Mesoscopic Structure and Social Aspects of Human Mobility

The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility – the sets and sequences of visited locations – have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary “habitats” capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.

[1]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[2]  Jukka-Pekka Onnela,et al.  Geographic Constraints on Social Network Groups , 2010, PloS one.

[3]  Ryuichi Kitamura,et al.  Micro-simulation of daily activity-travel patterns for travel demand forecasting , 2000 .

[4]  Hunter N. B. Moseley,et al.  Limits of Predictability in Human Mobility , 2010 .

[5]  S. Stouffer Intervening opportunities: a theory relating mobility and distance , 1940 .

[6]  D. Brockmann,et al.  The Structure of Borders in a Small World , 2010, PLoS ONE.

[7]  Alessandro Vespignani,et al.  The role of the airline transportation network in the prediction and predictability of global epidemics , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Dan Cosley,et al.  Inferring social ties from geographic coincidences , 2010, Proceedings of the National Academy of Sciences.

[9]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[10]  Albert-László Barabási,et al.  Collective Response of Human Populations to Large-Scale Emergencies , 2011, PloS one.

[11]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[12]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[13]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[14]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[15]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[16]  Jon M. Kleinberg,et al.  Center of Attention: How Facebook Users Allocate Attention across Friends , 2011, ICWSM.

[17]  Zbigniew Smoreda,et al.  Interplay between Telecommunications and Face-to-Face Interactions: A Study Using Mobile Phone Data , 2011, PloS one.

[18]  Renaud Lambiotte,et al.  Uncovering space-independent communities in spatial networks , 2010, Proceedings of the National Academy of Sciences.

[19]  Peter Nijkamp,et al.  Price Elasticities of Demand for Passenger Air Travel , 2001 .

[20]  Robin I. M. Dunbar Neocortex size as a constraint on group size in primates , 1992 .

[21]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[22]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[23]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[24]  S. Strogatz,et al.  Redrawing the Map of Great Britain from a Network of Human Interactions , 2010, PloS one.

[25]  M. Kendall Rank Correlation Methods , 1949 .

[26]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[27]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[29]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[30]  James P. Bagrow,et al.  Investigating Bimodal Clustering in Human Mobility , 2009, 2009 International Conference on Computational Science and Engineering.

[31]  M. Kendall,et al.  Rank Correlation Methods , 1949 .

[32]  Juyong Park,et al.  The eigenmode analysis of human motion , 2010, 1603.04810.

[33]  Pan Hui,et al.  Pocket switched networks and human mobility in conference environments , 2005, WDTN '05.

[34]  Morton E. O'Kelly,et al.  EMBEDDING ECONOMIES OF SCALE CONCEPTS FOR HUB NETWORK DESIGN. , 2001 .