Spatiotemporal Patterns of Tourist Flow in Beijing and Their Influencing Factors: An Investigation Using Digital Footprint

Xiaoyuan Zhang, Jinlian Shi, Qijun Yang, Xinru Chen, Xiankai Huang, Lei Kong () and Dandan Gu ()
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Xiaoyuan Zhang: Business School, Beijing Technology and Business University, Beijing 100048, China
Jinlian Shi: Business School, Beijing Technology and Business University, Beijing 100048, China
Qijun Yang: Business School, Beijing Technology and Business University, Beijing 100048, China
Xinru Chen: Business School, Beijing Technology and Business University, Beijing 100048, China
Xiankai Huang: Business School, Beijing Technology and Business University, Beijing 100048, China
Lei Kong: Institute for Cultural and Tourism Development, Beijing Technology and Business University, Beijing 100048, China
Dandan Gu: Institute for Cultural and Tourism Development, Beijing Technology and Business University, Beijing 100048, China

Sustainability, 2025, vol. 17, issue 15, 1-21

Abstract: Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era.

Keywords: digital footprint; tourist flow; gravity center; spatiotemporal evolution; social network analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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