长江流域资源与环境 >> 2019, Vol. 28 >> Issue (08): 1811-1822.doi: 10.11870/cjlyzyyhj201908006

• 区域可持续发展 • 上一篇    下一篇

上海与周边客源地间的旅游业经济联系——基于微博受众群体短期出行类型分析

王璐玮1,夏四友1,储姗姗1,王苏鹏2,汪  涛1*   

  1. (1. 南京师范大学地理科学学院,江苏 南京 210046; 2.河海大学环境学院,江苏 南京 210024)
  • 出版日期:2019-08-20 发布日期:2019-08-19

Tourism Economic Linkage Between Shanghai and the Surrounding Toursit Sources: Analysis of Short-term Travel Types Based on Weibo Users

WANG Lu-wei1, XIA Si-you1, CHU Shan-shan1, WANG Su-peng2, WANG Tao1   

  1. (1.School of Geographic Sciences, Nanjing Normal University, Nanjing 210046, China; 2.School of Environmental Science and Engineering, Hehai University, Nanjing 210024, China)
  • Online:2019-08-20 Published:2019-08-19

摘要: 以上海周边地区的新浪微博用户为研究对象,采集其有关上海的博文和评论,通过词频统计、高频词共现网络、出行特征集合以及客源地经济差异函数,分析以上海为目的地的周边地区人口短期出行类型,探究上海与周边地区间旅游业经济联系的辐射、接受作用及其协调性,从短期旅游视角为上海旅游业经济发展提出合理建议。结果表明:(1)不同微博群体出行上海的频次有明显不同,客源地与上海旅游业经济的辐射力、接受力及其差量、效率既存在差别,又在一定范围内具有一致性,故将上海周边客源地划分为5类,其中合肥、嘉兴、舟山、南通表现最佳;(2)以医疗为目的和周期性商务、学习交流为主的群体对上海旅游业经济有稳定的促进作用,且周期性商务、学习交流群体的信息、出行成本最低,其他类型成本呈“点状”或“梯度”分异;(3)道路通达性与客源地经济差异系数拟合曲线呈“反抛物线”型,其对客源地与上海的旅游业经济协调性有明显影响。

Abstract: The data of blog post and comments from Sina Weibo users in Shanghai and its surrounding areas were collected. By using of word frequency statistics, high frequency co-occurrence network, travel features set and tourist source economic different function, short-term travel types around Shanghai were classified, to explore the coordination and acceptance between Shanghai and surrounding areas. This study may provide reasonable suggestions for the development of tourism in Shanghai through a short-term travel perspective. First, the results disclosed that there are significant differences in the frequency of different Weibo user groups travel to Shanghai, radiation, acceptance, difference and efficiency of the tourism economy between tourist source and Shanghai were also presented significant different. While, they presented consistent in a certain range. Therefore, the source of tourists around Shanghai was divided into five categories, of which Hefei, Jiaxing, Zhoushan and Nantong performed best. Second, aim to medical, cyclical business, and learning exchanges have a stable role in promoting Shanghai’s tourism economy. The costs of information and travel were lower in the cyclical business and learning exchange. Other types of costs presented “dotted” or “gradient” distribution. Furthermore, we found that the fitting curve of the road accessibility and the economic difference coefficient of the source area presented “anti-parabolic”, which has a significant positive impact on the coordination of tourist sources and Shanghai tourism economy.

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