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Study on Tourism Flow Network Patterns on May Day Holiday

Author

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  • Shanshan Wu

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Lucang Wang

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

  • Haiyang Liu

    (College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China)

Abstract

The development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distribution characteristics and rules of tourism flow in China, providing suggestions for the development of tourism. The results demonstrate that the 361 cities studied can be divided into three types: destination-oriented, tourist-origin-oriented, and destination-oriented and tourist-origin-oriented. There are significant differences in the quantity of flow, the area of concentration, and the factors affecting the flow in the three types of cities. The larger the flow of tourism between cities, the higher the network level, and the wider the network range. The high-level nodes are closely related, while the peripheral nodes are more widely distributed, with weak attractiveness and inconvenient traffic, forming a “core-edge” structure. Different network patterns are established for different modes of transportation. The degree of response of different types of transportation to distance is the main factor influencing the network patterns of diverse paths. These findings have practical implications for the choice of appropriate travel destinations and transportation modes for tourists.

Suggested Citation

  • Shanshan Wu & Lucang Wang & Haiyang Liu, 2021. "Study on Tourism Flow Network Patterns on May Day Holiday," Sustainability, MDPI, vol. 13(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:947-:d:482529
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    References listed on IDEAS

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    1. Yin, Ping & Lin, Zhibin & Prideaux, Bruce, 2019. "The impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin," Journal of Transport Geography, Elsevier, vol. 80(C).
    2. Lee, Sang-Hyun & Choi, Jin-Yong & Yoo, Seung-Hwan & Oh, Yun-Gyeong, 2013. "Evaluating spatial centrality for integrated tourism management in rural areas using GIS and network analysis," Tourism Management, Elsevier, vol. 34(C), pages 14-24.
    3. Marcel Fafchamps & Sanjeev Goyal & Marco J. van der Leij, 2010. "Matching and Network Effects," Journal of the European Economic Association, MIT Press, vol. 8(1), pages 203-231, March.
    4. Juan Ignacio Pulido-Fernández & Jairo Casado-Montilla & Isabel Carrillo-Hidalgo, 2020. "Understanding the Behaviour of Olive Oil Tourists: A Cluster Analysis in Southern Spain," Sustainability, MDPI, vol. 12(17), pages 1-21, August.
    5. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    6. Alderighi, Marco & Gaggero, Alberto A., 2019. "Flight availability and international tourism flows," Annals of Tourism Research, Elsevier, vol. 79(C).
    7. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    8. Chua, Alvin & Servillo, Loris & Marcheggiani, Ernesto & Moere, Andrew Vande, 2016. "Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy," Tourism Management, Elsevier, vol. 57(C), pages 295-310.
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    Cited by:

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    2. Yuewei Wang & Mengmeng Xi & Hang Chen & Cong Lu, 2022. "Evolution and Driving Mechanism of Tourism Flow Networks in the Yangtze River Delta Urban Agglomeration Based on Social Network Analysis and Geographic Information System: A Double-Network Perspective," Sustainability, MDPI, vol. 14(13), pages 1-21, June.
    3. Yingtong Chen & Fei Wu & Dayong Zhang & Qiang Ji, 2024. "Tourism in pandemic: the role of digital travel vouchers in China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    4. Tianzhi Liu & Fen Luo & Jiawen He, 2023. "Evolution of Spatial Structure of Tourist Flows for a Domestic Destination: A Case Study of Zhangjiajie, China," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    5. Yuzhen Li & Guofang Gong & Fengtai Zhang & Lei Gao & Yuedong Xiao & Xingyu Yang & Pengzhen Yu, 2022. "Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    6. Huixin Gong & Yaomin Zheng & Jinlian Shi & Jiaxin Wang & Huize Yang & Sinead Praise A. Sibalo & Amani Mwamlima & Jingyu Li & Shuting Xu & Dandan Xu & Xiankai Huang, 2023. "An Examination of the Spatial Spillover Effects of Tourism Transportation on Sustainable Development from a Multiple-Indicator Cross-Perspective," Sustainability, MDPI, vol. 15(5), pages 1-20, March.

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