旅游季节性测度框架及其实证研究

An Analytical Framework and Empirical Study for Tourism Seasonality

针对旅游季节性研究缺乏深入细致的长期纵向跟踪研究和测度方法众多等问题,利用黄山风景区长达10年的日接待数据,在归纳出旅游季节性测度框架的基础上,将熵测度方法及其组合分析方法用于旅游季节性测度,深入分析黄山风景区旅游季节性。研究发现:①旅游季节性模式方面。运用图示和以均值为核心的描述统计方法,能够直观地体现季节性的峰谷形态、周末效应和假日效应。②季节性均衡程度(离散程度)方面,季节变动指数测度出年内的旺季、平季和淡季,能为管理者提供概念性管理工具。③在模型测度方面,熵测度方法具有较大优势,运用年、月、周熵冗余指数分析年内、月内和周内的季节性,与其它方法相比较,更加深入细致,政策指导意义更强;熵分解方法,能够剖析历年季节性变化的内在根源,能进一步提升政策指导意义。

Seasonality in tourism is a hot topic in academic literature, but it is lack of a longitudinal, in-depth analysis.According to the tourist data of Huangshan Scenic Area every day from 2008 to 2017 and based on the analytical framework of tourism seasonality, this article applies entropy measure method and combinational analysis to measure and deeply analyze the tourism seasonality of Huangshan Scenic Area. The results are shown as follows: 1) On seasonal pattern, it, based on mean values, is able to visually reflect seasonal peak-valley patterns, weekend effects, and holiday effects by using graph, table and other descriptive statistical methods. 2) On seasonal amplitude, it measures peak season,off-season and shoulder season of the year by the seasonal variation index, which provides conceptual tool for tourism managers. 3) On measuring model, compared with other methods, the entropy measurement method which can analyze the seasonality within the year, month and week based on the annual, monthly and weekly entropy redundancy index is more in-depth and more meaningful for the policy guidance. The entropy decomposition method can analyze the fundamental causes of seasonal changes over the years and further enhance the policy guiding significance. According to the results of the case study, the measuring framework and analysis method of tourism seasonality in this article have certain scientific and practical values. To a large extent, the theoretical connotation of tourism seasonality research is perfected, and more importantly, the effect of policy guidance on tourism seasonality research is improved.