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The lack of high-resolution tourist volume data for specific types of tourism activities poses a substantial obstacle to quantitatively assessing the impacts of climate change. Here, a new method of generating daily tourist volume for cherry blossom viewing tourism from user-generated content (UGC) was proposed, based on which the daily tourist volume for cherry blossom viewing in 220 cities across China from 2010 to 2019 was generated and verified. Then, generalized additive model (GAM) and segmented regression were introduced to reveal the non-linear and threshold relationships between daily tourist volume and temperature. Finally, the trends in the daily tourist volume from 2020 to 2050 were simulated under two future climate change scenarios SSP2-4.5 and SSP5-8.5 (Shared Socioeconomic Pathway, SSP). The proposed method can provide methodological support for analyzing other specific types of tourism activities by generating alternative data on tourist volume.
Wang et al. (Thu,) studied this question.