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Identifying macroscopic features in foreign visitor travel pathways

Author

Listed:
  • Tatsuro Kawamoto

    (National Institute of Advanced Industrial Science and Technology)

  • Ryutaro Hashimoto

    (Waseda University)

Abstract

Human travel patterns are commonly studied as networks in which the points of departure and destination are encoded as nodes and the travel frequency between two points is recorded as a weighted edge. However, because travelers often visit multiple destinations, which constitute pathways, an analysis incorporating pathway statistics is expected to be more informative over an approach based solely on pairwise frequencies. Hence, in this study, we apply a higher-order network representation framework to identify characteristic travel patterns from foreign visitor pathways in Japan. We expect that the results herein are mainly useful for marketing research in the tourism industry.

Suggested Citation

  • Tatsuro Kawamoto & Ryutaro Hashimoto, 2021. "Identifying macroscopic features in foreign visitor travel pathways," The Japanese Economic Review, Springer, vol. 72(1), pages 129-144, January.
    • Handle: RePEc:spr:jecrev:v:72:y:2021:i:1:d:10.1007_s42973-020-00058-4
    DOI: 10.1007/s42973-020-00058-4
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    References listed on IDEAS

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    4. Martin Rosvall & Alcides V. Esquivel & Andrea Lancichinetti & Jevin D. West & Renaud Lambiotte, 2014. "Memory in network flows and its effects on spreading dynamics and community detection," Nature Communications, Nature, vol. 5(1), pages 1-13, December.
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