Deep learning four decades of human migration
type: NewsArticle
description: Researchers have developed a new global dataset of annual migration flows across 230 countries from 1990 to the present, using deep learning to integrate diverse data sources. The model, which outperforms existing five-year estimates, reveals previously obscured short-term migration dynamics driven by conflict, environmental crises and political change. The team released all data, code and trained models to provide a transparent foundation for future research and policy.
date published: 2026-06-11T21:40:20+00:00
date modified: 2026-06-11T21:49:15+00:00
language: en
is accessible for free: true
article body: ## Abstract Human migration is a fundamental driver of global demographic change, shaping population structure, labour markets and social policy across countries 1,2,3. Although long-term migration patterns are often linked to economic development , they can shift rapidly in response to shocks such as conflict, environmental crises and political change [4](/articles/s41586-026-10611-7#ref-CR4). Despite its importance, migration remains difficult to measure consistently: existing data are sparse, concentrated in high-income settings and are fragmented across incompatible definitions, temporal resolutions and data types [5](/articles/s41586-026-10611-7#ref-CR5). Past efforts have relied on partial datasets, including flow records, stock estimates and model-based reconstructions with limited coverage [6](#ref-CR6),[7](#ref-CR7),[8](/articles/s41586-026-10611-7#ref-CR8). A central challenge is therefore to construct a globally consistent, high-resolution account of migration flows over time. Here we present a new dataset of annual origin-destination migration across 230 countries and regions from 1990 to the present, integrating diverse data sources into a unified modelling framework. By combining official statistics, census-based stocks, net migration estimates and past flow reconstructions, our approach produces temporally detailed and spatially comprehensive estimates that substantially extend existing resources. Using an ensemble of deep recurrent neural networks informed by geographic, economic, cultural and political covariates, we capture both persistent trends and short-term responses to changing conditions—all while propagating uncertainty to generate confidence bounds. Our results outperform existing five-year flow estimates on held-out data and provide finer temporal resolution, revealing previously obscured dynamics in global migration patterns. This framework highlights regions in which uncertainty remains high and data collection is most urgently needed. B…
publisher: Web Pulse
keywords: machine-learning, neural-networks, artificial-intelligence
citation: CreativeWork