SCIENTIA GEOGRAPHICA SINICA ›› 2019, Vol. 39 ›› Issue (11): 1739-1748.doi: 10.13249/j.cnki.sgs.2019.11.007

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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

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

CLC Number: 

  • K901.6