Correlations Between Human Mobility and Social Interaction Reveal General Activity Patterns
Anders Mollgaard, Sune Lehmann, Joachim Mathiesen

TL;DR
This study analyzes smartphone data from 638 individuals to uncover shared activity patterns across communication, mobility, and proximity, revealing that population-wide models can predict most behaviors with simple linear models, indicating common underlying principles.
Contribution
The paper demonstrates that population-level activity patterns are highly predictive of individual behaviors and that simple linear models can effectively describe diverse activity types.
Findings
Population-wide models predict 71% of activity and 85% of inactivity.
Individual patterns only marginally improve prediction accuracy.
A simple linear model suffices to explain various social and physical activities.
Abstract
A day in the life of a person involves a broad range of activities which are common across many people. Going beyond diurnal cycles, a central question is: to what extent do individuals act according to patterns shared across an entire population? Here we investigate the interplay between different activity types, namely communication, motion, and physical proximity by analyzing data collected from smartphones distributed among 638 individuals. We explore two central questions: Which underlying principles govern the formation of the activity patterns? Are the patterns specific to each individual or shared across the entire population? We find that statistics of the entire population allows us to successfully predict 71\% of the activity and 85\% of the inactivity involved in communication, mobility, and physical proximity. Surprisingly, individual level statistics only result in…
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