| Summary: | Landslide Susceptibility Assessment (LSA) is a fundamental component of landslide risk management and a substantial area of geospatial research. Previous researchers have considered the spatial non-stationarity relationship between landslide occurrences and Landslide Conditioning Factors (<i>LCFs</i>) as fixed effects. The fixed effects consider the spatial non-stationarity scale between different <i>LCFs</i> as an average value, which is represented by a single bandwidth in the Geographically Weighted Regression (GWR) model. The present study analyzes the non-stationarity scale effect of the spatial relationship between <i>LCFs</i> and landslides and explains the influence of factor correlation on the LSA. A Principal-Component-Analysis-based Multiscale GWR (PCAMGWR) model is proposed for landslide susceptibility mapping, in which hexagonal neighborhoods express spatial proximity and extract <i>LCFs</i> as the model input. The area under the receiver operating characteristic curve and other statistical indicators are used to compare the PCAMGWR model with other GWR-based models and global regression models, and the PCAMGWR model has the best prediction effect. Different spatial non-stationarity scales are obtained and improve the prediction accuracy of landslide susceptibility compared to a single spatial non-stationarity scale.
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