Unsupervised embedding of trajectories captures the latent structure of scientific migration.

ABSTRACT: 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

SUBMITTER: Murray D 

PROVIDER: S-EPMC10756268 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature