Scaling Behavior of Human Mobility Distributions

  • Tuhin PaulEmail author
  • Kevin Stanley
  • Nathaniel Osgood
  • Scott Bell
  • Nazeem Muhajarine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9927)


Recent technical advances have made high-fidelity tracking of populations possible. However, these datasets, such as GPS traces, can be comprised of millions of records, well beyond what even a skilled analyst can digest. To facilitate human analysis, these records are often expressed as aggregate distributions capturing behaviors of interest. While these aggregate distributions can provide substantial insight, the spatio-temporal resolution at which they are captured can impact the shape of the resulting distribution. We present an analysis of five spatial datasets, and codify the impact of rebinning the data at different spatio-temporal resolutions. We find that all aggregate metrics considered are affected by rebinning, but that some distributions do so regularly and predictably, while others do not. This work provides important insight into which metrics can be used to compare human behavior across datasets and the kinds of relationships between that can be expected.


Spatial data Mobility GPS Analytics 



We would like to acknowledge the Natural Sciences and Engineering Research Council of Canada for providing funding for this work.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tuhin Paul
    • 1
    Email author
  • Kevin Stanley
    • 1
  • Nathaniel Osgood
    • 1
    • 2
  • Scott Bell
    • 3
  • Nazeem Muhajarine
    • 2
  1. 1.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
  2. 2.Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
  3. 3.Department of Geography and PlanningUniversity of SaskatchewanSaskatoonCanada

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