用深度学习重建1990年以来全球35年迁徙图谱，精度超传统方法。
Deep learning four decades of human migration
- 用18个变量训练循环神经网络，捕捉长期时间关联。
- 比传统方法更准，五年人口流动预测误差显著降低。
- 数据与代码全开源，适合人口、政策研究者使用。
我们构建了一个新颖且详尽的全球人口迁移数据集，涵盖1990年至当前时期230个国家和地区间的年度迁徙流量与存量，按出生国细分，全面呈现过去35年的迁徙格局。通过训练深度循环神经网络，从地理、经济、文化、社会及政治等18项协变量中学习迁徙模式，其递归结构使历史信息可影响当前迁徙趋势，从而捕捉长期时间相关性。通过集成多个神经网络，并将协变量不确定性传播至模型输出，获得所有估计值的置信区间，帮助研究者识别数据最匮乏的地区。在多组未见数据测试中，该方法显著优于传统五年人口流动估算方法，且实现了更高的时间分辨率。模型完全开源：训练数据、神经网络权重与训练代码均公开，为未来人类迁徙研究提供宝贵资源。
原文摘要 · Abstract (English)
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.
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