Traditional villages (TVs) are clusters for the conservation of farming civilization heritage with multiple values. Studying their spatial distribution and driving mechanisms is conducive to formulating conservation and development strategies. In this study, 777 TVs in Yunnan Province were examined. Spatial analysis methods such as the nearest neighbor index (NNI), kernel density estimation (KDE), and Moran’s I were used to investigate their spatial distribution patterns. Twelve driving factors were selected from natural, spatial, social, and cultural aspects. The driving mechanisms affecting the distribution of TVs were explored by employing the geographically weighted regression (GWR) model and Geodetector combined with mathematical and statistical methods to systematically study the spatial autocorrelation and heterogeneity of TVs in Yunnan. The results of this study indicate that: (1) The 777 TVs in Yunnan Province are evidently clustered in the overall distribution, presenting a contiguous and concentrated distribution pattern of “3 high-density areas + multiple medium-density belts”. However, the distribution of TVs is unbalanced. The spatial distribution is significantly positively correlated globally, and there exist three types of clustering in local areas. (2) The spatial layout of TVs in Yunnan Province is jointly influenced by multiple factors, with obvious spatial heterogeneity. Regional cultural factors are the key ones. TVs tend to be distributed in areas with medium–high elevation, flat slops, sunny slopes, moderate precipitation and temperatures, and a certain distance from water sources and roads. A large number of TVs are distributed in areas with a high proportion of ethnic minorities. (3) The main factors affecting the distribution of national-level TVs in Yunnan are intangible cultural heritage, cultural relic protection units, followed by factors such as transportation, slope, and elevation. The interaction between intangible cultural heritage (X11) and cultural relic protection units (X12) reveals the strongest interactive driving force. This study reveals the diverse characteristics of the spatial distribution of national-level TVs in Yunnan and their influencing mechanisms, which can provide a scientific decision-making basis for the future protection and development of TVs in Yunnan and guide the sustainable development of TV cultural heritage clusters.

The construction of ecological security patterns is an effective means to improve ecological environment quality, protect regional biodiversity, and alleviate the landscape fragmentation caused by urbanization in plateau lake cities. Taking the Dianchi Lake area as an example, we used the morphological spatial pattern analysis method (MSPA) and the minimum cumulative resistance model (MCR) to construct a comprehensive resistance surface, ecological corridor network, and ecological security pattern for the study area. Firstly, we selected 12 ecological sources with more than 1000 hm², PC, IIC index more than 1, and high habitat quality and connectivity from the study area, including the Dianchi lake body and the mountain forests in the south, north, and west. The overall habitat quality in the eastern region was poor. Secondly, the regional comprehensive resistance value was 1.0925–4.5395. The comprehensive resistance surface showed that the influence of human activities in the region was strong, and the connectivity between important sources was poor. Thirdly, we identified 26 important corridors with interaction force values higher than 50, mostly mountain corridors, between sources that were close to one another and over 40 general corridors with interaction force values lower than 50 in urban built-up areas, most of which were river corridors. Fourthly, by identifying five potential sources and 43 potential corridors in the eastern region, we improved the ecological network function and overall connectivity. The α index (loop pass degree), β index (line point rate), and γ index (connectivity degree) were 2.895, 5.5, and 2.2 before optimization and 3.206, 6.412, and 2.422 after optimization, respectively. Lastly, the “ridge lines” and “valley lines” were used to screen the ecological nodes in our ecological network model and construct a “one core, three regions, and one belt” ecological security pattern by combining the geographical characteristics of the research region and the local policy planning guidance. We also provided ecological control, restoration, and construction suggestions based on the corridor plans of other administrative regions and the different types of source area.

Ecological sensitivity measures an ecosystem’s reaction and restoration difficulty to exogenous disturbances. Regional ecological and environmental challenges can be identified using ecological sensitivity evaluation. The regional characteristics and environmental challenges of plateau lakes were quantified to create four assessment indexes: landform, natural conditions, human activities, and ecological security. Eleven ecologically sensitive characteristics were chosen. We evaluated the landscape ecological sensitivity of the Erhai Lake Basin, China, using ArcGIS and the analytical hierarchy process (AHP). The results showed that the Erhai Lake Basin was mostly forest from 1990 to 2020 and that the dynamic land-use attitude increased and then reduced. The cultivated land became mostly woods and grassland. The largest building area was tied to reverting farms to woods and urban construction. The highest weight was from single-factor ecologically sensitive vegetation covering, followed by river systems and roadways, and lowest was from landslides and collapses. The ecologically sensitive areas with more vegetation and a greater distance from roads and woodland are harder to recover from harm. According to the thorough sensitivity analysis, the study region’s high sensitivity area is 1102.36 km² (26.16%) and the higher sensitivity area is 1177.10 km² (27.93%). The ecological sensitivity increases from neighborhoods to nature reserves. High-sensitivity areas were in hilly woods and grasslands with few people. The low-sensitivity area was around water and homes. The dynamic stability of the area ecological environment influenced ecological sensitivity. This study aims to provide sustainable land use solutions for the Erhai Lake Basin and a scientific basis for managing and protecting ecologically vulnerable areas.