Traditional villages in the eastern Qinghai–Tibet Plateau (EQTP) represent an important component of global rural cultural diversity. However, an in-depth understanding of the spatial distribution and potential influencing factors of their different landscape types at a regional scale is still lacking. Accordingly, this study developed an integrated analytical framework based on 449 officially designated traditional villages. The K-means clustering algorithm was used to identify traditional-village landscape characteristics. By integrating spatial statistical methods with the GeoDetector model, this study quantitatively examined the spatial associations between each traditional-village type and natural, socio-economic, and cultural factors. The analysis indicated: (1) four traditional-village types were identified, including River valley cropland-dominated traditional villages (RV-CDTV), Mid-mountain steep-slope river-distant forest–cropland mosaic traditional villages (MMRD-FCMTV), Mid-mountain steep-slope riverside forest-dominated traditional villages (MMR-FDTV), and Plateau riverside grassland-dominated traditional villages (PR-GDTV); (2) all traditional-village types exhibited significant clustering patterns and distinct spatial configurations; and (3) the highest explanatory power for the spatial distribution of the four village types is derived from: social factors (for RV-CDTVs), the interaction of social and cultural factors (for MMRD-FCMTVs), the interaction of cultural and natural factors (for MMR-FDTVs), and natural factors (for PR-GDTVs). The study reveals that the explanatory power of various influencing factors exhibits phased shifts as traditional-village landscape types differentiate. This not only provides a new perspective for exploring the spatial distribution characteristics and multidimensional influencing factors of traditional villages in complex terrains but also offers a scientific basis for implementing differentiated conservation strategies for traditional villages in high-elevation mountainous areas.
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Li et al. (2026) studied this question.
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