Distorted insights from human mobility data

Abstract The description of human mobility is at the core of many fundamental applications ranging from urbanism and transportation to epidemics containment. Data about human movements, once scarce, is now widely available thanks to new sources such as phone call detail records, GPS devices, or Smartphone apps. Nevertheless, it is still common to rely on a single dataset by implicitly assuming that the statistical properties observed are robust regardless of data gathering and processing techniques. Here, we test this assumption on a broad scale by comparing human mobility datasets obtained from 7 different data-sources, tracing 500+ millions individuals in 145 countries. We report wide quantifiable differences in the resulting mobility networks and in the displacement distribution. These variations impact processes taking place on these networks like epidemic spreading. Our results point to the need for disclosing the data processing and, overall, to follow good practices to ensure robust and reproducible results.

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Institutions

Publication Details

Journal
Communications Physics
Published
2024-12-24
DOI
https://doi.org/10.1038/s42005-024-01909-x
Citations
14
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
9.57

Funders

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article

Distorted insights from human mobility data

Davide Maniscalco, Manlio De Domenico, Riccardo Gallotti, Marc Barthélemy
14 citations
Communications Physics
Human Mobility and Location-Based Analysis
9.57
article

Distorted insights from human mobility data

Davide Maniscalco, Manlio De Domenico, Riccardo Gallotti, Marc Barthélemy
article en
14 citations

Abstract

Abstract The description of human mobility is at the core of many fundamental applications ranging from urbanism and transportation to epidemics containment. Data about human movements, once scarce, is now widely available thanks to new sources such as phone call detail records, GPS devices, or Smartphone apps. Nevertheless, it is still common to rely on a single dataset by implicitly assuming that the statistical properties observed are robust regardless of data gathering and processing techniques. Here, we test this assumption on a broad scale by comparing human mobility datasets obtained from 7 different data-sources, tracing 500+ millions individuals in 145 countries. We report wide quantifiable differences in the resulting mobility networks and in the displacement distribution. These variations impact processes taking place on these networks like epidemic spreading. Our results point to the need for disclosing the data processing and, overall, to follow good practices to ensure robust and reproducible results.

Communications PhysicsVol. 7(1)
Centre National de la Recherche Scientifique (FR), University of Padua (IT), Inserm (FR), Istituto Nazionale di Fisica Nucleare (IT), Fondazione Bruno Kessler (IT), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Istituto Nazionale di Fisica Nucleare, Galileo Galilei Institute for Theoretical Physics (IT), Sorbonne Université (FR), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT), CEA Paris-Saclay (FR), Institut Pierre Louis d‘Épidémiologie et de Santé Publique (FR), Institut de Physique Théorique (FR), École des hautes études en sciences sociales (FR)
National Research Centre, Ministero dell’Istruzione, dell’Università e della Ricerca
Openalex Percentile: Top 2%
Human Mobility and Location-Based Analysis
9.57
DOIOpen Access PDFOpenAlex
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