Unveiling Dynamic Determinants of Tourism Competitiveness: Analysis Integrating Multimodal Data and Spatiotemporal Machine Learning
本研究以中国为例，整合多模态大数据与时空机器学习，分析国内和国际旅游竞争力的动态决定因素，发现线上热度对国内市场重要，而自然环境支撑国际市场，且基础设施和政府支持的作用随时间增强。
Maintaining tourism competitiveness is crucial for destinations’ long-term success. However, studies often lack longitudinal analyses that capture the evolving determinants of tourism competitiveness in both domestic and international markets. Taking China as a case study, this research introduces an innovative framework that integrates spatiotemporal machine learning with multimodal big data to examine dynamic factors influencing domestic and international tourism competitiveness. Several findings emerge: (1) overall, online popularity is essential for domestic tourism competitiveness, whereas the natural environment underpins international tourism competitiveness; (2) temporally, tourism infrastructure, government support, and online tourist perception are becoming more critical for tourism competitiveness over time, while tourism attractions’ role is waning; and (3) developed destinations are increasingly leveraging tourism infrastructure to enhance competitive advantage, and less-developed areas are benefiting more from online visibility. This research further contextualizes the dynamic nature of tourism competitiveness and offers strategies for sustainable tourism development. Also available in Chinese. See Supplemental Material for details.