Unsupervised embedding of trajectories captures the latent structure of mobility

Human mobility and migration drive major societal phenomena such as the growth and evolution of cities, epidemics, economies, and innovation. Historically, human mobility has been strongly constrained by physical separation -- geographic distance. However, geographic distance is becoming less relevant in the increasingly-globalized world in which physical barriers are shrinking while linguistic, cultural, and historical relationships are becoming more important. As understanding mobility is becoming critical for contemporary society, finding frameworks that can capture this complexity is of paramount importance. Here, using three distinct human trajectory datasets, we demonstrate that a neural embedding model can encode nuanced relationships between locations into a vector-space, providing an effective measure of distance that reflects the multi-faceted structure of human mobility. Focusing on the case of scientific mobility, we show that embeddings of scientific organizations uncover cultural and linguistic relations, and even academic prestige, at multiple levels of granularity. Furthermore, the embedding vectors reveal universal relationships between organizational characteristics and their place in the global landscape of scientific mobility. The ability to learn scalable, dense, and meaningful representations of mobility directly from the data can open up a new avenue of studying mobility across domains.