Inferring the social-connectedness of locations from mobility data

Tristan Brugman, Mitra Baratchi*, Geert Heijenk, Maarten van Steen

*Corresponding author for this work

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    Abstract

    An often discriminating feature of a location is its social character or how well its visitors know each other. In this paper, we address the question of how we can infer the social contentedness of a location by observing the presence of mobile entities in it. We study a large number of mobility features that can be extracted from visits to a location. We use these features for predicting the social tie strengths of the device owners present in the location at a given moment in time, and output an aggregate score of social connectedness for that location. We evaluate this method by testing it on a real-world dataset. Using a synthetically modified version of this dataset, we further evaluate its robustness against factors that normally degrade the quality of such ubiquitously collected data (e.g. noise, sampling frequency). In each case, we found that the accuracy of the proposed method highly outperforms that of a state-of-the-art baseline methodology.

    Original languageEnglish
    Title of host publicationSocial Informatics
    Subtitle of host publication9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017
    EditorsGiovanni Luca Ciampaglia, Afra Mashhadi, Taha Yasseri
    Place of PublicationCham
    PublisherSpringer
    Pages443-457
    Number of pages15
    ISBN (Electronic)978-3-319-67256-4
    ISBN (Print)978-3-319-67255-7
    DOIs
    Publication statusPublished - 2017
    Event9th International Conference on Social Informatics, SocInfo 2017 - Oxford, United Kingdom
    Duration: 13 Sep 201715 Sep 2017

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume10540
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference9th International Conference on Social Informatics, SocInfo 2017
    CountryUnited Kingdom
    CityOxford
    Period13/09/1715/09/17

    Keywords

    • Link prediction
    • Mobility data mining
    • Mobility modeling
    • Spatial profiling
    • Wi-Fi scanning

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