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Scaling Behavior of Human Mobility Distributions

  • Tuhin PaulEmail author
  • Kevin Stanley
  • Nathaniel Osgood
  • Scott Bell
  • Nazeem Muhajarine
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9927)

Abstract

Recent technical advances have made high-fidelity tracking of populations possible. However, these datasets, such as GPS traces, can be comprised of millions of records, well beyond what even a skilled analyst can digest. To facilitate human analysis, these records are often expressed as aggregate distributions capturing behaviors of interest. While these aggregate distributions can provide substantial insight, the spatio-temporal resolution at which they are captured can impact the shape of the resulting distribution. We present an analysis of five spatial datasets, and codify the impact of rebinning the data at different spatio-temporal resolutions. We find that all aggregate metrics considered are affected by rebinning, but that some distributions do so regularly and predictably, while others do not. This work provides important insight into which metrics can be used to compare human behavior across datasets and the kinds of relationships between that can be expected.

Keywords

Spatial data Mobility GPS Analytics 

Notes

Acknowledgments

We would like to acknowledge the Natural Sciences and Engineering Research Council of Canada for providing funding for this work.

References

  1. 1.
    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. Cartography Geogr. Inf. Sci. 34(4), 259–273 (2007)CrossRefGoogle Scholar
  2. 2.
    Arribas-Bel, D.: Accidental, open and everywhere: emerging data sources for the understanding of cities. Appl. Geogr. 49, 45–53 (2014)CrossRefGoogle Scholar
  3. 3.
    Barrat, A., Fernandez, B., Lin, K.K., Young, L.S.: Modeling temporal networks using random itineraries. Phys. Rev. Lett. 110(15), 158702 (2013)CrossRefGoogle Scholar
  4. 4.
    Bell, S., Wilson, K., Bissonnette, L., Shah, T.: Access to primary health care: does neighborhood of residence matter? Annals Assoc. Am. Geogr. 103(1), 85–105 (2013)CrossRefGoogle Scholar
  5. 5.
    Dark, S.J., Bram, D.: The modifiable areal unit problem (MAUP) in physical geography. Prog. Phys. Geogr. 31(5), 471–479 (2007)CrossRefGoogle Scholar
  6. 6.
    Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquitous Comput. 10(4), 255–268 (2006)CrossRefGoogle Scholar
  7. 7.
    Gabriel, A.K., Goldstein, R.M., Zebker, H.A.: Mapping small elevation changes over large areas: differential radar interferometry. J. Geophys. Res.: Solid Earth 94(B7), 9183–9191 (1989)CrossRefGoogle Scholar
  8. 8.
    Goodchild, M.F.: Giscience, geography, form, and process. Annals Assoc. Am. Geogr. 94(4), 709–714 (2004)Google Scholar
  9. 9.
    Hashemian, M., Knowles, D., Calver, J., Qian, W., Bullock, M.C., Bell, S., Mandryk, R.L., Osgood, N., 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, pp. 3–8. ACM (2012)Google Scholar
  10. 10.
    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, pp. 255–264. ACM (2012)Google Scholar
  11. 11.
    Kim, M., Kotz, D., Kim, S.: Extracting a mobility model from real user traces. In: INFOCOM 2006, 25th IEEE International Conference on Computer Communications. Proceedings, pp. 1–13, April 2006Google Scholar
  12. 12.
    Knowles, D.L., Stanley, K.G., Osgood, N.D.: A field-validated architecture for the collection of health-relevant behavioural data. In: 2014 IEEE International Conference on Healthcare Informatics (ICHI), pp. 79–88. IEEE (2014)Google Scholar
  13. 13.
    Kos, T., Grgic, M., Sisul, G.: Mobile user positioning in GSM/UMTS cellular networks. In: 48th International Symposium ELMAR-2006 Focused on Multimedia Signal Processing and Communications, pp. 185–188. IEEE (2006)Google Scholar
  14. 14.
    Kwan, M.P.: Algorithmic geographies: big data, algorithmic uncertainty, and the production of geographic knowledge. Annals. Am. Assoc. Geogr. 106(2), 274–282 (2016)Google Scholar
  15. 15.
    Lee, K., Hong, S., Kim, S.J., Rhee, I., Chong, S.: SLAW: a new mobility model for human walks. In: INFOCOM 2009, pp. 855–863. IEEE, April 2009Google Scholar
  16. 16.
    Miller, H.J., Goodchild, M.F.: Data-driven geography. GeoJournal 80(4), 449–461 (2015)CrossRefGoogle Scholar
  17. 17.
    Modsching, M., Kramer, R., ten Hagen, K.: Field trial on GPS accuracy in a medium size city: the influence of built-up. In: 3rd Workshop on Positioning, Navigation and Communication, pp. 209–218 (2006)Google Scholar
  18. 18.
    Muhajarine, N., Katapally, T.R., Fuller, D., Stanley, K.G., Rainham, D.: Longitudinal active living research to address physical inactivity and sedentary behaviour in children in transition from preadolescence to adolescence. BMC Public Health 15(1), 1–9 (2015). http://dx.doi.org/10.1186/s12889-015-1822-2 CrossRefGoogle Scholar
  19. 19.
    Openshaw, S., Openshaw, S.: The Modifiable Areal Unit Problem. Geo Abstracts, University of East Anglia, Norwich (1984)Google Scholar
  20. 20.
    Openshaw, S., Taylor, P.J.: A million or so correlation coefficients: three experiments on the modifiable areal unit problem. Stat. Appl. Spat. Sci. 21, 127–144 (1979)Google Scholar
  21. 21.
    Qian, W., Stanley, K.G., Osgood, N.D.: The impact of spatial resolution and representation on human mobility predictability. In: Liang, S.H.L., Wang, X., Claramunt, C. (eds.) W2GIS 2013. LNCS, vol. 7820, pp. 25–40. Springer, Heidelberg (2013)Google Scholar
  22. 22.
    Rhee, I., Shin, M., Hong, S., Lee, K., Kim, S.J., Chong, S.: On the levy-walk nature of human mobility. IEEE/ACM Trans. Networking 19(3), 630–643 (2011)CrossRefGoogle Scholar
  23. 23.
    Smith, G., Wieser, R., Goulding, J., Barrack, D.: A refined limit on the predictability of human mobility. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 88–94. IEEE (2014)Google Scholar
  24. 24.
    Song, C., Koren, T., Wang, P., Barabási, A.L.: Modelling the scaling properties of human mobility. Nat. Phys. 6(10), 818–823 (2010)CrossRefGoogle Scholar
  25. 25.
    Song, C., Koren, T., Wang, P., Barabási, A.L.: Modelling the scaling properties of human mobility supplementary material. Nat. Phys. 6(10), 1–20 (2010)CrossRefGoogle Scholar
  26. 26.
    Song, C., Qu, Z., Blumm, N., Barabási, A.L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Stanley, K., Bell, S., Kreuger, L.K., Bhowmik, P., Shojaati, N., Elliot, A., Osgood, N.D.: Opportunistic natural experiments using digital telemetry: a transit disruption case study. Int. J. Geogr. Inf. Sci. (to appear)Google Scholar
  28. 28.
    Sui, D., Goodchild, M.: The convergence of GIS and social media: challenges for giscience. Int. J. Geogr. Inf. Sci. 25(11), 1737–1748 (2011)CrossRefGoogle Scholar
  29. 29.
    Versichele, M., Neutens, T., Claeys Bouuaert, M., Van de Weghe, N.: Time-geographic derivation of feasible co-presence opportunities from network-constrained episodic movement data. Trans. GIS 18(5), 687–703 (2014)CrossRefGoogle Scholar
  30. 30.
    Wu, X., Mazurowski, M., Chen, Z., Meratnia, N.: Emergency message dissemination system for smartphones during natural disasters. In: 2011 11th International Conference on ITS Telecommunications (ITST), pp. 258–263. IEEE (2011)Google Scholar
  31. 31.
    Yuan, Y., Raubal, M., Liu, Y.: Correlating mobile phone usage and travel behavior-a case study of Harbin, China. Comput. Environ. Urban Syst. 36(2), 118–130 (2012)CrossRefGoogle Scholar
  32. 32.
    Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull. 33(2), 32–39 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Tuhin Paul
    • 1
    Email author
  • Kevin Stanley
    • 1
  • Nathaniel Osgood
    • 1
    • 2
  • Scott Bell
    • 3
  • Nazeem Muhajarine
    • 2
  1. 1.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
  2. 2.Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
  3. 3.Department of Geography and PlanningUniversity of SaskatchewanSaskatoonCanada

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