## 4 Results and Analysis
### Analysis of the Spatial Correlation between Habitat Quality and Geological Hazard Susceptibility  
Utilizing the GeoDa software platform, a queen contiguity spatial weight matrix was constructed to analyze the bivariate global spatial autocorrelation. [PERSON]'s I index for habitat quality and geological disaster susceptibility in Pingshan County for the year 2020, measured at a 30 m grid scale, was calculated to be 0.782 (Figure 6). This index achieved a \(p\)-value of less than 0.01, indicating statistical significance within a 99% confidence interval. The positive [PERSON]'s I index suggests a significant positive spatial correlation between habitat quality and susceptibility to geological disasters in Pingshan County. The LISA clustering analysis (Figure 7) of habitat quality and geological disaster susceptibility shows that the dominant cluster types in the study area are high-high and low-low. The high-high clusters are mainly located in the northwestern mountainous regions, often at the borders of provinces and counties within the Yanshan-Taihangshan ecological conservation area, which includes various nature reserves and ecotourism zones. In these regions, land use planning prioritizes ecological balance and environmental conservation, characterized by substantial vegetation coverage and a low population density. This configuration minimizes human interference, enhancing habitat quality. However, these areas, noted for their high altitudes, significant topographical variations, and extensive fault structures, are also subject to frequent geological disasters, indicating high susceptibility. Conversely, in the eastern hilly and plain areas, the predominant land uses are agriculture and urban development, with higher population densities. Intense human activity has modified the original environment, and urban development has disrupted the continuity of vast farmland, intensifying habitat fragmentation and degrading habitat quality. Nonetheless, the flatter terrain and more stable soil conditions, combined with effective monitoring and  
Figure 6: Moran’s I index. disaster prevention strategies, render these regions less susceptible to geological disasters, leading to the identification of the low-low cluster type. As global climate change continues, phenomena such as the degradation of glaciers and permafrost, rising sea levels, increased evaporation, and significant changes in rainfall frequency, periodicity, and intensity have become increasingly apparent. These changes interact with soil and rock masses, leading to various types of geological disasters, such as landslides and mudflows, posing a serious threat to human life. Therefore, there has been an increased focus on the relationship between ecological environments and geological disasters, aiming to provide scientific bases for the prevention and response to geological disasters and the protection of ecological environments [37; 38; 39; 40]. The function of habitat quality services closely links human well-being with the ecological environment, not only helping people understand and solve the dilemmas of development and protection but also directly reflecting the state of the ecological environment and its impact on human society. In view of this, this paper uses ArcGIS software 10.6 to count the number and density of geological disasters in areas with different levels of habitat quality (Table 6), deeply studying the impact of habitat quality on susceptibility to geological disasters. In regions with an extremely high habitat quality, there are 88 disaster points, resulting in the second highest density of 0.0566 points per square kilometer. Conversely, the area with a high habitat quality, despite having the highest density of 0.0920 points per square kilometer, has only 18 disaster points, ranking third. This indicates that geological disasters are more frequent in areas of high habitat quality.