Identifying Suitable Zones for Tourism Activities on the Qinghai–Tibet Plateau Based on Trajectory Data and Machine Learning

The Qinghai–Tibet Plateau (QTP), a globally significant tourist destination and critical ecological barrier, faces an intrinsic conflict between development and conservation. The scientific identification of suitable tourism zones is therefore crucial for formulating sustainable development policies. Conventional suitability assessments, however, which typically rely on subjective, expert-based weighting and static, supply-side data, often fail to capture the complex, non-linear dynamics of actual tourist–environment interactions. To overcome these limitations, an innovative analytical framework is presented, integrating massive tourist trajectory big data (66.7 million GPS points) as an objective, demand-driven suitability proxy, a Geo-detector model to identify key drivers and their interactions, and a Random Forest algorithm for spatial prediction. The framework achieves high predictive accuracy (AUC = 0.827). The results reveal significant spatial heterogeneity: over 85% of the QTP is unsuitable for tourism, while suitable zones are intensely concentrated in southeastern river valleys, forming distinct agglomerations around core cities and along primary transport arteries. Analysis demonstrates that supporting conditions—particularly transport accessibility and service facility density—are the dominant drivers, their influence substantially surpassing that of natural resource endowment. Furthermore, the formation of high-suitability zones is not attributable to any single factor but rather to the synergistic coupling of multiple conditions. This research establishes a replicable, data-driven paradigm for tourism planning in environmentally sensitive regions, offering a robust scientific basis to guide the sustainable development of the QTP.