The Xizang–Sichuan region is rich in tourism resources, yet its complex geography and lagging transportation infrastructure have resulted in pronounced spatial disparities in tourism development. From a sustainability perspective, such disparities can lead to a ‘rich-get-richer’ cycle: over-tourism and ecological stress in high-accessibility cores, versus underdevelopment and resource idling in low-accessibility peripheries. To systematically examine the spatial structure of tourism in this region, this study uses counties as basic units and integrates the Analytic Hierarchy Process (AHP) with an improved gravity model for tourism network potential accessibility (TNPA) to assess regional tourism development levels and the network-based accessibility among counties. A comprehensive evaluation framework was established across three dimensions: tourism resource endowment, service capacity, and socio-economic support. Travel times between county centers were obtained from the Amap API, and a TNPA index was computed to reflect each county’s potential for tourism interaction with the rest of the region. Results indicate that resource endowment dominates the evaluation, with scenic quality as the critical factor. TNPA exhibits a pronounced core–periphery differentiation, with Chengdu and surrounding areas forming a high-value network core, while most counties in Xizang show extremely low network potential. Equity analysis reveals a significant imbalance in the distribution of network accessibility between Sichuan and Xizang. Under the baseline setting, the population-weighted Gini coefficient, coefficient of variation, and Theil index reach 0.677, 1.724, and 0.884, respectively, indicating that tourism network potential remains highly concentrated in a limited number of counties. The population-weighted mean TNPA of Sichuan is also far higher than that of Xizang, revealing a distinct interregional accessibility gap. The development–TNPA mismatch analysis further identifies counties where tourism development foundations are relatively strong but network integration remains weak. Robustness checks indicate that the high-development–low-TNPA pattern is not simply an artefact of the median-based classification, with the most evident cases mainly concentrated in Aba and Garze in western Sichuan and a few counties in Xizang. The study highlights the asymmetric relationships among resource endowment, network accessibility, and equity, providing a scientific basis for optimizing cross-regional tourism cooperation and transportation corridors in the Xizang–Sichuan region.

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities.