## 3 Study Area and Data
### Landslide Predisposing Factors  
A landslide is the result of the coupling action of internal and external driving forces [8; 44]. In LHA studies, the predisposing factors related to landslide formation are the basic variables for the calculation of the landslide hazard. Based on field investigations and literature research, we analyzed the controlling factors of landslide occurrence in the study area. Eventually, geomorphological factors (elevation, aspect, slope, and topographic relief), geological factors (rock group, fault density, and peak ground acceleration (PGA)), hydrological factors (river density and annual precipitation), and a surface cover factor (normalized difference vegetation index (NDVI)) were selected for the LHA of the study area. We obtained the original data of these predisposing factors from existing survey data and open databases. Detailed information about these data and their sources is shown in Table 2.  
Subsequently, we used the zonal statistics tool of the GIS software (ArcGIS v10.4) to assign the data of the selected predisposing factors to the generated evaluation units based \begin{table}
\begin{tabular}{c c c} \hline
**Predisposing Factor** & **Data Source** & **Resolution or Scale** \\ \hline Elevation, slope, aspect, and topographic relief & Geospatial Data Cloud ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn))) & 30 m \\ \hline Rock group and fault density & Geological map (accessed on 6 June 2024) & 1:250,000 \\ \hline \multirow{3}{*}{PGA} & China Earthquake Administration & \\  & ([[https://www.cea.gov.cn/cea/index/index.html](https://www.cea.gov.cn/cea/index/index.html)]([https://www.cea.gov.cn/cea/index/index.html](https://www.cea.gov.cn/cea/index/index.html))) & 1:250,000 \\  & (accessed on 24 June 2024) & \\ \hline \multirow{3}{*}{Annual precipitation} & WorldClim ([[https://www.worldclim.org](https://www.worldclim.org)]([https://www.worldclim.org](https://www.worldclim.org))) & \multirow{3}{*}{1 km} \\  & (accessed on 26 December 2024) & \\ \hline \multirow{3}{*}{Population density and GDP density} & Resource and Environmental Science Data & \\  & Platform ([[https://www.resdc.cn](https://www.resdc.cn)]([https://www.resdc.cn](https://www.resdc.cn))) (accessed on 26 December 2024) & \\ \hline River density, road density, and mine density & National Platform for Common Geospatial & \\  & Information Services ([[https://map.ianditu.gov.cn](https://map.ianditu.gov.cn)]([https://map.ianditu.gov.cn](https://map.ianditu.gov.cn))) & 30 m \\  & (accessed on 26 December 2024) & \\ \hline \multirow{3}{*}{NDVI and land use} & National Cryosphere Desert Data Center & \\  & ([[http://www.ncdc.ac.cn](http://www.ncdc.ac.cn)]([http://www.ncdc.ac.cn](http://www.ncdc.ac.cn))) (accessed on & 1:10,000 \\  & 23 January 2025) & \\ \hline \end{tabular}
\end{table} 
Table 2: The sources of the spatial databases used in this study. on spatial relationships, thereby obtaining data layers for all predisposing factors. Most of these predisposing factor layers were divided into eight classes according to the natural breakpoint method [45]. Among them, the aspect, rock group, and PGA were discontinuous factors and were classified according to their natural attributes [19]. Finally, we analyzed the relationships between each predisposing factor and landslide occurrence using the DFR and FR methods. The analysis results and the classification of each predisposing factor are shown in Table 3.