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Inferring fine-grained migration patterns across the United States
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  • Published: 26 December 2025

Inferring fine-grained migration patterns across the United States

  • Gabriel Agostini  ORCID: orcid.org/0009-0004-0038-22611,
  • Rachel Young2,3,4,
  • Maria Fitzpatrick5,
  • Nikhil Garg1 &
  • …
  • Emma Pierson  ORCID: orcid.org/0000-0002-6149-55673 

Nature Communications , Article number:  (2025) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Geography
  • Social sciences

Abstract

Fine-grained migration data illuminate demographic, environmental, and health phenomena. However, United States migration data have serious drawbacks: public data lack spatial granularity, and higher-resolution proprietary data suffer from multiple biases. To address this, we develop a method that fuses high-resolution proprietary data with coarse Census data to create MIGRATE: annual migration matrices capturing flows between 47.4 billion US Census Block Group pairs—approximately four thousand times the spatial resolution of current public data. Our estimates are highly correlated with external ground-truth datasets and improve accuracy relative to raw proprietary data. We use MIGRATE to analyze national and local migration patterns. Nationally, we document demographic and temporal variation in homophily, upward mobility, and moving distance—for example, rising moves into top-income-quartile block groups and racial disparities in upward mobility. Locally, MIGRATE reveals patterns such as wildfire-driven out-migration that are invisible in coarser previous data. We release MIGRATE as a resource for migration researchers.

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Data availability

MIGRATE is available upon request for non-profit research use at our website. To mitigate any privacy risks, interested researchers must agree to a data usage agreement pledging not to re-identify individuals in the data, and to adhere to privacy-protecting measures when storing data and presenting results. Manual review of their application should be completed within 10 business days, and will last for the duration of the proposed research project.

Code availability

Code to reproduce our research findings is available on GitHub: https://github.com/gsagostini/MIGRATE.

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Acknowledgements

Throughout this research, E.P. was supported by a Google Research Scholar award, an AI2050 Early Career Fellowship, NSF CAREER #2142419, a CIFAR Azrieli Global scholarship, a gift to the LinkedIn-Cornell Bowers CIS Strategic Partnership, the Survival and Flourishing Fund, Coefficient Giving, and the Zhang Family Endowed professorship. N.G. was supported by NSF CAREER IIS-2339427, and NASA, Cornell Tech Urban Tech Hub, Google, Meta, and Amazon research awards.

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Authors and Affiliations

  1. Cornell Tech, New York City, NY, USA

    Gabriel Agostini & Nikhil Garg

  2. University of Minnesota, Minneapolis, MN, USA

    Rachel Young

  3. University of California, Berkeley, Berkeley, CA, USA

    Rachel Young & Emma Pierson

  4. Princeton University, Princeton, NJ, USA

    Rachel Young

  5. Cornell University, Ithaca, NY, USA

    Maria Fitzpatrick

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  1. Gabriel Agostini
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Contributions

G.A. performed the experiments. All authors (G.A., R.Y., M.F., N.G., and E.P.) conceived and designed the experiments. M.F. provided the raw dataset. All authors contributed to the interpretation of the results, the analyses of the data, and the writing of the manuscript.

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Correspondence to Emma Pierson.

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Agostini, G., Young, R., Fitzpatrick, M. et al. Inferring fine-grained migration patterns across the United States. Nat Commun (2025). https://doi.org/10.1038/s41467-025-68019-2

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  • Received: 16 June 2025

  • Accepted: 16 December 2025

  • Published: 26 December 2025

  • DOI: https://doi.org/10.1038/s41467-025-68019-2

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