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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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    Cited by:

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    5. 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.
    6. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    7. 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.
    8. Shoval, Noam & Kahani, Alon & De Cantis, Stefano & Ferrante, Mauro, 2020. "Impact of incentives on tourist activity in space-time," Annals of Tourism Research, Elsevier, vol. 80(C).
    9. Jun Shao & Xuesong Chang & Alastair M. Morrison, 2017. "How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan," Sustainability, MDPI, vol. 9(12), pages 1-17, December.
    10. 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.
    11. 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.
    12. Ljubica Knezevic Cvelbar & Mojca Mayr & Damjan Vavpotic, 2018. "Geographical mapping of visitor flow in tourism," Tourism Economics, , vol. 24(6), pages 701-719, September.
    13. 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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    15. 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.

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