Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. 1-3 A concurrent literature has emphasized the explorative nature of human behavior, showing that the number of visited places grows steadily over time. [4][5][6][7] How to reconcile these seemingly contradicting facts remains an open question. Here, we analyze high-resolution multi-year traces of ∼40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ∼25 locations. We use this finding to improve state-of-theart modeling of human mobility. 4, 8 Furthermore, shifting the attention from aggregated quantities to individual behavior, we show that the size of an individual's set of preferred locations correlates with the number of her social interactions. This result suggests a connection between the conserved quantity we identify, which as we show can not be understood purely on the basis of time constraints, and the 'Dunbar number' 9, 10 describing a cognitive upper limit to an individual's number of social relations. We anticipate that our work will spark further research linking the study of Human Mobility and the Cognitive and Behavioral Sciences.There is a disagreement between the current scientific understanding of human mobility as highly predictable and stable over time, 1,4,5 and the fact that individual lives are constantly evolving due to changing needs and circumstances. 11 The role of cultural, social and legal constraints on the spacetime fixity of daily activities has long been recognized. 2, 12, 13 Recent studies based on the analysis of human digital traces including mobile phone records, 14, 15 online location-based social networks, 16-20 and Global Positioning System (GPS) location data of vehicles 21-26 have shown that individuals universally exhibit a markedly regular pattern characterized by few locations, or points of interest, 27, 28 where 1 arXiv:1609.03526v3 [physics.soc-ph] 19 Jun 2018 they return regularly 6, 29 and predictably. 4 However, the observed regularity mainly concerns human activities taking place at the daily 28,30,31 or weekly 14, 15, 17 time-scales, such as commuting between home and office, 14, 15,32,33 pursuing habitual leisure activities, and socializing with established friends and acquaintances. 16 Thus, while the role played by slowly occurring changes on the evolution of individuals' social relationships has been widely investigated, 34-41 their effects on human mobility behavior are not well understood and not included in most available models. 4,8,[42][43][44][45][46][47] Here, we investigate individuals' routines across months and years. We reveal how individuals balance the trade-off between the exploitation of familiar places and the exploration of new opportunities...

Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

There is a contradiction at the heart of our current understanding of individual and collective mobility patterns. On one hand, a highly influential stream of literature on human mobility driven by analyses of massive empirical datasets finds that human movements show no evidence of characteristic spatial scales. There, human mobility is described as scale-free. 1-3 On the other hand, in geography, the concept of scale, referring to meaningful levels of description from individual buildings through neighborhoods, cities, regions, and countries, is central for the description of various aspects of human behavior such as socio-economic interactions, or political and cultural dynamics. 4, 5 Here, we resolve this apparent paradox by showing that day-to-day human mobility does indeed contain meaningful scales, corresponding to spatial containers restricting mobility behavior. The scale-free results arise from aggregating displacements across containers. We present a simple model, which given a person's 1 arXiv:2109.07381v1 [physics.soc-ph] 15 Sep 2021trajectory, infers their neighborhoods, cities, and so on, as well as the sizes of these geographical containers. We find that the containers characterizing the trajectories of more than 700 000 individuals do indeed have typical sizes. We show that our model generates highly realistic trajectories without overfitting and provides a new lens through which to understand the differences in mobility behaviour across countries,