Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations1,2,3. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time4,5,6,7. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse 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. We use this finding to improve state-of-the-art modelling of human mobility4,8. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot 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 behavioural sciences.
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Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. Limits of predictability in human mobility. Science 327, 1018–1021 (2010).
Schwanen, T., Kwan, M.-P. & Ren, F. How fixed is fixed? Gendered rigidity of space–time constraints and geographies of everyday activities. Geoforum 39, 2109–2121 (2008).
Golledge, R. G. Spatial Behavior: A Geographic Perspective (Guilford Press, London & New York, NY, 1997).
Song, C., Koren, T., Wang, P. & Barabási, A.-L. Modelling the scaling properties of human mobility. Nat. Phys. 6, 818–823 (2010).
Gonzalez, M. C., Hidalgo, C. A. & Barabasi, A.-L. Understanding individual human mobility patterns. Nature 453, 779–782 (2008).
Pappalardo, L. et al. Returners and explorers dichotomy in human mobility. Nat. Commun. 6, 8166 (2015).
Alessandretti, L., Sapiezynski, P., Lehmann, S. & Baronchelli, A. Multi-scale spatio-temporal analysis of human mobility. PLoS ONE 12, e0171686 (2017).
Jiang, S. et al. The TimeGeo modeling framework for urban motility without travel surveys.Proc. Natl Acad. Sci. USA 113, E5370–E5378 (2016).
Dunbar, R. I. Coevolution of neocortical size, group size and language in humans. Behav. Brain Sci. 16, 681–694 (1993).
Gonçalves, B., Perra, N. & Vespignani, A. Modeling users’ activity on twitter networks: validation of Dunbar’s number. PLoS ONE 6, e22656 (2011).
Sarason, I. G., Johnson, J. H. & Siegel, J. M.Assessing the impact of life changes: development of the life experiences survey. J. Consult. Clin. Psychol. 46, 932–946 (1978).
Hägerstraand, T. What about people in regional science? Pap. Reg. Sci. 24, 7–24 (1970).
Burns, L. D. Transportation, Temporal, and Spatial Components of Accessibility (Lexington Books, Lexington, 1980).
Csáji, B. C. et al. Exploring the mobility of mobile phone users. Phys. A Stat. Mech. Appl. 392, 1459–1473 (2013).
Sevtsuk, A. & Ratti, C. Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. J. Urban Technol. 17, 41–60 (2010).
Cho, E., Myers, S. A. & Leskovec, J. Friendship and mobility: user movement in location-based social networks. In Proc. 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1082–1090 (ACM, 2011).
Cheng, Z., Caverlee, J., Lee, K. & Sui, D. Z. Exploring millions of footprints in location sharing services. In Proc. 5th International AAAI Conference on Weblogs and Social Media 81–88 (AAAI, 2011).
Brown, C., Lathia, N., Mascolo, C., Noulas, A. & Blondel, V. Group colocation behavior in technological social networks. PLoS ONE 9, e105816 (2014).
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C. A tale of many cities: universal patterns in human urban mobility. PLoS ONE 7, e37027 (2012).
Bapierre, H., Jesdabodi, C. & Groh, G. Mobile homophily and social location prediction. Preprint at https://arxiv.org/abs/1506.07763 (2015).
Giannotti, F. et al. Unveiling the complexity of human mobility by querying and mining massive trajectory data. VLDB J. 20, 695–719 (2011).
Scellato, S., Musolesi, M., Mascolo, C., Latora, V. & Campbell, A. T. in Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science Vol. 6696 (eds Lyons K., Hightower J. & Huang E. M.) 152–169 (Springer, Berlin & Heidelberg, 2011).
Liang, X., Zheng, X., Lv, W., Zhu, T. & Xu, K. The scaling of human mobility by taxis is exponential. Phys. A Stat. Mech. Appl. 391, 2135–2144 (2012).
Gallotti, R., Bazzani, A. & Rambaldi, S.Towards a statistical physics of human mobility.Int. J. Modern Phys. C 23, 1250061 (2012).
Bazzani, A., Giorgini, B., Rambaldi, S., Gallotti, R. & Giovannini, L. Statistical laws in urban mobility from microscopic GPS data in the area of Florence. J. Stat. Mech. Theory Exp. 2010, P05001 (2010).
Jiang, B., Yin, J. & Zhao, S. Characterizing the human mobility pattern in a large street network. Phys. Rev. E 80, 021136 (2009).
Mülligann, C., Janowicz, K., Ye, M. & Lee, W.-C. Analyzing the spatial–semantic interaction of points of interest in volunteered geographic information. In Proc. International Conference on Spatial Information Theory 350–370 (Springer, 2011).
Phithakkitnukoon, S., Horanont, T., Di Lorenzo, G., Shibasaki, R. & Ratti, C. Activity-aware map: identifying human daily activity pattern using mobile phone data. In Int. Workshop on Human Behavior Understanding 14–25 (Springer, 2010).
Isaacman, S. et al. Identifying important places in people’s lives from cellular network data. In Pervasive Computing. Pervasive 2011. Lecture Notes in Computer Science Vol. 6696 (eds Lyons K., Hightower J. & Huang E. M.) 133–151 (Springer, Berlin & Heidelberg, 2011).
Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z. & González, M. C. Unravelling daily human mobility motifs. J. R. Soc. Interface 10, 20130246 (2013).
Bagrow, J. P. & Lin, Y.-R. Mesoscopic structure and social aspects of human mobility. PLoS ONE 7, e37676 (2012).
Ranjan, G., Zang, H., Zhang, Z.-L. & Bolot, J. Are call detail records biased for sampling human mobility? ACM SIGMOBILE Mob. Comput. Commun. Rev. 16, 33–44 (2012).
Zang, H. & Bolot, J. Anonymization of location data does not work: a large-scale measurement study. In Proc. 17th Annual International Conference on Mobile Computing and Networking 145–156 (ACM, 2011).
Kossinets, G. & Watts, D. J. Empirical analysis of an evolving social network. Science 311, 88–90 (2006).
Kossinets, G. & Watts, D. J. Origins of homophily in an evolving social network 1. Am. J. Sociol. 115, 405–450 (2009).
Romero, D. M., Meeder, B., Barash, V. & Kleinberg, J. Maintaining ties on social media sites: the competing effects of balance, exchange, and betweenness. In Proc. 5th International AAAI Conference on Weblogs and Social Media (AAAI, 2011).
Martin, J. L. & Yeung, K.-T. Persistence of close personal ties over a 12-year period. Soc. Networks 28, 331–362 (2006).
Miritello, G., Lara, R., Cebrian, M. & Moro, E.Limited communication capacity unveils strategies for human interaction.Sci. Rep. 3, 1950 (2013).
Saramäki, J. et al. Persistence of social signatures in human communication. Proc. Natl Acad. Sci. USA 111, 942–947 (2014).
Burt, R. S. Decay functions. Soc. Networks 22, 1–28 (2000).
Arnaboldi, V., Conti, M., Passarella, A. & Dunbar, R. Dynamics of personal social relationships in online social networks: a study on twitter. In Proc. 1st ACM Conference on Online Social Networks 15–26 (ACM, 2013).
Isaacman, S. et al. Human mobility modeling at metropolitan scales. In Proc. 10th International Conference on Mobile Systems, Applications, and Services 239–252 (ACM, 2012).
Lee, K., Hong, S., Kim, S. J., Rhee, I. & Chong, S. SLAW: a new mobility model for human walks. In Proc. IEEE INFOCOM 2009 855–863 (IEEE, 2009).
Kim, M., Kotz, D. & Kim, S. Extracting a mobility model from real user traces. In Proc. 25th IEEE International Conference on Computer Communications 1–13 (IEEE, 2006).
Jia, T., Jiang, B., Carling, K., Bolin, M. & Ban, Y. An empirical study on human mobility and its agent-based modeling. J. Stat. Mech. Theory Exp. 2012, P11024 (2012).
Han, X.-P., Hao, Q., Wang, B.-H. & Zhou, T. Origin of the scaling law in human mobility: hierarchy of traffic systems. Phys. Rev. E 83, 036117 (2011).
Pappalardo, L., Rinzivillo, S. & Simini, F.Human mobility modelling: exploration and preferential return meet the gravity model. Procedia Comput. Sci. 83, 934–939 (2016).
Stopczynski, A. et al. Measuring large-scale social networks with high resolution. PLoS ONE 9, e95978 (2014).
Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D. & Laurila, J. Towards rich mobile phone datasets: Lausanne Data Collection Campaign. In Proc. ACM International Conference on Pervasive Services (ICPS, 2010).
Laurila, J. K. et al. The mobile data challenge: big data for mobile computing research. In Proc. Workshop on the Nokia Mobile Data Challenge, in Conjunction with the 10th International Conference on Pervasive Computing EPFL-CONF-192489 (2012).
Eagle, N. & Pentland, A. S. Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10, 255–268 (2006).
Eagle, N., Pentland, A. S. & Lazer, D. Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. USA 106, 15274–15278 (2009).
Çolak, S., Alexander, L. P., Alvim, B. G., Mehndiretta, S. R. & González, M. C. Analyzing cell phone location data for urban travel: current methods, limitations and opportunities. In Proc. Transportation Research Board 94th Annual Meeting 15-5279 (2015).
Lenormand, M. et al. Influence of sociodemographic characteristics on human mobility. Preprint at https://arxiv.org/abs/1411.7895 (2014).
Heaps, H. S. Information Retrieval: Computational and Theoretical Aspects (Academic Press, Orlando, 1978).
Horton, F. E. & Reynolds, D. R. Effects of urban spatial structure on individual behavior. Econ. Geogr. 47, 36–48 (1971).
Mazey, M. E. The effect of a physio-political barrier upon urban activity space.Ohio J. Sci. 81, 212–217 2981).
Yuan, Y. & Raubal, M. Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study. Int. J. Geogr. Inf. Sci. 30, 1594–1621 (2016).
Sherman, J. E., Spencer, J., Preisser, J. S., Gesler, W. M. & Arcury, T. A. A suite of methods for representing activity space in a healthcare accessibility study. Int. J. Health Geogr. 4, 24 (2005).
Zhou, C., Bhatnagar, N., Shekhar, S. & Terveen, L. Mining personally important places from GPS tracks. In Proc. 2007 IEEE 23rd International Conference on Data Engineering Workshop 517–526 (IEEE, 2007).
Barbosa, H., de Lima-Neto, F. B., Evsukoff, A. & Menezes, R. The effect of recency to human mobility. EPJ Data Sci. 4, 21 (2015).
Szell, M., Sinatra, R., Petri, G., Thurner, S. & Latora, V.Understanding mobility in a social petri dish.Sci. Rep. 2, 457 (2012).
Axhausen, K. W. Activity spaces, biographies, social networks and their welfare gains and externalities: some hypotheses and empirical results. Mobilities 2, 15–36 (2007).
Costa, P. T. & McCrae, R. R. Four ways five factors are basic. Pers. Individ. Diff. 13, 653–665 (1992).
Kalish, Y. & Robins, G. Psychological predispositions and network structure: the relationship between individual predispositions, structural holes and network closure. Soc. Networks 28, 56–84 (2006).
Pollet, T. V., Roberts, S. G. & Dunbar, R. I.Extraverts have larger social network layers: but do not feel emotionally closer to individuals at any layer.J. Individ. Diff. 32, 161–169 (2011).
Eagle, N. The Reality Mining Data (Massachusetts Institute of Technology, 2010).
Sapiezynski, P., Gatej, R., Mislove, A. & Lehmann, S. Opportunities and challenges in crowdsourced wardriving. In Proc. 2015 Internet Measurement Conference 267–273 (ACM, 2015).
John, O. P. & Srivastava, S. in Handbook of Personality: Theory and Research 2nd edn (eds Pervin, L. & John, O. P.) 102–138 (Guilford, New York, NY, 1999).
Cuttone, A., Lehmann, S. & Larsen, J. E. Inferring human mobility from sparse low accuracy mobile sensing data. In Proc. 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication 995–1004 (ACM, 2014).
Laurila, J. K. et al. From big smartphone data to worldwide research: the Mobile Data Challenge. Pervasive Mob. Comput. 9, 752–771 (2013).
This work was partially supported by the Villum Foundation (High Resolution Networks project, for which S.L. is the principal investigator), a UCPH-2016 grant (Social Fabric project, for which S.L. is a co-principal investigator) and the Danish Council for Independent Research (Microdynamics of Influence in Social Systems project, for which S.L. is the principal investigator; grant ID 4184-00556). Portions of the research in this paper used the MDC Database made available by the Idiap Research Institute, Switzerland and owned by Nokia. V.S. was supported by Sony Mobile Communications. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. V.S. thanks H. Jonsson for invaluable technical assistance.
The authors declare no competing interests.
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Alessandretti, L., Sapiezynski, P., Sekara, V. et al. Evidence for a conserved quantity in human mobility. Nat Hum Behav 2, 485–491 (2018). https://doi.org/10.1038/s41562-018-0364-x
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