地理科学 ›› 2019, Vol. 39 ›› Issue (11): 1739-1748.doi: 10.13249/j.cnki.sgs.2019.11.007

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基于手机信令数据的南京市旅游客源地网络层级结构及区域分异研究

顾秋实1, 张海平2, 陈旻2, 谢毅3   

  1. 1.东南大学人文学院,江苏 南京 210096
    2.南京师范大学地理科学学院/虚拟地理环境教育部重点实验室/江苏省地理信息资源开发与利用协同创新中心,江苏 南京,210023
    3.南京市文化和旅游局信息中心,江苏 南京 210019
  • 收稿日期:2018-12-07 修回日期:2019-03-26 出版日期:2019-11-10 发布日期:2020-01-09
  • 作者简介:顾秋实(1982-),女,江苏如皋人,讲师,博士,主要从事旅游行为和旅游大数据研究。E-mail:101012151@seu.edu.cn.
  • 基金资助:
    南京留学人员科技创新择优项目资助

Hierarchical Network Structures and Regional Differentiations of Tourist Source Destinations of Nanjing Based on Cellular Signaling Data

Gu Qiushi1, Zhang Haiping2, Chen Min2, Xie Yi3   

  1. 1.School of Humanities, Southeast University, Nanjing 210096, Jiangsu, China
    2.School of Geography, Nanjing Normal University/Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) /Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, Jiangsu, China
    3.Nanjing Information Center of Culture and Tourism Bureau,Nanjing 210019, Jiangsu, China
  • Received:2018-12-07 Revised:2019-03-26 Online:2019-11-10 Published:2020-01-09
  • Supported by:
    Scientific and Technological Innovation Projects for Returned Scholars of Nanjing

摘要:

游客源地和目的地之间构成了一张动态的空间网络,流空间视角下的客源网络研究有助于更为真实地反映客流空间结构和客源地区位结构特征。以南京市51个景区的监测客源大数据为例,分别选取元旦、清明节、劳动节、端午节和中秋节5个时段的游客数据,采用社会空间网络聚类分析方法和空间区域划分模型,从地级市层面展开客源地网络层次结构和区域分异模式分析。结果表明:地市层面的客流强度表现出显著的空间层次结构特征,高等级节点主要位于南京的最邻近区域和次邻近区域,外生网络效应明显;不同小长假客源网络节点在全局上呈现相似的层次结构和分布模式,局部区域差异显著;区域化的客源流量表现为南北分异模式,而区域化的客流强度则呈现东西分异模式。

关键词: 旅游客源地, 网络层级结构, 区域分异模式, 客源大数据, 南京

Abstract:

This paper studies the network hierarchy and regional differentiation patterns of the source market from a city level. Based on the big data of tourist number monitored in Nanjing, 5short-period festivals are selected including New Year's Day, Qingming Festival, Labor′s Day, Dragon Boat Festival and Mid-Autumn Festival of China. The methods of overall trend analysis based on spatial variables, social space network clustering analysis and spatial regional division model are adopted. Among them, the first method can be used to determine the overall spatial distribution trend of tourist flow intensity. The second one can realize the hierarchical division of the source network nodes, so as to simultaneously examine the hierarchical structure and spatial distribution structure of the network nodes. The division model based on machine learning can be used to distinguish the tourist volume and tourist flow intensity in the source market from the city level. The results are as follows: 1) The intensity of tourist flow shows significant spatial hierarchy characteristics. The high-ranking nodes are mainly located in the most adjacent and sub-adjacent areas of Nanjing, and the exogenous network effect is also obvious. 2) Overall, the source network nodes in five different short-period national holidays show similar hierarchical structures and distribution patterns while the regional differences are apparent. 3) Although there are many differences in the spatial distribution patterns of the high level city nodes among the five short-period national holidays, the basic spatial pattern could be generalized as: the most adjacent area of ??Nanjing as the first cluster, the sub-adjacent area of Nanjing as the second cluster and the adjacent area closed to Beijing and Guangzhou as the third and fourth clusters. Those four clusters constitute the most critical tourist generating areas to Nanjing. 4) The regionalized tourist volume division is characterized by a north-south differentiation pattern, while the regionalized tourist flow intensity division exhibits an east-west differentiation pattern. The analytical results of this paper have important practical implication for deepening the zoning of tourist source in Nanjing and provide references to other destinations. It also could help to conduct accurate marketing strategy in tourism marketing and optimizing the configuration of tourism supporting facilities.

Key words: tourist source market, hierarchical network structure, regional differentiations patterns, big data of tourists, Nanjing City

中图分类号: 

  • K901.6