess Plateau increased from 137.15 Mt to 458.43 Mt, with an average annual growth rate of 6.22%. Affected by industrialization and urbanization, the LUCEs showed significant spatial differences at the provincial and county scales. (3) There was a certain positive spatial correlation between ESV and LUCEs. The distribution of significantly correlated areas did not change significantly from 2000 to 2020, and the relationship characteristics were mainly characterized by Type IV transitions. (4) At the county scale, ESV and LUCEs exhibited temporal stability, with most counties situated in the general ecological category, indicating substantial potential for enhancing regional ecological quality. These research outcomes offer a foundational framework for devising tailored regional carbon emission reduction strategies.