Advertisement

The Impact of Spatial Resolution and Representation on Human Mobility Predictability

  • Weicheng Qian
  • Kevin G. Stanley
  • Nathaniel D. Osgood
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7820)

Abstract

Western society is distinguished by its mobility. At no time in our history have we enjoyed the capacity to travel as rapidly, conveniently and safely. On the surface, this might suggest that human mobility patterns are highly irregular and impossible to predict. Drawing on a detailed multisensory positioning data set, we replicate earlier cell tower based predictability analyses with granular spatial and temporal multisensory data, and demonstrate a spatial resolution dependence of entropy, while reinforcing the claims of inherent predictability of human mobility advanced in early works. We demonstrate that mobility entropies reported with GPS data remain essentially unchanged with pruning of noisy GPS signals, lending additional credence to our methodology. We further compare cell tower results to those from WiFi-based localization for exactly the same time periods and participants, and demonstrate that the finer spatial resolution of WiFi also results in reported entropy exceeding that from cell tower traces, indicating that the resolution dependence observed for GPS data is not entirely due to GPS noise or discretization representation. This work represents a significant step towards fundamental understanding of human mobility patterns, which serve as key mediators to policy design in fields as diverse as public health, urban planning, and delay tolerant networks.

Keywords

Measurement Experimentation Human Factors 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fMRI: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7(6), 643–659 (2011)CrossRefGoogle Scholar
  2. 2.
    Amundson, I., Koutsoukos, X.D.: A Survey on Localization for Mobile Wireless Sensor Networks. In: Fuller, R., Koutsoukos, X.D. (eds.) MELT 2009. LNCS, vol. 5801, pp. 235–254. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Bell, S., Jung, W.: Wifi-based enhanced positioning systems: Accuracy through Mapping, Calibration, and Classification. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, pp. 3–9 (November 2010)Google Scholar
  4. 4.
    Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006)CrossRefGoogle Scholar
  5. 5.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  6. 6.
    González, M.C., Hidalgo, C.A., Barabási, A.-L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRefGoogle Scholar
  7. 7.
    Hashemian, M., Knowles, D., Calver, J., Qian, W., Bullock, M.C., Bell, S., Mandryk, R.L., Osgood, N.D., Stanley, K.G.: iEpi: an end to end solution for collecting, conditioning and utilizing epidemiologically relevant data. In: Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare (MobileHealth 2012), pp. 3–8. ACM (2012)Google Scholar
  8. 8.
    Hashemian, M.S., Stanley, K.G., Knowles, D.L., Calver, J., Osgood, N.D.: Human network data collection in the wild: the epidemiological utility of micro-contact and location data. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium (IHI 2012), pp. 255–264. ACM (2012)Google Scholar
  9. 9.
    Hui, P., Crowcroft, J., Yoneki, E.: Bubble rap: social-based forwarding in delay tolerant networks. In: Proceedings of the 9th ACM International Symposium on Mobile ad Hoc Networking and Computing (MobiHoc 2008), pp. 241–250. ACM (2008)Google Scholar
  10. 10.
    Isaacman, S., Becker, R.: Human mobility modeling at metropolitan scales. In: Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys 2012), pp. 239–252. ACM (2012)Google Scholar
  11. 11.
    Jensen, B., Larsen, J., Hansen, L.: Predictability of Mobile Phone Associations. In: Inter. Workshop on Mining Ubiquitous and Social Environments (2010)Google Scholar
  12. 12.
    Jiang, X., Liu, Y., Wang, X.: Proceedings of the 2009 WASE International Conference on Information Engineering (ICIE 2009), vol. 1, pp. 169–172. IEEE Computer Society (2009)Google Scholar
  13. 13.
    Karagiannis, T., Le Boudec, J.Y., Vojnovic, M.: Power law and exponential decay of intercontact times between mobile devices. IEEE Transactions on Mobile Computing 9(10), 1377–1390 (2010)CrossRefGoogle Scholar
  14. 14.
    Kosta, S., Mei, A.: Small world in motion (SWIM): Modeling communities in ad-hoc mobile networking. In: Proceedings of The 7th IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2010), Boston, MA, U.S.A., pp. 2106–2113 (June 2010)Google Scholar
  15. 15.
    Kotz, D., Essien, K.: Analysis of a campus-wide wireless network. Wireless Networks 11(1-2), 115–133 (2005)CrossRefGoogle Scholar
  16. 16.
    Lee, K., Hong, S., Kim, S., Rhee, I.: SLAW: A new mobility model for human walks. In: IEEE International Conference on Computer Communications (INFOCOM 2009), pp. 855–863. IEEE (2009)Google Scholar
  17. 17.
    Loxcel. Canadian Cell Tower Map, http://www.loxcel.com/celltower
  18. 18.
    May, R.M.: Network structure and the biology of populations. Trends in Ecology & Evolution 21(7), 394–399 (2006)CrossRefGoogle Scholar
  19. 19.
    Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: indoor location sensing using active RFID. Wireless Networks 10(6), 701–710 (2004)CrossRefGoogle Scholar
  20. 20.
    Saab, S., Nakad, S.: A standalone RFID indoor positioning system using passive tags. IEEE Transactions on Industrial Electronics 58, 1961–1970 (2010)Google Scholar
  21. 21.
    Scott, D.W.: On optimal and data-based histograms. Biometrika 66(3), 605–610 (1979)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
  23. 23.
    Song, C., Koren, T., Wang, P., Barabási, A.-L.: Modelling the scaling properties of human mobility. Nature Physics 6(10), 818–823 (2010)CrossRefGoogle Scholar
  24. 24.
    Song, C., Qu, Z., Blumm, N., Barabási, A.-L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Yuan, Y., Raubal, M.: Extracting Dynamic Urban Mobility Patterns from Mobile Phone Data. In: Xiao, N., Kwan, M.-P., Goodchild, M.F., Shekhar, S. (eds.) GIScience 2012. LNCS, vol. 7478, pp. 354–367. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Weicheng Qian
    • 1
  • Kevin G. Stanley
    • 1
  • Nathaniel D. Osgood
    • 1
    • 2
  1. 1.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
  2. 2.Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada

Personalised recommendations