Uncovering the spatial structure of mobility networks

TL;DR

This paper introduces a versatile method to simplify and analyze large mobility networks by categorizing flows into four types, revealing differences in commuting patterns across Spanish cities based on mobile phone data.

Contribution

The paper presents a new coarse-grained approach to analyze origin-destination matrices, enabling classification of cities by their mobility network structure.

Findings

Cities differ mainly in the proportion of integrated versus random flows.

Random flows increase with city size.

The method effectively classifies cities by commuting patterns.

Abstract

The extraction of a clear and simple footprint of the structure of large, weighted and directed networks is a general problem that has many applications. An important example is given by origin-destination matrices which contain the complete information on commuting flows, but are difficult to analyze and compare. We propose here a versatile method which extracts a coarse-grained signature of mobility networks, under the form of a $2\times 2$ matrix that separates the flows into four categories. We apply this method to origin-destination matrices extracted from mobile phone data recorded in thirty-one Spanish cities. We show that these cities essentially differ by their proportion of two types of flows: integrated (between residential and employment hotspots) and random flows, whose importance increases with city size. Finally the method allows to determine categories of networks, and in the mobility case to classify cities according to their commuting structure.