code

These three scripts together establish the complete technical workflow of this study, from image classification to spatial driver analysis. First, the CNN classification script uses EfficientNetV2-S to build a Cultural Ecosystem Services (CES) image classification model. By incorporating a two-stage training strategy, data augmentation, focal loss, EMA weights, and center-priority test-time augmentation (TTA), the script performs multi-class classification of social media images and outputs the best-performing model together with training diagnostics, thus providing a reliable CES data foundation for subsequent spatial analysis. Second, the GRF bandwidth search script focuses on parameter optimization for the Geographical Random Forest (GRF) model. Using grid-level CES diversity and environmental variables, it conducts a systematic search of key parameter combinations, such as bandwidth, ntree, and mtry, and evaluates model performance using out-of-bag mean squared error (OOB MSE), thereby identifying the optimal settings for robust spatial modeling. Finally, the RF-SHAP-GRF integration script combines Random Forest (RF), SHAP, and GRF analyses into a unified framework. It identifies the global importance of explanatory variables, interprets the direction and magnitude of their effects on CES diversity, and further reveals the spatial non-stationarity of driving mechanisms. At the same time, it generates the core visual outputs of the study, including RF importance plots, SHAP summary plots, RF–SHAP overlay figures, and GRF local importance maps. Taken together, these three scripts correspond to the three major stages of the analytical pipeline—image classification, parameter optimization, and driver interpretation—and form a complete, reproducible, and highly interpretable methodological framework for the study.