Unified underpinning of human mobility in the real world and cyberspace

Human movements in the real world and in cyberspace affect not only dynamical processes such as epidemic spreading and information diffusion but also social and economical activities such as urban planning and personalized recommendation in online shopping. Despite recent efforts in characterizing and modeling human behaviors in both the real and cyber worlds, the fundamental dynamics underlying human mobility have not been well understood. We develop a minimal, memory-based random walk model in limited space for reproducing, with a single parameter, the key statistical behaviors characterizing human movements in both cases. The model is validated using relatively big data from mobile phone and online commerce, suggesting memory-based random walk dynamics as the unified underpinning for human mobility, regardless of whether it occurs in the real world or in cyberspace.

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

[2]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[3]  Vanessa Frías-Martínez,et al.  An Agent-Based Model of Epidemic Spread Using Human Mobility and Social Network Information , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[4]  J. Blumenstock,et al.  Divided We Call: Disparities in Access and Use of Mobile Phones in Rwanda , 2012 .

[5]  Marco Luca Sbodio,et al.  AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data , 2013, ECML/PKDD.

[6]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

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

[8]  Kazuko Yamasaki,et al.  Scaling and memory in volatility return intervals in financial markets. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Ahmed Eldawy,et al.  LARS: A Location-Aware Recommender System , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[10]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[11]  Kwang-Il Goh,et al.  Burstiness and memory in complex systems , 2006 .

[12]  Paul L. Krapivsky,et al.  Popularity-driven networking , 2011, ArXiv.

[13]  Vito Latora,et al.  Understanding mobility in a social petri dish , 2011, Scientific Reports.

[14]  G. Zipf.  The P 1 P 2 D Hypothesis: On the Intercity Movement of Persons , 1946 .

[15]  Jari Saramäki,et al.  Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences , 2013, Proceedings of the National Academy of Sciences.

[16]  Zhi-Dan Zhao,et al.  Scaling and correlation of human movements in cyberspace and physical space. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

[18]  Qiang Guo,et al.  Memory effect of the online user preference , 2014, Scientific Reports.

[19]  Antti Ylä-Jääski,et al.  Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator , 2015, PloS one.

[20]  Emilio Frazzoli,et al.  A review of urban computing for mobile phone traces: current methods, challenges and opportunities , 2013, UrbComp '13.

[21]  Dino Pedreschi,et al.  Returners and explorers dichotomy in human mobility , 2015, Nature Communications.

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

[23]  Marta C. González,et al.  Understanding individual human mobility patterns , 2008, Nature.

[24]  Jari Saramäki,et al.  Persistence of social signatures in human communication , 2012, Proceedings of the National Academy of Sciences.

[25]  C. Cattuto.  Semiotic dynamics in online social communities , 2006 .

[26]  Carlo Ratti,et al.  The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events , 2010, Pervasive.

[27]  Zengru Di,et al.  Toward a general understanding of the scaling laws in human and animal mobility , 2010, 1008.4394.

[28]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[29]  Zhi-Dan Zhao,et al.  Emergence of scaling in human-interest dynamics , 2013, Scientific Reports.

[30]  Zbigniew Smoreda,et al.  On the Use of Human Mobility Proxies for Modeling Epidemics , 2013, PLoS Comput. Biol..

[31]  Tao Zhou,et al.  Diversity of individual mobility patterns and emergence of aggregated scaling laws , 2012, Scientific Reports.

[32]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[33]  T. Geisel,et al.  Natural human mobility patterns and spatial spread of infectious diseases , 2011, 1103.6224.

[34]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[35]  Z. Smoreda,et al.  Gender-specific use of the domestic telephone , 2000 .

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

[37]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[38]  C. Cattuto,et al.  A Yule-Simon process with memory , 2006 .

[39]  Wen-Xu Wang,et al.  Universal predictability of mobility patterns in cities , 2013, Journal of The Royal Society Interface.

[40]  Mirco Musolesi,et al.  Disease Containment Strategies based on Mobility and Information Dissemination , 2015, Scientific Reports.