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Characterisation of Traveller Types Using Check-In Data from Location-Based Social Networks

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Information and Communication Technologies in Tourism 2019

Abstract

Characterising types of travellers can serve as a foundation for tourism recommender systems. This paper presents an approach to identify traveller types by analysing check-in data from location-based social networks. 33 million Foursquare check-ins from 266,909 users are segmented into 23,340 foreign trips based on traveller mobility patterns. Hierarchical clustering was then applied to identify distinct groups of trips by features such as travel duration, number of countries visited, radius of gyration, and the distance from home. The results revealed four clusters of trips, which manifest a novel grouping of people’s travel behaviour.

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Notes

  1. 1.

    See https://developer.foursquare.com/docs/api/venues/categories.

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Author information

Authors and Affiliations

  1. Technical University of Munich, Department of Informatics, Garching, Germany

    Linus W. Dietz, Rinita Roy & Wolfgang Wörndl

Authors
  1. Linus W. Dietz
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  2. Rinita Roy
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  3. Wolfgang Wörndl
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Corresponding author

Correspondence to Linus W. Dietz .

Editor information

Editors and Affiliations

  1. Business School, University of Eastern Finland, Joensuu, Finland

    Juho Pesonen

  2. Faculty of Informatics, TU Wien, Vienna, Austria

    Julia Neidhardt

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Dietz, L.W., Roy, R., Wörndl, W. (2019). Characterisation of Traveller Types Using Check-In Data from Location-Based Social Networks. In: Pesonen, J., Neidhardt, J. (eds) Information and Communication Technologies in Tourism 2019. Springer, Cham. https://doi.org/10.1007/978-3-030-05940-8_2

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