Analyzing tourists’ spatiotemporal behavior using GPS trajectory data: a case study of Sanya, China

Examining the spatiotemporal behavior (STB) of tourists is crucial for enhancing tourism destination planning and improving service levels. In this study, the STB of tourists in Sanya, China, was analyzed using trajectory data provided by the Six Feet platform. Grid-based method, kernel density analysis, and global spatial auto-correlation method were employed to investigate tourist behavior. Key findings include: (1) Spatial Distribution of Tourism Resources: Based on the analysis of tourism POIs, the spatial distribution characteristics of tourism resources in Sanya were identified. By matching the results with online maps, specific scenic areas with a high concentration of tourism POIs were pinpointed. (2) Seasonal Trajectory Density: Kernel density analysis revealed areas with dense tourist trajectories across different seasons, highlighting seasonal variations in tourist activity. (3) Popular Scenic Areas: The analysis identified several highly popular scenic areas, including Coconut Dream Corridor, Luhuitou Peak Park, Dadonghai Tourist Area, and Yalong Bay National Resort. (4) Temporal Behavior: Analysis of travel time data indicated that most tourists prefer visiting Sanya during the winter and spring seasons. These insights provide valuable empirical evidence for optimizing tourism resource allocation, improving destination planning, and enhancing tourist experience.