Investigating Bimodal Clustering in Human Mobility

TL;DR

This paper uses a straightforward clustering method on large mobile phone data to analyze human movement patterns, revealing insights into how people cluster spatially and temporally during their daily activities.

Contribution

It introduces a simple clustering approach to quantify bimodal human mobility patterns from large telecommunication datasets.

Findings

Quantifies the degree of clustering in human mobility.

Assesses the contribution of inter-cluster movement to overall dispersion.

Analyzes spatial and temporal separation of mobility clusters.

Abstract

We apply a simple clustering algorithm to a large dataset of cellular telecommunication records, reducing the complexity of mobile phone users' full trajectories and allowing for simple statistics to characterize their properties. For the case of two clusters, we quantify how clustered human mobility is, how much of a user's spatial dispersion is due to motion between clusters, and how spatially and temporally separated clusters are from one another.