Inferring the Social-Connectedness of Locations from Mobility Data

  • Tristan Brugman
  • Mitra Baratchi
  • Geert Heijenk
  • Maarten van Steen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)

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.

Keywords

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

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Tristan Brugman
    • 1
  • Mitra Baratchi
    • 1
  • Geert Heijenk
    • 1
  • Maarten van Steen
    • 1
  1. 1.University of TwenteEnschedeThe Netherlands

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