Deep learning four decades of human migration

TL;DR

This paper introduces a deep learning approach to estimate and analyze global human migration flows over 35 years, providing detailed, disaggregated data with uncertainty bounds, and outperforming traditional methods.

Contribution

The authors develop a recurrent neural network model that captures long-range temporal migration patterns and provides open access to comprehensive migration data and estimates.

Findings

Outperforms traditional migration estimation methods.

Provides high-resolution, long-term migration flow estimates.

Includes uncertainty bounds for all estimates.

Abstract

We present a novel and detailed dataset on origin-destination annual migration flows and stocks between 230 countries and regions, spanning the period from 1990 to the present. Our flow estimates are further disaggregated by country of birth, providing a comprehensive picture of migration over the last 35 years. The estimates are obtained by training a deep recurrent neural network to learn flow patterns from 18 covariates for all countries, including geographic, economic, cultural, societal, and political information. The recurrent architecture of the neural network means that the entire past can influence current migration patterns, allowing us to learn long-range temporal correlations. By training an ensemble of neural networks and additionally pushing uncertainty on the covariates through the trained network, we obtain confidence bounds for all our estimates, allowing researchers to pinpoint the geographic regions most in need of additional data collection. We validate our approach on various test sets of unseen data, demonstrating that it significantly outperforms traditional methods estimating five-year flows while delivering a significant increase in temporal resolution. The model is fully open source: all training data, neural network weights, and training code are made public alongside the migration estimates, providing a valuable resource for future studies of human migration.