Understanding the spatiotemporal dynamics of tourist volume is crucial for effective tourism management and planning. However, existing tourism data analysis methods often fail to capture the complex, continuous fluctuations and temporal variations in tourist behavior. To address this challenge, we apply functional data analysis (FDA) in the tourism industry to provide a more nuanced understanding of tourist volume dynamics. Specifically, we perform FDA on real-time tourist volume data from 56 major attractions in Beijing, China, revealing intrinsic fluctuation patterns, key factors driving tourist arrivals, and the dynamic characteristics of attractions across temporal scales. Our findings enhance the ability to optimize attraction management, marketing strategies, and policy-making, while also advancing tourism data science by integrating FDA. This approach fills the methodological gap and offers a comprehensive framework for exploring the spatiotemporal complexities of tourism data.
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