Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-26T00:23:23.361Z Has data issue: false hasContentIssue false
  • English
  • Français

Human Mobility Patterns at the Smallest Scales

Published online by Cambridge University Press:  30 July 2015

Pedro G. Lind  and
Adriano Moreira
Pedro G. Lind*
Affiliation:
ForWind - Center for Wind Energy Research, Institute of Physics, Carl-von-Ossietzky University of Oldenburg, DE-26111 Oldenburg, Germany
Adriano Moreira
Affiliation:
Centro Algoritmi, Escola de Engenharia, Universidade do Minho, Campus de Azurém, 4800-058 Guimarã es, Portugal
*
*Corresponding author. Email addresses: [email protected] (P. G. Lind), [email protected](A. Moreira)
Get access

Abstract

We present a study on human mobility at small spatial scales. Differently from large scale mobility, recently studied through dollar-bill tracking and mobile phone data sets within one big country or continent, we report Brownian features of human mobility at smaller scales. In particular, the scaling exponents found at the smallest scales is typically close to one-half, differently from the larger values for the exponent characterizing mobility at larger scales. We carefully analyze 12-month data of the Eduroam database within the Portuguese university of Minho. A full procedure is introduced with the aim of properly characterizing the human mobility within the network of access points composing the wireless system of the university. In particular, measures of flux are introduced for estimating a distance between access points. This distance is typically non-Euclidean, since the spatial constraints at such small scales distort the continuum space on which human mobility occurs. Since two different exponents are found depending on the scale human motion takes place, we raise the question at which scale the transition from Brownian to non-Brownian motion takes place. In this context, we discuss how the numerical approach can be extended to larger scales, using the full Eduroam in Europe and in Asia, for uncovering the transition between both dynamical regimes.

Keywords

89.75.Da89.75.Fb89.75.HcNetworksdata analysishuman mobility
Type
Research Article
Information
Communications in Computational Physics , Volume 18 , Issue 2 , August 2015 , pp. 417 - 428
Copyright
Copyright © Global-Science Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Guo, L., Cai, X., Emergence of Community Structure in the Adaptive Social Networks, Commun. Comput. Phys., 8 (2010), 835–844.Google Scholar
[2]Toral, R., Tessone, C.J., Finite Size Effects in the Dynamics of Opinion Formation, Commun. Comput. Phys., 2 (2007), 177–195.Google Scholar
[3]Ke, J., Gong, T., Wang, W.S., Language Change and Social Networks, Commun. Comput. Phys., 3 (2008), 935–949.Google Scholar
[4]González, M.C., Hidalgo, C.A. and Barabási, A.-L., Understanding individual human mobility patterns, Nature, 453 (2008), 779–782.Google Scholar
[5]Brockmann, D., Hufnagel, L. and Geisel, T., The scaling laws of human travel, Nature, 439 (2006), 462–465.Google Scholar
[6]Ahas, R., Aasa, A., Mark, Ü., Pae, T., Kull, A., Seasonal tourism spaces in Estonia: case study with mobile positioning data, Tourism Manag., 28 (2006) 898–910.Google Scholar
[7]Ahas, R., Aasa, A., Silm, S., Aunap, R., Kalle, H., Mark, Ü., Mobile Positioning in Space Time Behaviour Studies: Social Positioning Method Experiments in Estonia, Cart. Geog. Inf. Sci., 34 (2007) 259–273.Google Scholar
[8]Shlesinger, M.F., Klafter, J. and Zumofen, G., Above, below and beyond Brownian motion, Am. J. Phys., 67 (1999) 1253–1259.Google Scholar
[9]Moreira, A., Santos, M.Y., Enhancing a user context by real-time clustering mobile trajectories, Proceedings of the International Conference on Information Technology: coding and computing – ITCC 2005, April 4-8, Las Vegas, NV, USA, 2005.Google Scholar
[10]Moreira, A., Santos, M.Y., From GPS tracks to context – Inference of high-level context information through spatial clustering, Proceedings of the II International Conference & Exhibition on Geographic Information – GIS Planet 2005, May 30 – June 2, Estoril, Portugal, 2005.Google Scholar
[11]Meneses, F., Moreira, A., Using GSM CellID Positioning for Place Discovering, Proceedings of the Locare’06 - First Workshop on Location Based Services for Health Care, Innsbruck, Austria, November 28, 2006.Google Scholar
[12]Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. and Hwang, D.-U., Complex networks: Structures and dynamics, Phys. Rep., 424 (2006), 175–308.Google Scholar
[13]Dorogovtsev, S.N., Goltsev, A.V., Mendes, J.F.F.: Critical phenomena in complex networks, Rev. Mod. Phys., 80 (2008), 1275–1354.Google Scholar
[14]Lind, P.G., González, M.C. and Herrmann, H.J.: Cycles and clustering in bipartite networks, Phys. Rev. E, 72 (2005), 056127.Google Scholar
[15]González, M.C., Lind, P.G. and Herrmann, H.J., A system of mobile agents to model social networks, Phys. Rev. Lett., 96 (2006), 088702.Google Scholar
[16]González, M.C., Lind, P.G. and Herrmann, H.J., Networks based on collisions among mobile agents, Physica D, 224 (2006), 137–148.Google Scholar
[17]Lind, P.G. and Herrmann, H.J., New approaches to model and study social networks, New J. Phys., 9 (2007), 228.CrossRefGoogle Scholar
[18]Lind, P.G., Andrade, J.S. Jr., da Silva, L.R., Herrmann, H.J.: Model of mobile agents for sexual interactions networks, Europhys. Lett., 78 (2007), 68005.Google Scholar
[19]Raischel, F., Moreira, A., Lind, P.G., From human mobility to renewable energies, Eur. J. Phys. Spec. Top., 223 (2014), 2107–2118.Google Scholar