No abstract

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.

Systematized subject classification is essential for funding and assessing scientific projects. Conventionally, classification schemes are founded on the empirical knowledge of the group of experts; thus, the experts' perspectives have influenced the current systems of scientific classification. Those systems archived the current state-of-art in practice, yet the global effect of the accelerating scientific change over time has made the updating of the classifications system on a timely basis vertually impossible. To overcome the aforementioned limitations, we propose an unbiased classification scheme that takes dvantage of collective knowledge; Wikipedia, an Internet encyclopedia edited by millions of users, sets a prompt classification in a collective fashion. We construct a Wikipedia network for scientific disciplines and extract the backbone of the network. This structure displays a landscape of science and technology that is based on a collective intelligence and that is more unbiased and adaptable than conventional classifications.

No abstract

Understanding human urban dynamics is essential but challenging as cities are complex systems where people and space interact. Using a customer-level data set from a leading Korean accommodation platform, we identify that urban hierarchy, geographical distance, and attachment to a location are crucial factors of social gathering behaviors in urban areas. We also introduce a model that incorporates the factors and reconstructs the key characteristics of the data. Our model and analysis show that COVID-19 leads to significant behavioral changes in social gathering behaviors. After the outbreak, people are more likely to visit familiar places, avoid places at the highest level of the urban *