Understanding the spatial distribution and movement of tourists is fundamental for destination management, marketing, and infrastructure planning. Tourist flows are not random; they are shaped by destination attractiveness, transport connectivity, and spatial interactions between regions. Analyzing these patterns at both macro (inter-city) and micro (intra-attraction) levels allows stakeholders to manage overcrowding, design better itineraries, and leverage spillover benefits where a region benefits from the tourism growth of its neighbors. Furthermore, understanding location decision-making is critical for businesses, such as hotels, as location is a fixed attribute that significantly influences performance and survival.

 

Our work has extensively modeled tourist movements using advanced spatial econometrics and tracking technologies. We have investigated the “spillover effects” in tourism flows, confirming that inbound and domestic flows to Chinese cities are spatially autocorrelated and clustered in specific hot-spots like the Yangtze River Delta. Our team has developed a “Tourism Spillover Index” based on a two-stage distance-based model to quantify a region’s potential to benefit from multidestination travel. At a micro-level, we utilized open GPS-trajectory data to identify specific spatiotemporal behaviors in mountainous scenic areas, classifying movement into patterns like “proximity transfer” and “day-climbing”. We also creatively used user-generated travel tips to track discrepancies between “expected” (planned) and “actual” tourist flows, revealing how spatial constraints alter itineraries. Additionally, we examined the role of transport connectivity, finding that air transport generally has a greater influence on intercity flows than rail, though this varies by distance. Finally, regarding business location, we have analyzed hotel distribution patterns and the specific location factors that drive hotel selection, such as agglomeration effects and accessibility.