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Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

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  • Versichele, Mathias
  • de Groote, Liesbeth
  • Claeys Bouuaert, Manuel
  • Neutens, Tijs
  • Moerman, Ingrid
  • Van de Weghe, Nico

Abstract

The rapid evolution of information and positioning technologies, and their increasing adoption in tourism management practices allows for new and challenging research avenues. This paper presents an empirical case study on the mining of association rules in tourist attraction visits, registered for 15 days by the Bluetooth tracking methodology. This way, this paper aims to be a methodological contribution to the field of spatiotemporal tourism behavior research by demonstrating the potential of ad-hoc sensing networks in the non-participatory measurement of small-scale movements. An extensive filtering procedure is followed by an exploratory analysis, analyzing the discovered associations for different visitor segments and additionally visualizing them in ‘visit pattern maps’. Despite the limited duration of the tracking period, we were able to discover interesting associations and further identified a tendency of visitors to rarely combine visits in the center with visits outside of the city center. We conclude by discussing both the potential of the employed methodology as well as its further issues.

Suggested Citation

  • Versichele, Mathias & de Groote, Liesbeth & Claeys Bouuaert, Manuel & Neutens, Tijs & Moerman, Ingrid & Van de Weghe, Nico, 2014. "Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium," Tourism Management, Elsevier, vol. 44(C), pages 67-81.
    • Handle: RePEc:eee:touman:v:44:y:2014:i:c:p:67-81
    DOI: 10.1016/j.tourman.2014.02.009
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    6. Hardy, Anne & Birenboim, Amit & Wells, Martha, 2020. "Using geoinformatics to assess tourist dispersal at the state level," Annals of Tourism Research, Elsevier, vol. 82(C).
    7. Leask, Anna, 2016. "Visitor attraction management: A critical review of research 2009–2014," Tourism Management, Elsevier, vol. 57(C), pages 334-361.
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    9. 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.
    10. Tosporn Arreeras & Mikiharu Arimura & Takumi Asada & Saharat Arreeras, 2019. "Association Rule Mining Tourist-Attractive Destinations for the Sustainable Development of a Large Tourism Area in Hokkaido Using Wi-Fi Tracking Data," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    11. 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.
    12. 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.
    13. Michela Fazzolari & Marinella Petrocchi, 2018. "A study on online travel reviews through intelligent data analysis," Information Technology & Tourism, Springer, vol. 20(1), pages 37-58, December.

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