MIGRATE

Overview

MIGRATE (Migration Inference for GRAnular Trend Estimation) provides yearly CBG-to-CBG migration flows in the United States that are:

• thousands of times more granular than existing public migration datasets.
• highly correlated with external Census datasets.
• less biased, and more consistent with Census data, than proprietary address data.

We build MIGRATE using an iterative proportional fitting algorithm that reconciles fine-grained yet noisy proprietary address data with coarse yet reliable Census data.

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.