Evolving tourism activities and driving mechanism in COVID-19 era: an integrated OPGD-GTWR analysis using social media big data in Chongqing

This study proposes a methodological framework for analyzing tourism activities in COVID-19 era by integrating social media big data analytics with Optimal Parameters Geographical Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models. The framework is empirically implemented in Chongqing, China (2019-2023), revealing several key findings. First, tourism activities experienced initial decline followed by gradual recovery as restrictions eased. Second, spatial patterns of tourism activities shifted from concentration to dispersion favoring natural attractions during the pandemic. Third, a contrasting positive-negative effect was observed among the main driving factors. The densities of scenic areas, accommodation, and food services maintained a positive influence on tourism concentration. In contrast, factors such as generic green spaces, A-level scenic spots, shopping services, and intangible cultural heritage sites exhibited a negative association. Fourth, the influence of these drivers demonstrated significant spatiotemporal heterogeneity, with distinct center-periphery differences spatially and a temporal shift where traditional, enclosed tourism drivers weakened significantly during the peak of the pandemic while factors related to safety and nature persisted strongly into the recovery phase. Furthermore, combined effects of paired factors consistently exceeded individual factor influence. These findings advance theoretical understanding of crisis-driven tourism dynamics while providing implications for sustainable tourism revitalization.