IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i2p947-d482529.html
   My bibliography  Save this article

Study on Tourism Flow Network Patterns on May Day Holiday

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/2/947/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/2/947/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    7. Alderighi, Marco & Gaggero, Alberto A., 2019. "Flight availability and international tourism flows," Annals of Tourism Research, Elsevier, vol. 79(C).
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Yuewei Wang & Cong Lu & Hang Chen & Yuyan Zhao, 2022. "Evaluation and Spatial Characteristics of Cooperation among Tourist Attractions Based on a Geographic Information System: A Case Study of The Yangtze River Delta Region, China," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    5. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    2. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
    3. Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
    4. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    5. Salvatore Costantino & Maria Francesca Cracolici & J. Paul Elhorst, 2023. "A spatial origin‐destination approach for the analysis of local tourism demand in Italy," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 393-419, April.
    6. María Cristina Rodríguez Rangel & Marcelino Sánchez Rivero, 2020. "Spatial Imbalance Between Tourist Supply and Demand: The Identification of Spatial Clusters in Extremadura, Spain," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    7. Kádár, Bálint & Gede, Mátyás, 2021. "Tourism flows in large-scale destination systems," Annals of Tourism Research, Elsevier, vol. 87(C).
    8. Gunter, Ulrich & Önder, Irem & Smeral, Egon, 2019. "Scientific value of econometric tourism demand studies," Annals of Tourism Research, Elsevier, vol. 78(C), pages 1-1.
    9. Jiao, Xiaoying & Chen, Jason Li & Li, Gang, 2021. "Forecasting tourism demand: Developing a general nesting spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 90(C).
    10. Hong, Tao & Ma, Tao & Huan, Tzung-Cheng (T.C.), 2015. "Network behavior as driving forces for tourism flows," Journal of Business Research, Elsevier, vol. 68(1), pages 146-156.
    11. Eugenio-Martin, Juan L. & Cazorla-Artiles, José M., 2020. "The shares method for revealing latent tourism demand," Annals of Tourism Research, Elsevier, vol. 84(C).
    12. Eujin-Julia Kim & Yongjun Jo & Youngeun Kang, 2018. "Are Touristic Attractions Well-Connected in an Olympic Host City? A Network Analysis Measurement of Visitor Movement Patterns in Gangneung, South Korea," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    13. Eujin-Julia Kim & Youngeun Kang, 2020. "Spillover Effects of Mega-Events: The Influences of Residence, Transportation Mode, and Staying Period on Attraction Networks during Olympic Games," Sustainability, MDPI, vol. 12(3), pages 1-13, February.
    14. Tao Liu & Ying Zhang & Huan Zhang & Xiping Yang, 2021. "A Methodological Workflow for Deriving the Association of Tourist Destinations Based on Online Travel Reviews: A Case Study of Yunnan Province, China," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    15. Lorenzo Ductor & Sanjeev Goyal & Anja Prummer, 2018. "Gender & Collaboration," Working Papers 856, Queen Mary University of London, School of Economics and Finance.
    16. Cilem Selin Hazir & Corinne Autant-Bernard, 2012. "Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology," Working Papers 1212, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    17. Lorenzo Cassi & Anne Plunket, 2014. "Proximity, network formation and inventive performance: in search of the proximity paradox," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(2), pages 395-422, September.
    18. Laurent R. Bergé, 2017. "Network proximity in the geography of research collaboration," Papers in Regional Science, Wiley Blackwell, vol. 96(4), pages 785-815, November.
    19. Önder, Ali Sina & Schweitzer, Sascha & Yilmazkuday, Hakan, 2021. "Specialization, field distance, and quality in economists’ collaborations," Journal of Informetrics, Elsevier, vol. 15(4).
    20. Corinne Autant‐Bernard & Pascal Billand & David Frachisse & Nadine Massard, 2007. "Social distance versus spatial distance in R&D cooperation: Empirical evidence from European collaboration choices in micro and nanotechnologies," Papers in Regional Science, Wiley Blackwell, vol. 86(3), pages 495-519, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:947-:d:482529. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.