# Deep learning four decades of human migration

arXiv: https://arxiv.org/abs/2506.22821 · HTML: https://mlsanity.com/paper/2506.22821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 132 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Continuously Differentiable Exponential Linear Units](https://mlsanity.com/paper/1704.07483/) (2017)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Future Directions in Human Mobility Science](https://mlsanity.com/paper/2408.00702/) (2024)
- [A Machine Learning Approach to Modeling Human Migration](https://mlsanity.com/paper/1711.05462/) (2017)
- [Neural parameter calibration for large-scale multi-agent models](https://mlsanity.com/paper/2209.13565/) (2022)
- [An Interdisciplinary Survey on Origin-destination Flows Modeling: Theory and Techniques](https://mlsanity.com/paper/2306.10048/) (2023)
- [Human mobility is well described by closed-form gravity-like models learned automatically from data](https://mlsanity.com/paper/2312.11281/) (2023)
- [Inferring networks from time series: a neural approach](https://mlsanity.com/paper/2303.18059/) (2023)
- [Forecasting asylum-related migration flows with machine learning and data at scale](https://mlsanity.com/paper/2011.04348/) (2020)
- [Modelling Global Trade with Optimal Transport](https://mlsanity.com/paper/2409.06554/) (2024)
- [Measuring Global Migration Flows using Online Data](https://mlsanity.com/paper/2504.11691/) (2025)

## Abstract (arXiv)

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.