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Modelling spatio-temporal movement of tourists using finite Markov chains

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  • Xia, Jianhong (Cecilia)
  • Zeephongsekul, Panlop
  • Arrowsmith, Colin

Abstract

This paper presents a novel method for modelling the spatio-temporal movements of tourists at the macro-level using Markov chains methodology. Markov chains are used extensively in modelling random phenomena which results in a sequence of events linked together under the assumption of first-order dependence. In this paper, we utilise Markov chains to analyse the outcome and trend of events associated with spatio-temporal movement patterns. A case study was conducted on Phillip Island, which is situated in the state of Victoria, Australia, to test whether a stationary discrete absorbing Markov chain could be effectively used to model the spatio-temporal movements of tourists. The results obtained showed that this methodology can indeed be effectively used to provide information on tourist movement patterns. One significant outcome of this research is that it will assist park managers in developing better packages for tourists, and will also assist in tracking tourists’ movements using simulation based on the model used.

Suggested Citation

  • Xia, Jianhong (Cecilia) & Zeephongsekul, Panlop & Arrowsmith, Colin, 2009. "Modelling spatio-temporal movement of tourists using finite Markov chains," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(5), pages 1544-1553.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:5:p:1544-1553
    DOI: 10.1016/j.matcom.2008.06.007
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    2. Angela Chantre-Astaiza & Laura Fuentes-Moraleda & Ana Muñoz-Mazón & Gustavo Ramirez-Gonzalez, 2019. "Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability," Sustainability, MDPI, vol. 11(17), pages 1-32, August.
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    5. Xia, Jianhong (Cecilia) & Zeephongsekul, Panlop & Packer, David, 2011. "Spatial and temporal modelling of tourist movements using Semi-Markov processes," Tourism Management, Elsevier, vol. 32(4), pages 844-851.
    6. Vu, Huy Quan & Li, Gang & Law, Rob & Ye, Ben Haobin, 2015. "Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos," Tourism Management, Elsevier, vol. 46(C), pages 222-232.
    7. Jing Qin & Ci Song & Mingdi Tang & Youyin Zhang & Jinwei Wang, 2019. "Exploring the Spatial Characteristics of Inbound Tourist Flows in China Using Geotagged Photos," Sustainability, MDPI, vol. 11(20), pages 1-17, October.
    8. Shah Jahan Miah & HuyQuan Vu & John Gammack, 2019. "A big-data analytics method for capturing visitor activities and flows: the case of an island country," Information Technology and Management, Springer, vol. 20(4), pages 203-221, December.
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    10. 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.
    11. Ljubica Knezevic Cvelbar & Mojca Mayr & Damjan Vavpotic, 2018. "Geographical mapping of visitor flow in tourism," Tourism Economics, , vol. 24(6), pages 701-719, September.
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    13. Ding Ding & Yunhao Zheng & Yi Zhang & Yu Liu, 2024. "Understanding attractions’ connection patterns based on intra-destination tourist mobility: A network motif approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    14. Vassiliadis, Chris A. & Priporas, Constantinos-Vasilios & Andronikidis, Andreas, 2013. "An analysis of visitor behaviour using time blocks: A study of ski destinations in Greece," Tourism Management, Elsevier, vol. 34(C), pages 61-70.
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