Tourism Risk Prediction and Influencing Factor Analysis on the Qinghai–Tibet Plateau Based on Interpretable Machine Learning

Tourism safety in high altitude destinations is strongly affected by the combined effects of environmental constraints, tourism exposure, and safety support capacity. The Qinghai–Tibet Plateau (QTP), characterized by high altitude, complex terrain, sparse settlements, and limited emergency accessibility in remote areas, provides a representative case for tourism risk assessment in extreme plateau environments. To predict and interpret the spatial pattern of tourism risk on the QTP, this study constructs an assessment framework based on “Hazard–formative factors + Risk exposure + Safety security” and integrates XGBoost with SHAP interpretable machine learning. Eleven indicators representing environmental conditions, tourism exposure, and safety support capacity were used to model tourism risk at a 1 km × 1 km spatial resolution. The optimized XGBoost model achieved an AUC of 0.877, indicating good predictive performance. The results show that tourism risk on the QTP presents a spatial pattern of “high in the northwest and low in the southeast”. High risk and relatively high risk areas account for approximately 74.98% of the study area and are mainly distributed in remote hinterlands and northwestern plateau regions, whereas low risk areas are concentrated around southeastern river valleys, towns, mature scenic areas, and major transport corridors. SHAP analysis indicates that Distance to towns is the most important factor influencing predicted tourism risk, followed by Reception facility kernel density, Relief degree of land surface, and Scenic spot kernel density. Nonlinear and interaction analyses further suggest that remoteness, tourism facilities, terrain relief, and scenic area concentration jointly shape the predicted risk pattern. The findings provide spatial evidence for differentiated tourism risk management, including regular tourism development in relatively safe urban and scenic nodes, controlled management of medium risk tourism corridors, and stricter access management in remote high risk areas.