  Human migration and mobility drives major societal phenomena including
epidemics, economies, innovation, and the diffusion of ideas. Although human
mobility and migration have been heavily constrained by geographic distance
throughout the history, advances and globalization are making other factors
such as language and culture increasingly more important. Advances in neural
embedding models, originally designed for natural language, provide an
opportunity to tame this complexity and open new avenues for the study of
migration. Here, we demonstrate the ability of the model word2vec to encode
nuanced relationships between discrete locations from migration trajectories,
producing an accurate, dense, continuous, and meaningful vector-space
representation. The resulting representation provides a functional distance
between locations, as well as a digital double that can be distributed,
re-used, and itself interrogated to understand the many dimensions of
migration. We show that the unique power of word2vec to encode migration
patterns stems from its mathematical equivalence with the gravity model of
mobility. Focusing on the case of scientific migration, we apply word2vec to a
database of three million migration trajectories of scientists derived from the
affiliations listed on their publication records. Using techniques that
leverage its semantic structure, we demonstrate that embeddings can learn the
rich structure that underpins scientific migration, such as cultural,
linguistic, and prestige relationships at multiple levels of granularity. Our
results provide a theoretical foundation and methodological framework for using
neural embeddings to represent and understand migration both within and beyond
science.
