Influence of sociodemographics on human mobility

Human mobility has been traditionally studied using surveys that deliver snapshots of population displacement patterns. The growing accessibility to ICT information from portable digital media has recently opened the possibility of exploring human behavior at high spatio-temporal resolutions. Mobile phone records, geolocated tweets, check-ins from Foursquare or geotagged photos, have contributed to this purpose at different scales, from cities to countries, in different world areas. Many previous works lacked, however, details on the individuals’ attributes such as age or gender. In this work, we analyze credit-card records from Barcelona and Madrid and by examining the geolocated credit-card transactions of individuals living in the two provinces, we find that the mobility patterns vary according to gender, age and occupation. Differences in distance traveled and travel purpose are observed between younger and older people, but, curiously, either between males and females of similar age. While mobility displays some generic features, here we show that sociodemographic characteristics play a relevant role and must be taken into account for mobility and epidemiological modelization.

[1]  S. Riley Large-Scale Spatial-Transmission Models of Infectious Disease , 2007, Science.

[2]  Vittoria Colizza,et al.  Heterogeneous length of stay of hosts’ movements and spatial epidemic spread , 2012, Scientific Reports.

[3]  Fred Brauer,et al.  Epidemic Models with Heterogeneous Mixing and Treatment , 2008, Bulletin of mathematical biology.

[4]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[5]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[6]  Alessandro Vespignani,et al.  Multiscale mobility networks and the spatial spreading of infectious diseases , 2009, Proceedings of the National Academy of Sciences.

[7]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[8]  Víctor Soto,et al.  Characterizing Urban Landscapes Using Geolocated Tweets , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[9]  T. Caliński,et al.  A dendrite method for cluster analysis , 1974 .

[10]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[11]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

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

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

[14]  P. Olivier,et al.  Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data , 2012, PloS one.

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

[16]  Hiroshi Nishiura,et al.  Travel and age of influenza A (H1N1) 2009 virus infection. , 2010, Journal of travel medicine.

[17]  Alessandro Vespignani,et al.  Modeling human mobility responses to the large-scale spreading of infectious diseases , 2011, Scientific reports.

[18]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[19]  L. A. Rvachev,et al.  A mathematical model for the global spread of influenza , 1985 .

[20]  Chenghu Zhou,et al.  A new insight into land use classification based on aggregated mobile phone data , 2013, Int. J. Geogr. Inf. Sci..

[21]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Ciro Cattuto,et al.  Gender homophily from spatial behavior in a primary school: A sociometric study , 2013, Soc. Networks.

[23]  Adrian Streinu-Cercel,et al.  Neurocognitive impairment screening in Romanian HCV infected patients , 2013, BMC Infectious Diseases.

[24]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[25]  Carlo Ratti,et al.  Mining Urban Performance: Scale-Independent Classification of Cities Based on Individual Economic Transactions , 2014, ArXiv.

[26]  Alessandro Vespignani,et al.  Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm , 2012, BMC Medicine.

[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]  Jasmine Novak,et al.  Geographic routing in social networks , 2005, Proc. Natl. Acad. Sci. USA.

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

[30]  Víctor Soto,et al.  Automated land use identification using cell-phone records , 2011, HotPlanet '11.

[31]  C. Furlanello,et al.  Mitigation Measures for Pandemic Influenza in Italy: An Individual Based Model Considering Different Scenarios , 2008, PloS one.

[32]  M. Kretzschmar,et al.  Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents. , 2006, American journal of epidemiology.

[33]  Alessandro Vespignani,et al.  The Twitter of Babel: Mapping World Languages through Microblogging Platforms , 2012, PloS one.

[34]  Mary Anne Kennan,et al.  The State of the Nation: A Snapshot of Australian Institutional Repositories , 2009, First Monday.

[35]  Benjamin J. Cowling,et al.  Assortativity and the Probability of Epidemic Extinction: A Case Study of Pandemic Influenza A (H1N1-2009) , 2010, Interdisciplinary perspectives on infectious diseases.

[36]  Emilio Ferrara,et al.  A large-scale community structure analysis in Facebook , 2011, EPJ Data Science.

[37]  Fred Mannering,et al.  Modeling Travelers' Postwork Activity Involvement: Toward a New Methodology , 1993, Transp. Sci..

[38]  M. McNally,et al.  A MODEL OF ACTIVITY PARTICIPATION AND TRAVEL INTERACTIONS BETWEEN HOUSEHOLD HEADS , 1996 .

[39]  M. Barthelemy,et al.  From mobile phone data to the spatial structure of cities , 2014, Scientific Reports.

[40]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[41]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[42]  G. W. Brown,et al.  On Median Tests for Linear Hypotheses , 1951 .

[43]  V. Colizza,et al.  Age-specific contacts and travel patterns in the spatial spread of 2009 H1N1 influenza pandemic , 2013, BMC Infectious Diseases.

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

[45]  Enrique Frías-Martínez,et al.  Cross-Checking Different Sources of Mobility Information , 2014, PloS one.

[46]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

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

[48]  D. Watts A twenty-first century science , 2007, Nature.

[49]  Carlo Ratti,et al.  Cellular Census: Explorations in Urban Data Collection , 2007, IEEE Pervasive Computing.

[50]  Alessandro Vespignani,et al.  Human Mobility Networks, Travel Restrictions, and the Global Spread of 2009 H1N1 Pandemic , 2011, PloS one.

[51]  A. Nizam,et al.  Containing Pandemic Influenza at the Source , 2005, Science.

[52]  Petter Holme,et al.  Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts , 2010, PLoS Comput. Biol..

[53]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[54]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[55]  J. Hyman,et al.  Scaling laws for the movement of people between locations in a large city. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[56]  Víctor M. Eguíluz,et al.  Entangling Mobility and Interactions in Social Media , 2013, PloS one.

[57]  Filippo Menczer,et al.  Traveling trends: social butterflies or frequent fliers? , 2013, COSN '13.

[58]  Pere Colet,et al.  Tweets on the Road , 2014, PloS one.

[59]  Esteban Moro,et al.  Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media , 2011, PloS one.

[60]  A. Vespignani Predicting the Behavior of Techno-Social Systems , 2009, Science.

[61]  J. H. Ellis,et al.  Erratum: Assessing the Impact of Airline Travel on the Geographic Spread of Pandemic Influenza , 2004, European Journal of Epidemiology.

[62]  Lars Backstrom,et al.  Find me if you can: improving geographical prediction with social and spatial proximity , 2010, WWW '10.

[63]  V. Colizza,et al.  Metapopulation epidemic models with heterogeneous mixing and travel behaviour , 2014, Theoretical Biology and Medical Modelling.

[64]  A. Pentland,et al.  Computational Social Science , 2009, Science.

[65]  Yu-Ru Lin,et al.  Mesoscopic Structure and Social Aspects of Human Mobility , 2012, PloS one.

[66]  Carlo Ratti,et al.  Geo-located Twitter as proxy for global mobility patterns , 2013, Cartography and geographic information science.