Southwest China is home to more than 30 ethnic minority groups. Since most of these populations reside in mountainous areas, convenient access to medical services is an important metric of how well their livelihoods are being protected. This paper proposes a medical convenience index (MCI) and computation model for mountain residents, taking into account various conditions including topography, geology, and climate. Data on road networks were used for comprehensive evaluation from three perspectives: vulnerability, complexity, and accessibility. The model is innovative for considering road network vulnerability in mountainous areas, and proposing a method of evaluating road network vulnerability by measuring the impacts of debris flows based on only links. The model was used to compute and rank the respective MCIs for settlements of each ethnic population in the Dehong Dai and Jingpo Autonomous Prefecture of Yunnan Province, in 2009 and 2015. Data on the settlements over the two periods were also used to analyze the spatial differentiation of medical convenience levels within the study area. The medical convenience levels of many settlements improved significantly. 80 settlements were greatly improved, while another 103 showed slight improvement.Areas with obvious improvement were distributed in clusters, and mainly located in the southwestern part of Yingjiang County, northern Longchuan County, eastern Lianghe County, and the region where Lianghe and Longchuan counties and Mang City intersect. Development of the road network was found to be a major contributor to improvements in MCI for mountain residents over the six-year period.

Accurate landslide susceptibility assessment is vital for disaster prevention, but current mapping lacks systematic analysis of the underlying mechanisms between multi-scale factors and model performance. Taking Zhenxiong County as an example, this paper combines the IV, WOE, LR models, and PCA to reveal the impact of methodological differences and scale selection on mapping results, and quantitatively evaluates them using ROC curves and landslide density statistics. Results show that: 1) The scale effect of influencing factors is significant. Natural factors such as topography, geological conditions, and rainfall play dominant roles at the regional scale, while the impacts of human activities, geological features, and soil erosion intensity are more pronounced at local and moderate scales. 2) The landslide susceptibility mapping results of the three models at different spatial scales show similar spatial distribution trends. As the spatial scale increases, high/very high susceptibility areas and low/very low susceptibility areas spread outward, while the spatial distribution of medium susceptibility areas shows a fragmented expansion outward first and then agglomeration and contraction inward. 3) Scale selection significantly affects the accuracy of landslide susceptibility mapping, and expanding the spatial scale appropriately improves mapping precision. The IV and WOE models show the highest AUC at the 600-m buffer, while the LR model peaks at 400 m. In terms of landslide identification accuracy, the IV model performs best at 400-m buffer, WOE at 600-m buffer, and LR at 100 -meter buffer. 4) Different methods have different mapping performances. Overall, the IV model performs best, followed by the WOE model, with the LR model lagging behind. In terms of high-risk area recognition, the LR model excels, followed by the IV model, while the WOE model performs relatively poorly. 5) Scale and method selection significantly impact landslide susceptibility mapping outcomes. The IV model excelled in global prediction at the 600-m buffer, whereas the LR model was effective in pinpointing high-risk areas at the 100-m buffer. This paper proposes a landslide susceptibility evaluation method that integrates model performance and scale effects, enhancing disaster assessment and prevention capabilities.