A Machine Learning Approach to Modeling Human Migration

TL;DR

This paper introduces machine learning models that improve the prediction of human migration flows by incorporating multiple features, surpassing traditional models in accuracy for both US and international migrations, and enabling scenario analysis.

Contribution

The paper presents a novel machine learning approach that captures complex migration dynamics and incorporates various exogenous features, outperforming traditional models.

Findings

ML models outperform traditional models on evaluation metrics

Models effectively predict US county and international migrations

Flexible framework for scenario-based migration predictions

Abstract

Human migration is a type of human mobility, where a trip involves a person moving with the intention of changing their home location. Predicting human migration as accurately as possible is important in city planning applications, international trade, spread of infectious diseases, conservation planning, and public policy development. Traditional human mobility models, such as gravity models or the more recent radiation model, predict human mobility flows based on population and distance features only. These models have been validated on commuting flows, a different type of human mobility, and are mainly used in modeling scenarios where large amounts of prior ground truth mobility data are not available. One downside of these models is that they have a fixed form and are therefore not able to capture more complicated migration dynamics. We propose machine learning models that are able to incorporate any number of exogenous features, to predict origin/destination human migration flows. Our machine learning models outperform traditional human mobility models on a variety of evaluation metrics, both in the task of predicting migrations between US counties as well as international migrations. In general, predictive machine learning models of human migration will provide a flexible base with which to model human migration under different what-if conditions, such as potential sea level rise or population growth scenarios.