地理科学进展

地理科学进展 ›› 2019, Vol. 38 ›› Issue (8): 1240-1247.doi: 10.18306/dlkxjz.2019.08.012

• 研究论文 • 上一篇    下一篇

基于熵测度方法的旅游季节性研究

余向洋1,3,张圆刚2,*(),朱国兴1,李德明1,王娟1   

  1. 1. 黄山学院旅游学院,安徽 黄山 245021
    2. 上海师范大学旅游学院,上海 200000
    3. 浙江农林大学旅游与健康学院,杭州 311300
  • 收稿日期:2018-12-27 修回日期:2019-03-04 出版日期:2019-08-25 发布日期:2019-08-25
  • 通讯作者: 张圆刚
  • 作者简介:余向洋(1969— ),男,博士,教授,研究方向为旅游地理学。E-mail: yxy417@163.com
  • 基金资助:
    国家自然科学基金项目(41571140);安徽省社科规划项目(AHSKY2015D66);安徽省高校人文社科重点项目(SK2016A0884);安徽省旅游局项目(AHLYZJ201615)

Tourism seasonality based on entropy measuring

YU Xiangyang1,3,ZHANG Yuangang2,*(),ZHU Guoxing1,LI Dengming1,WANG Juan1   

  1. 1. Tourism Depentment of Huangshan University, Huangshan 245021, Anhui, China
    2. Shanghai Institute of Tourism, Shanghai Normal University, Shanghai 200000, China
    3. College of Tourism and Health, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China
  • Received:2018-12-27 Revised:2019-03-04 Online:2019-08-25 Published:2019-08-25
  • Contact: ZHANG Yuangang
  • Supported by:
    National Natural Science Foundation of China(41571140);Social Science Planning Project of Anhui Province(AHSKY2015D66);Key Projects of Humanities and Social Sciences in Anhui Province(SK2016A0884);Anhui Tourism Bureau Project(AHLYZJ201615)

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摘要：

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问题,论文采用国内旅游学者尚未使用过的熵测度方法,以黄山风景区连续10 a的日接待数据为基准,深入剖析周、月、年度多个层面的季节性,结果表明：① 熵冗余指数测度旅游的年、月、周季节性具有一定的简洁性优势,并且其组合方法具有较强的政策指导意义。采用月度数据计算的熵冗余指数与基尼系数、季节强度指数具有同样的功效,其两两相关性达0.968以上;另外,利用日数据计算的月和周熵冗余指数及与其他指数的组合方法,能够判识各月、各周的季节性变动,具有微观的政策指导意义。② 熵分解方法既可以分析历年的季节性趋势,又可以观察出季节性变动趋势的内在根源。根据熵分解结果,黄山风景区季节性变动总体趋缓,月际和周际的季节性波动减弱,但月内周内的不均衡性反而增强。根据案例分析结果,将熵测度方法用于旅游季节性研究,很大程度上完善了旅游季节性研究的理论内涵和测度方法,更为重要的是提升了旅游季节性研究的政策指导作用。

关键词: 旅游季节性, 熵测度方法, 黄山风景区

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