Peiyao Wang

Mountainous traditional villages are rural heritage settlements shaped by complex environmental and socioeconomic interactions. Rapid urbanization has driven resource outflow and spatial restructuring, intensifying the conservation–development conflict. Existing conservation models relying on standardized criteria neglect village heterogeneity, and current studies insufficiently capture cluster-specific driving mechanisms across spatial scales. Using 153 traditional villages in the Qin–Ba Mountains of southern Shaanxi, this study shifts the analysis from overall spatial pattern identification to the comparison of cluster-specific driving mechanisms. The results indicate (1) traditional villages display an agglomeration pattern of “one primary core, one secondary core, and multiple peripheries,” forming valley corridor and hilly barrier clusters; (2) socioeconomic factors show greater explanatory power than natural factors at the global scale; (3) influencing factors exhibit dual heterogeneity, with intensity and direction varying across space and clusters displaying distinct dominant-factor combinations; and (4) socioeconomic factors may provide enabling conditions for traditional village conservation and adaptive reuse under appropriate governance contexts. Accordingly, valley corridor clusters can follow “conservation through development,” whereas hilly barrier clusters should adopt “development through conservation.” These findings offer empirical support for spatially differentiated governance of mountainous traditional villages.

Mountainous traditional villages represent unique socio-ecological systems that have evolved through centuries of adaptation to complex topographies and multi-hazard environments. Understanding their terrain–resilience coupling mechanisms is essential for risk-sensitive planning and heritage preservation in mountainous regions. This study integrates multi-source remote sensing data and GIS spatial analysis to investigate 57 national-level traditional villages in the southern Qinba Mountains, China. Using kernel density estimation (KDE), nearest neighbor index (NNI), and Geodetector modeling, we identify the spatial distribution characteristics and topographic driving forces that shape settlement patterns across macro-meso-micro scales. Results reveal that 83% of the villages are clustered in low-mountain and hilly zones (550–1200 m elevation), preferring slopes below 15° and south-facing aspects. Elevation exerts the strongest influence (q = 0.46), followed by slope (q = 0.32) and aspect (q = 0.29), forming a multi-level adaptation framework of “macro-elevation differentiation, meso-slope constraint, and micro-aspect optimization.” Morphological Spatial Pattern Analysis (MSPA) further indicates that traditional villages achieve ecological balance and disaster avoidance through adaptive spatial strategies such as terrace-based flood prevention, convex-bank stabilization, and platform-based hazard avoidance. These strategies are not merely topographic preferences but natural adaptation mechanisms formed by long-term responses to multi-hazard environments—dynamic adaptation processes that reduce disaster exposure and optimize resource use efficiency through active adjustment of site selection and spatial transformation (the disaster density in the 100m core zone buffer is 0.077 events/km², significantly lower than 0.290 events/km² in peripheral areas). These findings demonstrate that remote sensing techniques can effectively reveal the terrain–resilience coupling of traditional villages, providing quantitative evidence for integrating spatial resilience into cultural landscape conservation, ecological security assessment, and rural revitalization planning. The proposed multi-scale analytical framework offers a transferable approach for evaluating settlement adaptability and resilience in other mountainous cultural heritage regions worldwide.

The Qinba Mountain Region in southern Shaanxi, China, is both a key ecological barrier and a repository of cultural heritage, yet its traditional villages remain highly vulnerable to natural disasters. Disaster-relocation policies have reduced direct exposure to hazards but also created challenges such as settlement hollowing and weakening of cultural continuity. However, systematic studies on the resilience mechanisms of these villages and a corresponding governance framework remain limited. This study applies social–ecological resilience theory to evaluate the resilience of 57 nationally recognized traditional villages. Using a combination of Morphological Spatial Pattern Analysis (MSPA), the entropy weight method, and the geographical detector model, we construct a three-dimensional evaluation framework encompassing terrain adaptability, hazard exposure, and ecological sensitivity. The results show that the Terrain Adaptability Index (TAI) is the dominant driver of resilience, with an explanatory power of q = 0.61, while the interaction of Hazard Exposure Index (HEI, q = 0.58) and Ecological Sensitivity Index (ESI, q = 0.49) produces a nonlinear enhancement effect, significantly increasing vulnerability. Approximately 83% of villages adopt a “peripheral attachment–core avoidance” strategy, and 57% of high-resilience villages (CRI ≥ 0.85) rely on traditional clan-based networks and drainage systems to offset ecological fragility. Based on these differentiated resilience characteristics, the study proposes a three-tiered governance framework of core protection areas–ecological restoration zones–cultural corridors. While this framework demonstrates broad applicability, its findings are context-specific to the Qinba Mountains. Future studies should apply the model to other mountainous regions and integrate dynamic simulation methods to assess climate change impacts, thereby expanding the generalizability of resilience governance strategies.