Inferring fine-grained migration patterns across the United States

• URL: http://arxiv.org/abs/2503.20989v1
• Date: Wed, 26 Mar 2025 21:07:44 GMT
• Title: Inferring fine-grained migration patterns across the United States
• Authors: Gabriel Agostini, Rachel Young, Maria Fitzpatrick, Nikhil Garg, Emma Pierson,
• Abstract summary: We develop a scalable iterative-proportional-fitting based method which reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data.<n>We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010 - 2019 which captures flows between 47.4 billion pairs of Census Block Groups.<n>These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data.
• Score: 1.6594124470436404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-grained migration data illuminate important demographic, environmental, and health phenomena. However, migration datasets within the United States remain lacking: publicly available Census data are neither spatially nor temporally granular, and proprietary data have higher resolution but demographic and other biases. To address these limitations, we develop a scalable iterative-proportional-fitting based method which reconciles high-resolution but biased proprietary data with low-resolution but more reliable Census data. We apply this method to produce MIGRATE, a dataset of annual migration matrices from 2010 - 2019 which captures flows between 47.4 billion pairs of Census Block Groups -- about four thousand times more granular than publicly available data. These estimates are highly correlated with external ground-truth datasets, and improve accuracy and reduce bias relative to raw proprietary data. We publicly release MIGRATE estimates and provide a case study illustrating how they reveal granular patterns of migration in response to California wildfires.

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