Unsupervised embedding of trajectories captures the latent structure of mobility

Human mobility drives major societal phenomena including epidemics, economies, and innovation. Historically, mobility was constrained by geographic distance, however, in the globalizing world, language, culture, and history are increasingly important. We propose using the neural embedding model word2vec for studying mobility and capturing its complexity. Word2ec is shown to be mathematically equivalent to the gravity model of mobility, and using three human trajectory datasets, we demonstrate that it encodes nuanced relationships between locations into a vector-space, providing a measure of effective distance that outperforms baselines. Focusing on the case of scientific mobility, we show that embeddings uncover cultural, linguistic, and hierarchical relationships at multiple levels of granularity. Connecting neural embeddings to the gravity model opens up new avenues for the study of mobility.