Statistical Inference for Complete and Incomplete Mobility Trajectories under the Flight-Pause Model

TL;DR

This paper introduces a statistical flight-pause model for human mobility based on mobile phone tracking data, addressing missing data issues and proposing inference methods that improve trajectory analysis.

Contribution

It develops a novel statistical framework for parameter inference and trajectory imputation under complex missing data mechanisms in mobility data.

Findings

Common missing data assumptions are invalid for the flight-pause model.

Proposed adjustments improve inference accuracy in simulations.

Implications for better MPT data collection and analysis.

Abstract

We formulate a statistical flight-pause model for human mobility, represented by a collection of random objects, called motions, appropriate for mobile phone tracking (MPT) data. We develop the statistical machinery for parameter inference and trajectory imputation under various forms of missing data. We show that common assumptions about the missing data mechanism for MPT are not valid for the mechanism governing the random motions underlying the flight-pause model, representing an understudied missing data phenomenon. We demonstrate the consequences of missing data and our proposed adjustments in both simulations and real data, outlining implications for MPT data collection and design.