README.md

metadata

license: gpl-3.0
language:
  - en
pretty_name: 'Deep learning four decades of human migration: datasets'
tags:
  - arXiv:2506.22821

Deep learning four decades of human migration: datasets

This repository contains all migration flow estimates associated with the paper "Deep learning four decades of human migration." Evaluation code, training data, trained neural networks, and smaller flow datasets are available in the main GitHub repository, which also provides detailed instructions on data sourcing. Due to file size limits, the larger datasets are archived here. The repository contains three folders:

Estimates

This folder contains all the migration estimates. Data is available in both NetCDF (.nc) and CSV (.csv) formats. The NetCDF format is more compact and pre-indexed, making it suitable for large files. In Python, datasets can be opened as xarray.Dataset objects, enabling coordinate-based data selection.

Each dataset uses the following coordinate conventions:

• Year: 1990–2023
• Birth ISO: Country of birth (UN ISO3)
• Origin ISO: Country of origin (UN ISO3)
• Destination ISO: Destination country (UN ISO3)
• Country ISO: Used for net migration data (UN ISO3)

The following data files are provided:

• T.nc: Full table of flows disaggregated by country of birth. Dimensions: Year, Birth ISO, Origin ISO, Destination ISO
• flows.nc: Total origin-destination flows (equivalent to T summed over Birth ISO). Dimensions: Year, Origin ISO, Destination ISO
• net_migration.nc: Net migration data by country. Dimensions: Year, Country ISO
• stocks.nc: Stock estimates for each country pair. Dimensions: Year, Origin ISO (corresponding to Birth ISO), Destination ISO
• test_flows.nc: Flow estimates on a randomly selected set of test edges, used for model validation

Additionally, two CSV files are provided for convenience:

• mig_unilateral.csv: Unilateral migration estimates per country, comprising:
  • imm: Total immigration flows
  • emi: Total emigration flows
  • net: Net migration
  • imm_pop: Total immigrant population (non-native-born)
  • emi_pop: Total emigrant population (living abroad)
• mig_bilateral.csv: Bilateral flow data, comprising:
  • mig_prev: Total origin-destination flows
  • mig_brth: Total birth-destination flows, where Origin ISO reflects place of birth

Each dataset includes a mean variable (mean estimate) and a std variable (standard deviation of the estimate).

An ISO3 conversion table is also provided.

Data

The Data contains all the data used to train, evaluate, and test the neural network. It is stored thematically in different folders, and most folders again contains its own README file to further explain the specific sources and imputation methods. All data is given both as a .csv file and a .nc file, and follows the ISO3-naming convention outlined in the main README.

Training_data

This folder contains all the tensors used to train the neural network. All data is given as a PyTorch tensor (.pt) and can be loaded using torch.load(). The folder contains targets, weights, masks, input covariates (scaled and unscaled), and the edge indices of each input. See the folder README for further details.

Net migration (Net_migration)

This folder contains net migration data, sourced from national statistical offices, together with a list of sources and the UN WPP net migration figures.

GDP indicators (GDP_data)

This folder contains data on GDP/capita, GDP growth, nominal GDP, and other GDP-related indicators for all countries and years included in the training period.

Gravity covariates (Gravity_datasets)

Demographic indicators (UN_WPP_data)

Migrant stocks (UN_stock_data)

Refugee figures (UNHCR_data)

Total number of refugees, asylum-seekers, and other people in need of international protection, taken from the UNHCR dataset.

Conflict deaths (UCDP_data)

This folder contains data on deaths in conflict provided by UCDP Georeferenced Event dataset. NaN values are filled with 0.

Bilateral flows (Flow_data)

Trained networks

Contains the ensemble of trained neural networks