About the source data

Understanding the source and methodology behind the migration flow visualizations

Disclaimer: This visualization is an independent project and we have no affiliation with the research authors or their institutions. Further more, this data represents all types of migration (from aggregated data from Facebook) and is therefore a mix of both regular and irregular migration and should not be used for any policy decisions.

Research Source
Foundational research and methodology

"Measuring global migration flows using online data"

Authors: G. Chi, G.J. Abel, D. Johnston, E. Giraudy, & M. Bailey

Publication: Proc. Natl. Acad. Sci. U.S.A. 122 (18) e2409418122 (2025)

PNAS 2025
Peer-reviewed
View Research Paper
Data Coverage
Temporal and geographic scope

Time Period

2019-2022

Granularity

Monthly flows

Coverage

Global migration patterns

Source

Online data aggregation

Data Availability & Resources
Access to data, replication materials, and documentation

Aggregated Data

Anonymized migration flow estimates available publicly

HDX Platform

Replication Materials

Scripts and code to reproduce paper figures

Harvard Dataverse

Documentation

Complete methodology and supplementary materials

PNAS Appendix

Privacy Note: Individual-level data used to construct these estimates are not publicly available due to data provider restrictions. Only anonymized, aggregated migration flow estimates are accessible.

Methodology Overview
How global migration flows are measured using online data

This research represents an approach to measuring global migration flows by leveraging online data sources. The methodology provides unprecedented temporal resolution (monthly flows) and global coverage, offering new insights into migration patterns and seasonal variations.

Innovation

Uses online data to overcome traditional migration data limitations, providing more timely and granular insights.

Validation

Methods validated against traditional migration statistics and administrative records where available.

We gratefully acknowledge the authors for making their research and data accessible to the scientific community, enabling projects like this to build upon their important work. 🌍