2,276
Views
64
CrossRef citations to date
0
Altmetric
Original Articles

Analyzing the distribution of human activity space from mobile phone usage: an individual and urban-oriented study

&
Pages 1594-1621 | Received 09 Sep 2014, Accepted 31 Dec 2015, Published online: 12 Feb 2016

References

  • Ahas, R., et al., 2010. Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: case study with mobile positioning data. Transportation Research Part C-Emerging Technologies, 18 (1), 45–54. doi:10.1016/j.trc.2009.04.011
  • Ahas, R., et al., 2015. Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. International Journal of Geographical Information Science, 29 (11), 2017–2039.
  • Azevedo, T.S., et al., 2009. An analysis of human mobility using real traces. Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference. Budapest, Hungary: IEEE Press, 2390–2395.
  • Bagrow, J.P. and Koren, T., 2009. Investigating bimodal clustering in human mobility. International Conference on Computational Science and Engineering. Vancouver, Canada, 944–947.
  • Beckmann, K. 2000. Umweltgerechtes Verkehrsverhalten beginnt in den Köpfen. ed. Mobilitätsforschung für das 21, Köln, Jahrhundert, 213–238.
  • Borrel, V., De Amorim, M.D., and Fdida, S., 2006, On natural mobility models. Autonomic Communication, 3854, 243–253.
  • Box, G.E.P. and Draper, N.R., 1987. Empirical model-building and response surfaces. New York: Wiley.
  • Brown, L.A. and Moore, E.G., 1970, The intra-urban migration process: a perspective. Geografiska Annaler. Series B, Human Geography, 52 (1), 1–13. doi:10.2307/490436
  • Candia, J., et al., 2008. Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical, 41 (22), 1–11. doi:10.1088/1751-8113/41/22/224015
  • Caragliu, A., Bo, C.D., and Nijkamp, P., 2009. Smart cities in Europe, Serie Research Memoranda 0048.
  • Chainey, S., Tompson, L., and Uhlig, S., 2008, The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21 (1–2), 4–28. doi:10.1057/palgrave.sj.8350066
  • Cheyne, J.A. and Efran, M.G., 1972, The effect of spatial and interpersonal variables on the invasion of group controlled territories. Sociometry, 35 (3), 477–489. doi:10.2307/2786507
  • Fiore, F.D., et al. 2014. A set of perspectives on how mobile technology may affect travel. Journal of Transport Geography, 41, 97–106. doi:10.1016/j.jtrangeo.2014.08.014
  • Forbes, 2014. Scientists Warn About Bias In The Facebook And Twitter Data Used In Millions Of Studies.
  • Fuchs, M. and Busse, B., 2009, The coverage bias of mobile web surveys across European countries. International Journal of Internet Science, 4, 21–33.
  • Gao, S., et al., 2013. Discovering spatial interaction communities from mobile phone data. Transactions in GIS, 17 (3), 463–481. doi:10.1111/tgis.12042
  • Golledge, R.G. and Stimson, R.J., 1997. Spatial behavior: a geographic perspective. New York: Guilford Press.
  • González, M.C., Hidalgo, C.A., and Barabási, A.-L., 2008, Understanding individual human mobility patterns. Nature, 453 (7196), 779–782. doi:10.1038/nature06958
  • Gordon, P., Kumar, A., and Richardson, H.W., 1989, The influence of metropolitan spatial structure on commuting time. Journal of Urban Economics, 26 (2), 138–151. doi:10.1016/0094-1190(89)90013-2
  • Groves, P.D., 2013. Principles of GNSS, inertial, and multisensor integrated navigation systems. 2nd ed. Boston: Artech House.
  • Hägerstrand, T., 1970. What about people in regional science? Papers of the Regional Science Association, 24, 7–21. doi:10.1111/j.1435-5597.1970.tb01464.x
  • Harding, C., et al., 2012. Modeling the effect of land use on activity spaces. Transportation Research Record, 2323, 67–74. doi:10.3141/2323-08
  • Harvey, A.S. and Taylor, M.E., 2000, Activity settings and travel behaviour: A social contact perspective. Transportation, 27 (1), 53–73. doi:10.1023/A:1005207320044
  • Hasan, S., et al., 2013. Spatiotemporal patterns of urban human mobility. Journal of Statistical Physics, 151 (1–2), 304–318. doi:10.1007/s10955-012-0645-0
  • Herrera, J.C., et al., 2010. Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment. Transportation Research Part C-Emerging Technologies, 18 (4), 568–583. doi:10.1016/j.trc.2009.10.006
  • Horton, F. and Reynolds, D.R., 1971. Effects of urban spatial structure on individual behavior. Economic Geography, 47, 36–48. doi:10.2307/143224
  • Jiang, B. and Jia, T. 2011. Exploring human mobility patterns based on location information of US flights. arXiv:1104.4578.
  • Jiang, B. and Yin, J., 2013. Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features, arXiv:1305.0883.
  • Kang, C., et al., 2012. Intra-urban human mobility patterns: an urban morphology perspective. Physica A: Statistical Mechanics and its Applications, 391 (4), 1702–1717. doi:10.1016/j.physa.2011.11.005
  • Knox, P.L. and McCarthy, L., 2012. Urbanization: an introduction to urban geography. 3rd ed. Pearson: Boston.
  • Leitch, R.D., 1995. Reliability analysis for engineers: an introduction. Oxford; New York: Oxford University Press.
  • Lewis, G.K., 1959, Changes in suburban land-use patterns. Annals of the Association of American Geographers, 49 (2), 194–195.
  • Liu, Y., et al., 2012. Understanding intra-urban trip patterns from taxi trajectory data. Journal of Geographical Systems, 14 (4), 463–483. doi:10.1007/s10109-012-0166-z
  • Lu, Y., 2000. Spatial cluster analysis of point data: location quotients versus kernel density. Portland, OR: University Consortium of Geographic Information Science (UCGIS) Summer Assembly Graduate Papers.
  • Mason, M.J. and Korpela, K., 2009, Activity spaces and urban adolescent substance use and emotional health. Journal of Adolescence, 32 (4), 925–939. doi:10.1016/j.adolescence.2008.08.004
  • Mazey, M.E., 1981, The effect of a physio-political barrier upon urban activity space. Ohio Journal of Science, 81 (5–6), 212–217.
  • Mennis, J. and Mason, M.J., 2011, People, places, and adolescent substance use: integrating activity space and social network data for analyzing health behavior. Annals of the Association of American Geographers, 101 (2), 272–291. doi:10.1080/00045608.2010.534712
  • Miller, H.J., 2009. Geographic data mining and knowledge discovery: an overview. In: H.J. Miller and J. Han, eds. Geographic data mining and knowledge discovery. 2nd ed. London: CRC Press, 3–32.
  • Morgan, E.C., et al., 2011. Probability distributions for offshore wind speeds. Energy Conversion and Management, 52 (1), 15–26. doi:10.1016/j.enconman.2010.06.015
  • Neubeck, K., 2004. Practical reliability analysis. Upper Saddle River, NJ: Prentice Hall.
  • Nobis, C., Lenz, B., and Vance, C., 2005. Communication and travel behaviour: two facets of human activity patterns. In: H. Timmermans, ed. Progress in activity-based analysis. Oxford, UK: Elsevier, 471–488.
  • Ott, T. and Swiaczny, F., 2001. Time-integrative geographic information systems: management and analysis of spatio-temporal data [online]. Berlin; New York: Springer. Available from: http://www.loc.gov/catdir/toc/fy033/2001266545.html; http://www.loc.gov/catdir/enhancements/fy0812/2001266545-d.html
  • Pendyala, R.M., Goulias, K.G., and Kitamura, R., 1991, Impact of telecommuting on spatial and temporal patterns of household travel. Transportation, 18 (4), 383–409. doi:10.1007/BF00186566
  • Phithakkitnukoon, S., et al. 2010. Activity-aware map: Identifying human daily activity pattern using mobile phone data. In: A.A. Salah, et al. eds. HBU 2010. Heidelberg: LNCS, Springer, 14–25.
  • Ratti, C., et al. 2007. Mobile landscapes: graz in real time. In: G. Gartner, W. Cartwright and M.P. Peterson, eds. Location based services and teleCartography. Berlin: Springer, 433–444.
  • Rhee, I., et al., 2011. On the levy-walk nature of human mobility. Ieee-Acm Transactions on Networking, 19 (3), 630–643. doi:10.1109/TNET.2011.2120618
  • Rinne, H., 2008. The Weibull distribution: a handbook. Boca Raton, FL: Chapman and Hall/CRC.
  • Salingaros, N.A., 1998, Theory of the urban web. Journal of Urban Design, 3 (1), 53–71. doi:10.1080/13574809808724416
  • Schönfelder, S., 2006. Urban rhythms: Modelling the rhythms of individual travel behaviour. ( Doctoral). Swiss Federal Institute of Technology.
  • Schönfelder, S. and Axhausen, K.W., 2002. Measuring the size and structure of human activity spaces: the longitudinal perspective. In: Arbeitsberichte Verkehrs- und Raumplanung. Zürich: ETH Zürich, 135, IVT. DOI:10.3929/ethz-a-004444846.
  • Sherman, J.E., et al., 2005. A suite of methods for representing activity space in a healthcare accessibility study. International Journal of Health Geographics, 4 (1), 1–21. doi:10.1186/1476-072X-4-24
  • Silm, S. and Ahas, R., 2014, Ethnic differences in activity spaces: a study of out-of-home nonemployment activities with mobile phone data. Annals of the Association of American Geographers, 104 (3), 542–559. doi:10.1080/00045608.2014.892362
  • Singh, V., 1987, On application of the Weibull distribution in hydrology. Water Resources Management, 1 (1), 33–43. doi:10.1007/BF00421796
  • Song, C.M., et al., 2010. Limits of predictability in human mobility. Science, 327 (5968), 1018–1021. doi:10.1126/science.1177170
  • Talen, E., 1999, Sense of community and neighbourhood form: an assessment of the social doctrine of new urbanism. Urban Studies, 36 (8), 1361–1379. doi:10.1080/0042098993033
  • Traunmueller, M., Quattrone, G., and Capra, L., Mining mobile phone data to investigate urban crime theories at scale. ed. International Conference on Social Informatics, 2014, 396–411.
  • Warf, B., 2010. Encyclopedia of geography. Thousand Oaks, CA: Sage Publications.
  • Weibull, W., 1951, A statistical distribution function of wide applicability. Journal of Applied Mechanics—Transactions of the ASME, 18 (3), 293–297.
  • Xia, Y., 2005. Integrating uncertainty in data mining. Doctoral Dissertation. University of California.
  • Yuan, Y. and Raubal, M., 2012. Extracting dynamic urban mobility patterns from mobile phone data. In: N. Xiao, et al., ed. Geographic information science – 7th international conference. Columbus, OH: Springer, 354–367.
  • Yuan, Y. and Raubal, M., 2014, Measuring similarity of mobile phone user trajectories – a Spatio-temporal Edit Distance method. International Journal of Geographical Information Science, 28 (3), 496–520. doi:10.1080/13658816.2013.854369
  • Yuan, Y., Raubal, M., and Liu, Y., 2012, Correlating mobile phone usage and travel behavior - a case study of Harbin, China. Computers, Environment and Urban Systems, 36 (2), 118–130. doi:10.1016/j.compenvurbsys.2011.07.003
  • Yue, Y., et al., 2014. Zooming into individuals to understand the collective: a review of trajectory-based travel behaviour studies. Travel Behaviour and Society, 1 (2), 69–78. doi:10.1016/j.tbs.2013.12.002

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.