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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Statistical inference for complete and incomplete mobility trajectories under the flight-pause model.

Marcin Jurek1, Catherine A Calder1, Corwin Zigler1

  • 1Department of Statistics and Data Sciences, University of Texas at Austin, Austin, TX, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|January 15, 2024
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Summary

We developed a statistical flight-pause model for human mobility data. Our model addresses unique missing data challenges in mobile phone tracking, improving data analysis and collection strategies.

Keywords:
digital phenotypingmissing datasemi-Markov processspace–time processtrajectory data

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Area of Science:

  • Statistical modeling
  • Human mobility research
  • Data science

Background:

  • Mobile phone tracking (MPT) generates vast human mobility data.
  • Existing models often make invalid assumptions about missing data in MPT.
  • Understanding human movement patterns requires robust statistical frameworks.

Purpose of the Study:

  • To introduce a novel statistical flight-pause model (FPM) for human mobility.
  • To develop methods for parameter inference and trajectory imputation with missing MPT data.
  • To address an understudied missing data phenomenon in mobility data.

Main Methods:

  • Formulation of a statistical flight-pause model (FPM).
  • Development of statistical machinery for parameter inference and trajectory imputation.
  • Analysis of missing data mechanisms specific to random motions in human mobility.

Main Results:

  • Common missing data assumptions for MPT are invalid for FPM's underlying random motions.
  • Demonstrated consequences of missing data in simulations and real-world MPT datasets.
  • Proposed adjustments effectively handle the identified missing data phenomenon.

Conclusions:

  • The flight-pause model offers a more accurate representation of human mobility from MPT data.
  • Standard missing data techniques may be inappropriate for mobility data analysis.
  • Findings provide critical insights for optimizing MPT data collection and study design.