From centre to centres: polycentric structures in individual mobility

TL;DR

This paper introduces a polycentric approach to analyzing individual mobility patterns, addressing limitations of the radius of gyration by capturing multiple centers of activity, and demonstrates its effectiveness on large datasets.

Contribution

It proposes a generalized polycentric radius of gyration and a new metric, enabling better characterization of complex mobility patterns than traditional monocentric models.

Findings

Polycentric models better capture mobility features than monocentric ones.

The new metrics effectively identify multiple activity centers.

Application to large datasets shows improved mobility description.

Abstract

The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.