JOURNAL OF NATURAL RESOURCES ›› 2020, Vol. 35 ›› Issue (5): 1068-1089.doi: 10.31497/zrzyxb.20200505

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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

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