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

  • Linus W. DietzEmail author
  • Rinita Roy
  • Wolfgang Wörndl
Conference paper

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.

Keywords

Data mining Cluster analysis Human mobility patterns Tourism Recommender systems 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Technical University of MunichDepartment of InformaticsGarchingGermany

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