自然资源学报 ›› 2020, Vol. 35 ›› Issue (5): 1068-1089.doi: 10.31497/zrzyxb.20200505

• 研究论文 • 上一篇    下一篇

中国红色旅游网络关注度时空特征及影响因素

高楠1, 张新成2, 王琳艳3   

  1. 1.山西财经大学文化旅游学院,太原 030031;
    2.西北大学经济管理学院,西安 710127;
    3.西安理工大学经济与管理学院,西安 710054
  • 收稿日期:2019-06-27 出版日期:2020-05-28 发布日期:2020-05-28
  • 作者简介:高楠(1982- ),男,山西稷山人,博士,副教授,主要从事旅游目的地运营与管理研究。E-mail: gaonan0901@163.com
  • 基金资助:
    国家社会科学基金项目(19BGL141)

Spatio-temporal characteristics and influencing factors of Chinese red tourism network attention

GAO Nan1, ZHANG Xin-cheng2, WANG Lin-yan3   

  1. 1. College of Culture Tourism, Shanxi University of Finance and Economics, Taiyuan 030031, China;
    2. School of Economics and Management, Northwest University, Xi'an 710127, China;
    3. School of Economics and Management, Xi'an University of Technology, Xi'an 710054, China
  • Received:2019-06-27 Online:2020-05-28 Published:2020-05-28

摘要: 红色旅游网络关注度是红色旅游宣传推广水平的一种典型测量手段,也是红色旅游影响力的重要反映。以31个省(区、市)“红色旅游网络关注度”为研究对象,运用莫兰指数、面板向量自回归模型等对2011—2018年中国红色旅游网络关注度的时空特征及其影响因素进行研究。结果表明:(1)2011—2018年全国红色旅游网络关注度呈现波动增长趋势,且季节性差异显著;(2)全国红色旅游网络关注度差异性显著,呈现东部—中部—西部依次递减的趋势,但西部红色旅游5A级景区关注度上升态势凸显;(3)各省(区、市)红色旅游网络关注度具有显著的全局空间自相关性,“高—高”“低—低”集聚现象分别集中于东、中部和西部地区;(4)红色旅游网络关注度影响因素的贡献度大小排序为:互联网普及率>人均GDP>旅游信息化指数>区域媒体关注度>红色旅游经典景区网络关注度。

关键词: 红色旅游, 影响因素, 网络关注度, PVAR模型

Abstract: Red tourism network attention degree is a typical method for measuring the performance level of red tourism development promotion, and it is also an important reflection of the influence of red tourism promotion level. This paper takes the "red tourism network attention degree" of 31 provincial-level regions as the research object, and uses the Moran index and panel vector autoregressive model to analyze the spatio-temporal characteristics and the influencing factors of the red tourism network in China from 2011 to 2018. The results show: (1) The network attention of the national red tourism shows a fluctuant growth trend in the study period, and its seasonal difference is significant. (2) The national red tourism network attention shows a decreasing trend from the eastern to central and western regions, but the trend of attention in the red tourism 5A-level tourist attractions is prominent in the western region. (3) The attention of red tourism networks in the 31 provincial-level regions has significant global spatial autocorrelation. The phenomenon of "high-high" and "low-low" agglomerations is concentrated in the eastern region, and central and western region. (4) The contribution of the factors affecting red tourism network attention was ranked as follows: internet penetration rate > per capita GDP > tourism information index > regional media attention > red tourism classic scenic network attention.

Key words: red tourism, network attention, PVAR model, influencing factor