Traditional villages are human treasures left behind by the integration of material space and non-material culture in the process of agricultural civilization. Studying the spatial autocorrelation characteristics, heterogeneity, and quantitative attribution of the factors influencing traditional villages provides new ideas for the protection of traditional villages. This study took 75 traditional villages as the research object. From the perspective of spatial autocorrelation and spatial heterogeneity, the study used nuclear density estimation and Moran’s I index to analyze the spatial distribution patterns and selected 12 factors to construct the GWR modeling and geodetector to analyze the main driving forces and the interaction mechanism. The results showed that, firstly, the overall spatial layout of traditional villages in the Awa Mountain area had two cores, two sides, and a scattered distribution; the global Moran’s I was 0.774, and 55.6% of traditional villages showed a clustering phenomenon. Second, the spatial layout of traditional villages in the Awa Mountain area has been jointly promoted and mutually constrained by multiple factors in a dynamic and complex mechanism with obvious spatial heterogeneity. The natural factor is the basic factor, which determines the location and scale of development of villages; the spatial factor is the auxiliary factor; the social factor is the decisive factor, with a negative global correlation and a positive local correlation; the regional cultural factor is the key factor, and the regional factor and the social factor complement each other; and factors such as a backward economic level, restricted transportation, less external communication, and low population density play a protective role. Third, the main driving factor is the proportion of ethnic minorities (X10), and the explanatory power of q-value reaches 0.54; the proportion of ethnic minorities (X10) ∩ average annual precipitation (X4) has the strongest interactive driving force, which belongs to nonlinear enhancement, and the q-value is 0.93, which proves that the explanatory power of the two-factor model is much greater than the single-factor model from the system perspective.

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.

Analyzing vegetation cover provides a basis for detecting ecological and environmental health in urban areas. We analyzed the temporal and spatial changes in vegetation cover using NDVI data from the central Yunnan urban agglomeration (CYUA). The dimidiate pixel model (DPM) and intensity analysis were used to study changes at three levels: time intervals, category, and transition. Analysis of time series data from 1990–2020 using the Theil–Sen Median with Mann–Kendal test identified the overall trends. Geodetector explored the relationship between natural and human factors in vegetation cover change. The CYUA’s vegetation cover gradually decreases from west to east and south to north, with middle–high and high vegetation occupying over 55%. During 1990–2020, significant improvement was observed in the east and north regions, with an increase of 22.49%. The anthropogenic core area showed severe degradation with nearly 1.56% coverage. The transformation intensity of middle vegetation coverage was dominant from 1990–2010 but was replaced by middle–high vegetation coverage from 2010–2020. Meanwhile, high vegetation coverage became the most prominent gains target, and the conversion of middle–high to high vegetation showed a system tendency to exceed the average in absolute number and relative intensity. Spatial and temporal differences in vegetation cover were mostly affected by land cover (q = 0.4726, p < 0.001), and the most influential topographic factor was the slope (q = 0.1491, p < 0.001). The impact of human activities has increased to 16%, double that of 2000. The CYUA’s vegetation cover improved more than it degraded, but required site-specific forest management due to human activities.