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Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs

Author

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  • Yong Gao

    (Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China)

  • Chao Ye

    (Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China)

  • Xiang Zhong

    (Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China)

  • Lun Wu

    (Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China)

  • Yu Liu

    (Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China)

Abstract

Spatial patterns of tourist mobility are important for tourism management and planning. A large number of traveler-generated content accumulated on the internet provide a unique opportunity for revealing comprehensive spatial patterns of tourist movements. Instead of concentrating on a single city or attraction in previous research, this work investigates the intercity travel flows extracted from the online travel blogs in China from 2012 to 2016. The descriptive statistics of travel flows are first analyzed. The distribution of travel volume is found to satisfy the power-law distribution. Based on the intercity travel flows, a network structure is then constructed to investigate tourism interactions between cities. After four communities and 14 sub-communities being detected from the network, a tourism spatial layout with regional agglomeration effects are recognized. This research concludes that distance is essential in determining tourist movements based on a spatial interaction model. Intercity travel flows decline with distance under a power-law function. These results reveal the spatial patterns of tourist movements at an intercity scale. It will be helpful for arranging tourism resources, predicting tourist flows, and maintaining sustainable tourism.

Suggested Citation

  • Yong Gao & Chao Ye & Xiang Zhong & Lun Wu & Yu Liu, 2019. "Extracting Spatial Patterns of Intercity Tourist Movements from Online Travel Blogs," Sustainability, MDPI, vol. 11(13), pages 1-18, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3526-:d:243359
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    References listed on IDEAS

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    1. Liu, Yaping & Zhang, Yu & Nie, Linlin, 2012. "Patterns of self-drive tourists: The case of Nanning City, China," Tourism Management, Elsevier, vol. 33(1), pages 225-227.
    2. CSAJI, Balazs Cs. & BROWET, Arnaud & TRAAG, V.A. & DELVENNE, Jean-Charles, 2013. "Exploring the mobility of mobile phone users," LIDAM Reprints CORE 2508, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Yu Liu & Xi Liu & Song Gao & Li Gong & Chaogui Kang & Ye Zhi & Guanghua Chi & Li Shi, 2015. "Social Sensing: A New Approach to Understanding Our Socioeconomic Environments," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(3), pages 512-530, May.
    4. Jose J Padilla & Hamdi Kavak & Christopher J Lynch & Ross J Gore & Saikou Y Diallo, 2018. "Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-20, June.
    5. Csáji, Balázs Cs. & Browet, Arnaud & Traag, V.A. & Delvenne, Jean-Charles & Huens, Etienne & Van Dooren, Paul & Smoreda, Zbigniew & Blondel, Vincent D., 2013. "Exploring the mobility of mobile phone users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1459-1473.
    6. East, Duncan & Osborne, Patrick & Kemp, Simon & Woodfine, Tim, 2017. "Combining GPS & survey data improves understanding of visitor behaviour," Tourism Management, Elsevier, vol. 61(C), pages 307-320.
    7. Hee Chung Chung & Namho Chung & Yoonjae Nam, 2017. "A Social Network Analysis of Tourist Movement Patterns in Blogs: Korean Backpackers in Europe," Sustainability, MDPI, vol. 9(12), pages 1-19, December.
    8. Kati Nilbe & Rein Ahas & Siiri Silm, 2014. "Evaluating the Travel Distances of Events Visitors and Regular Visitors Using Mobile Positioning Data: The Case of Estonia," Journal of Urban Technology, Taylor & Francis Journals, vol. 21(2), pages 91-107, April.
    9. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    10. Ruone Zhang & Xin Ye & Ke Wang & Dongjin Li & Jiayu Zhu, 2019. "Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    11. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
    12. De Cantis, Stefano & Ferrante, Mauro & Kahani, Alon & Shoval, Noam, 2016. "Cruise passengers' behavior at the destination: Investigation using GPS technology," Tourism Management, Elsevier, vol. 52(C), pages 133-150.
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