Objectively assessing the impact of various basic public service facilities on social–spatial differentiation has become a prerequisite for promoting urban social integration and development. However, in practice, the configuration of basic public service facilities is not always conducive to social integration, especially at the microscale. Effectively measuring the inhibitory or aggravating effects of various basic public service facility configurations on social–spatial differentiation has become a challenge. Based on the assumption that the configuration of basic public service facilities has inhibitory and aggravating effects on social–spatial differentiation, this study selected two types of objects: social space and basic public service facilities to refine the research elements. Using spatial and statistical analysis methods such as ecological factors, clustering, correlation, mediation, and superposition analysis, a framework was constructed to evaluate the impact of basic public service facility configuration on social–spatial differentiation and take the Zhaomushan area in Chongqing, China, as a typical case for verification. The study found that registered residence, income, employment location, and residential density are still the main factors of social–spatial differentiation in the study area. The main factors contributing to the differentiation of basic public service facilities are elderly care and housing security, public transportation and green space access, education and employment security, and small-scale medical and health facilities in the study area. In the eight principal factor pairs after the superposition of two differentiation spaces, six pairs showed weakened spatial differentiation, while two pairs showed intensified spatial differentiation. This indicates that the allocation of basic public service facilities will simultaneously inhibit and exacerbate social–spatial differentiation, but the inhibitory effect is significantly stronger than the exacerbating effect. Among them, public transportation and green parks are the main types of facilities that mainly exacerbate social–spatial differentiation. This dual effect is specifically reflected in the change in the spatial adaptation position of social space and basic public services, the weakening of the original social space differentiation boundary and the emergence of new differentiation boundaries simultaneously, and the multicenter composite form of social space. In the future, quantitative evaluation based on research frameworks can provide scientific basis for constructing spatial adaptability strategies for the supply of basic public service facilities and social production and life, such as adjusting the distribution, scale, and spatiotemporal relationship between basic public service facilities and residential communities in a reasonable manner. This is crucial for promoting social integration.

Road infrastructure is reshaping the rural settlement landscape in the Himalayan area of China through the construction of the rural road and strategic highway network. However, most methods based on multiple factors described in spatial analysis of rural settlement are limited by poor spatial response mechanisms of key factors. This study provides insight into the temporal and spatial process involving 15 rural settlements of Zhada County, west of the Himalayas. The growth of rural settlement follows a “short-head S-shape” function and the general expansion rule. It indicates the mode of evolution and the characteristics of construction. The results show that 70% of rural settlements continue to report the inertia of growth, while the reconstruction of the original site leads to historical spatial displacement under spatio-temporal compression. In addition, rural settlements display a spatial organization of interface area, hinterland, and fringe area and reveal two spatial paradigms of near-road expansion and peripheral extrusion. Further, the hinterland space, which is the core of rural settlement, is compact and intensive; a quarter of the hinterland space encompasses 45% of the settlement scale. These conclusions provide guidance for delineating village boundaries and improving the human settlement environment in the Himalayan-alpine plateau.

Extreme rainfalls could greatly affect operations of urban rail transit systems of mountainous cities, which are prone to have landslides and floods under rainfalls. Therefore, it is essential to assess and enhance the resilience of mountainous urban rail transit networks under heavy rainfalls. Taking the metro network of Chongqing, the largest mountainous city in China, as an example, this study establishes a network topology model to identify the high-risk nodes under rainfalls and find the effective recovery strategies. By introducing the metro ridership and topological shortest distances, a network service efficiency function is developed, and the importance of nodes is quantified using service efficiency index and topological importance index. The resilience assessment model based on service efficiency is constructed using the resilience triangle theory. Additionally, risk levels for landslide and flood-prone areas are classified using the K-means algorithm, based on rainfall, elevation, and slope data, identifying high-risk stations. Finally, the node recovery sequence and strategies for high-risk nodes affected by landslides and floods are examined. The results indicate that in extreme rainfall scenarios, two transfer stations (Daping and Fuhua Road) are among the high-risk landslide stations, while most other nodes have a service efficiency index of less than 0.2. High-risk flood stations are located on non-transfer lines and mostly on metro lines with high traffic flow, with service efficiency index generally high, with some stations, like Bijin Station, exceeding 0.3. When all affected nodes fail, network service efficiency decreases by 84.0% and 75.2% under landslide and flood disasters, respectively. Compared with the random recovery strategy, recovery strategies based on topological importance and service efficiency index, the optimal recovery strategy based on genetic algorithm performs much better.