Abstract:

In view of the lack of longitudinal, in-depth analyses for tourism seasonality, this study adopted the entropy measuring method, which has not been used by Chinese tourism scholars, and analyzed weekly, monthly, and yearly seasonality based on the daily reception data of the Huangshan Scenic Area for 10 consecutive years. The results are as follows: 1) Entropy redundancy index for measuring weekly, monthly, and yearly seasonality is to some extent superiority due to its simplicity and its combination method has strong policy relevance. The entropy redundancy index calculated by monthly data has the same effect as the Gini coefficient and the seasonal intensity index, and the correlation between them is more than 0.968; in addition, the monthly and weekly entropy redundancy indices calculated by daily data and the combination with other indices can identify seasonal changes in each month and week, and have more micro policy relevance. 2) The entropy decomposition method not only can analyze the seasonal trends of past years, but also can reveal fundamental causes of seasonal fluctuations. According to the entropy decomposition results, seasonal changes in the Huangshan Scenic Area have generally slowed down, and seasonal fluctuations across months and weeks have weakened, but the imbalance within each month and each week has increased. In summary, applying the entropy measurement method to the research of tourism seasonality has greatly improved the theoretical basis and measuring methods of tourism seasonality research, and more importantly, enhanced the policy guidance role of tourism seasonality research.

Key words: tourism seasonality, entropy measuring, Huangshan Scenic Area