Examining the Driving Factors of SOM Using a Multi-Scale GWR Model Augmented by Geo-Detector and GWPCA Analysis

A model incorporating geo-detector analysis and geographically weighted principal component into Multi-scale Geographically Weighted regression (GWPCA-MGWR) was developed to reveal the factors driving spatial variation in soil organic matter (SOM). The accuracy residuals from GWPCA-MGWR were compared those of classical (GWR), (MGWR), GWPCA-GWR. Our results revealed that local multi-collinearity on fitting negatively affects different degrees. Additionally, other models, provided lowest MAE (0.001) little-to-no residual autocorrelation is best for SOM distribution identification dominant factors. produced non-stationary variably affected by nutrient content, type, human activity, geomorphic second place. In conclusion, information obtained provides a valuable reference understanding influence variation.