Glossary
Concise definitions of the technical terms used throughout the Migration Scenario Engine. 21 terms grouped by category.
Indices & outcomes
- MPI
- Migration Pressure Index — composite index (0–1) from 5 stress components (climate, conflict, disasters, governance, economy). Higher = more migration pressure. Normalised by percentile rank within each SSP group.
- TPI
- Trapped Population Index — combines migration pressure (MPI) with limited mobility. Flagged at P95 within SSP. High TPI = high stress AND low mobility.
- Net Migration
- Immigration minus emigration. 5-year cumulative. Positive = net inflow.
- Flow Rate
- Migration rate per 1,000 origin population per 5-year period.
- Corridors
- Bilateral origin–destination pairs (e.g. MEX → USA). The panel covers ~52,670 directed corridors.
- Diaspora Stock
- Cumulative stock of migrants from origin in destination country. The strongest single predictor in the historical panel (r ≈ 0.33).
Climate scenarios
- SSP
- Shared Socioeconomic Pathways — IPCC scenarios: SSP1 (Sustainability), SSP2 (Middle of the Road), SSP3 (Rivalry), SSP5 (Fossil-fueled).
- Scenario
- Narrative future scenario crossed with each SSP: Baseline (ML only), Baseline+, Adaptation Success, Fragmentation, Climate Extreme.
- CMIP6
- Coupled Model Intercomparison Project Phase 6 — climate model ensemble for projections through 2100. The MSE uses a 5-GCM ensemble (ACCESS-CM2, GFDL-ESM4, IPSL-CM6A-LR, MIROC6, MPI-ESM1-2-LR).
- Temperature Anomaly
- Deviation of mean temperature from the WMO 1961–1990 reference, in °C.
- Precipitation Anomaly
- Deviation of precipitation from the WMO 1961–1990 reference, in mm/year.
- ND-GAIN
- Notre Dame Global Adaptation Initiative — climate adaptation score (0–100) per country.
Governance & structural variables
- WGI
- Worldwide Governance Indicators — World Bank index. The MSE uses Government Effectiveness (GE) and Rule of Law (RL). Scale: −2.5 to +2.5.
- Gravity Model
- Migration flows as a function of mass (population) and distance — analogous to physical gravity. The MSE uses CEPII GeoDist for the static gravity backbone.
Modelling & statistics
- Stacking Ensemble
- Combination of three models — GAM (weight 31.7%, OOF R² = 0.795), Random Forest (33.3%, R² = 0.804) and XGBoost (35.0%, R² = 0.813) — via a Ridge meta-learner on out-of-fold predictions. Pooled OOF R² = 0.826. Reaches 99.9% of the temporal autocorrelation ceiling.
- OOF
- Out-of-Fold — predictions on data not used during training (cross-validation). Avoids overfitting bias. The MSE uses 5-fold expanding-window OOF predictions.
- IPF
- Iterative Proportional Fitting — calibration method that aligns model predictions to known marginal totals (UN WPP origin and destination totals).
- CPI
- Conformal Prediction Intervals — distribution-free uncertainty intervals (50% and 90% coverage) based on out-of-fold residuals. Mondrian-binned by flow magnitude; multiplicative bootstrap with N = 500 replicates.
- SHAP
- SHapley Additive exPlanations — explainability method that quantifies each feature's contribution to the prediction. The MSE publishes SHAP attributions for both Random Forest and XGBoost components.