1,838
Views
0
CrossRef citations to date
0
Altmetric
Articles

Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon

Bibliography

  • F. Asgari, V. Gauthier, and M. Becker, “A Survey on Human Mobility and its Applications” (2013) arXiv preprint arXiv:1307.0814.
  • P. Bajardi, M. Delfino, A. Panisson, G. Petri, and M. Tizzoni, “Unveiling Patterns of International Communities in a Global City Using Mobile Phone Data,” EPJ Data Science 4: 3 (2015) 1–17.
  • J. Beckers, M. Vanhoof, and A. Verhetsel, “Returning the Particular: Understanding Hierarchies in the Belgian Logistics System,” Journal of Transport Geography (2017) https://doi.org/10.1016/j.jtrangeo.2017.09.015. Accessed April 11, 2018.
  • V. D. Blondel, A. Decuyper, and G. Krings, “A Survey of Results on Mobile Phone Datasets Analysis,” EPJ Data Science 4: 10 (2015) 1–57.
  • I. Bojic, E. Massaro, A. Belyi, S. Sobolevsky, and C. Ratti, “Choosing the Right Home Location Definition Method for the Given Dataset,” in T.-Y. Liu, C. N. Scollon, and W. Zhu, eds, Social Informatics Proceedings 9471 (Bejing: Springer 2015) 194–208.
  • C. Brutel and D. Levy, “Le nouveau zonage en aires urbaines de 2010,” Insee Première 1374 (2011) p 2, <https://www.insee.fr/fr/statistiques/1281191> Accessed April 18, 2018.
  • C. Chen, L. Bian, and J. Ma, “From Traces to Trajectories: How Well Can We Guess Activity Locations from Mobile Phone Traces?” Transportation Research Part C: Emerging Technologies 46 (2014) 326–337. doi: 10.1016/j.trc.2014.07.001
  • Z. Cheng, J. Caverlee, K. Lee, and D. Sui, “Exploring Millions of Footprints in Location Sharing Services,” ICWSM 2011 (2011) 81–88.
  • E. Cho, S. Myers, and J. Leskovec, “Friendship and Mobility: User Movement in Location-Based Social Networks,” in Proceedings of the 17th ACM SIGKDD 2011 (2011) 1082–1090.
  • S. Combes, M.-P. De Bellefon, and M. Vanhoof, “Mining Mobile Phone Data to Recognize Urban Areas,” in Proceedings of New Techniques and Technologies for Statistics (NTTS) 2017 (2017) doi:10.2901/EUROSTAT.C2017.001
  • J. Cranshaw, E. Toch, and J. Hong, “Bridging the Gap between Physical Location and Online Social Networks,” in Proceedings of the 12th ACM International Conference on Ubiquitous Computing (2010) 119–128.
  • P. Deville, C. Linard, S. Martin, M. Gilbert, F. R. Stevens, A. E. Gaughan, V. D. Blondel, and A. J. Tatem, “Dynamic Population Mapping Using Mobile Phone Data,” Proceedings of the National Academy of Sciences of the USA 111: 45 (2014) 15888–15893. doi: 10.1073/pnas.1408439111
  • M. De Domenico, A. Lima, and M. Musolesi, “Interdependence and Predictability of Human Mobility and Social Interactions,” Pervasive and Mobile Computing 9: 6 (2013) 798–807. doi: 10.1016/j.pmcj.2013.07.008
  • M. C. González, C. A. Hidalgo, and A.-L. Barabási, “Understanding Individual Human Mobility Patterns,” Nature 453: 7196 (2008) 779–782. doi: 10.1038/nature06958
  • M. Janzen, M. Vanhoof, and K. W. Axhausen, “Estimating Long-Distance Travel Demand with Mobile Phone Billing Data,” paper presented at 16th Swiss Transport Research Conference (STRC 2016) (Ascona, Switzerland, May 18–20, 2016) 1–17.
  • M. Janzen, M. Vanhoof, Z. Smoreda, and K.W. Axhausen, “Closer to the Total? Long-Distance Travel of French Mobile Phone Users,” Travel Behaviour and Society 11 (2018) 31–42. doi: 10.1016/j.tbs.2017.12.001
  • O. Järv, R. Ahas, and F. Witlox, “Understanding Monthly Variability in Human Activity Spaces: A Twelve-Month Study Using Mobile Phone Call Detail Records,” Transportation Research Part C: Emerging Technologies 38 (2014) 122–135. doi: 10.1016/j.trc.2013.11.003
  • B.S. Jensen, J. E. Larsen, K. Jensen, J. Larsen, and L. K. Hansen, “Estimating Human Predictability from Mobile Sensor Data,” in Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010 (2010) 196–201. doi: 10.1109/MLSP.2010.5588997
  • C. Kang, X. Ma, D. Tong, and Y. Liu, “Intra-Urban Human Mobility Patterns: An Urban Morphology Perspective,” Physica A: Statistical Mechanics and its Applications 391: 4 (2012) 1702–1717. doi: 10.1016/j.physa.2011.11.005
  • K.S. Kung, K. Greco, S. Sobolevsky, and C. Ratti, “Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data,” PLoS ONE 9: 6 (2014) e96180, https://doi.org/10.1371/journal.pone.0096180
  • M. Lin, W.-J. Hsu, and Z. Q. Lee, “Predictability of Individuals’ Mobility with High-Resolution Positioning Data,” in Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp ‘12 (2012) 381–390.
  • F. Liu, D. Janssens, G. Wets, and M. Cools, “Annotating Mobile Phone Location Data with Activity Purposes Using Machine Learning Algorithms,” Expert Systems with Applications 40: 8 (2013) 3299–3311. doi: 10.1016/j.eswa.2012.12.100
  • Y. Liu, Z. Sui, C. Kang, Y. Gao, and D. Brockmann, “Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data,” PLoS ONE 9: 1 (2014) e86026, https://doi.org/10.1371/journal.pone.0086026
  • S. Lu, Z. Fang, X. Zhang, S.-L. Shaw, L. Yin, Z. Zhao, and X. Yang, “Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators,” ISPRS International Journal of Geo-Information 6: 1 (2017) 7, doi:10.3390/ijgi6010007
  • Y.A. de Montjoye, C.A. Hidalgo, M. Verleysen, V.D. Blondel, “Unique in the Crowd: The Privacy Bounds of Human Mobility,” Scientific Reports 3 (2013) 1376, doi: 10.1038/srep01376
  • Y. A. de Montjoye, L. Rocher, and A. S. Pentland “bandicoot: a Python Toolbox for Mobile Phone Metadata,” Journal of Machine Learning Research 17: 175 (2016) 1–5.
  • A. Noulas, S. Scellato, R. Lambiotte, M. Pontil, and C. Mascolo, “A Tale of Many Cities: Universal Patterns in Human Urban Mobility,” PLoS ONE 7: 5 (2012) e37027, https://doi.org/10.1371/journal.pone.0037027
  • N. D. Osgood, T. Paul, K. G. Stanley, and W. Qian, “A Theoretical Basis for Entropy-Scaling Effects in Human Mobility Patterns,” PLoS ONE 11: 8 (2016) e0161630 , https://doi.org/10.1371/journal.pone.0161630
  • L. Pappalardo, S. Rinzivillo, Z. Qu, D. Pedreschi, and F. Giannotti, “Understanding the Patterns of Car Travel,” The European Physical Journal Special Topics 215: 1 (2013) 61–73. doi: 10.1140/epjst/e2013-01715-5
  • L. Pappalardo, M. Vanhoof, L. Gabrielli, Z. Smoreda, D. Pedreschi, and F. Giannotti, “An Analytical Framework to Nowcast Well-Being Using Mobile Phone Data,” International Journal of Data Science and Analytics 2: 1-2 (2016) 75–92. doi: 10.1007/s41060-016-0013-2
  • S. Phithakkitnukoon, Z. Smoreda, and P. Olivier, “Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data,” PloS ONE 7: 6 (2012) e39253, https://doi.org/10.1371/journal.pone.0039253
  • G. Ranjan, H. Zang, Z.-L. Zhang, and J. Bolot, “Are Call Detail Records Biased for Sampling Human Mobility?” ACM SIGMOBILE Mobile Computing and Communications Review 16: 3 (2012) 33–44. doi: 10.1145/2412096.2412101
  • R. Schlich and K.W. Axhausen, “Habitual Travel Behavior: Evidence from a Six-Week Travel Diary,” Transportation 30:1 (2013) 13–36.
  • T. Schwanen, “Geographies of Transport II: Reconciling the General and the Particular,” Progress in Human Geography (2016) https://doi.org/10.1177/0309132516628259
  • C.E. Shannon, “A Mathematical Theory of Communication,” ACM SIGMOBILE Mobile Computing and Communications Review 5: 1 (2001) 3–55. Reprinted with corrections from The Bell System Technical Journal 27 (1948) 379–423, 623–656. doi: 10.1145/584091.584093
  • F. Simini, M. C. González, A. Maritan, and A.-L. Barabási, “A Universal Model for Mobility and Migration Patterns,” Nature 484: 7392 (2012) 96–100. doi: 10.1038/nature10856
  • G. Smith, R. Wieser, J. Goulding, and D. Barrack, “A Refined Limit on the Predictability of Human Mobility,” in Proceedings of 2014 IEEE International Conference on Pervasive Computing and Communications, PerCom 2014 (2014) 88–94.
  • C. Song, T. Koren, P. Wang, and A.-L. Barabási, “Modelling the Scaling Properties of Human Mobility,” Nature Physics. 6: 10 (2010a) 818–823. doi: 10.1038/nphys1760
  • C. Song, Z. Qu, N. Blumm, and A.-L. Barabási, “Limits of Predictability in Human Mobility” Science 327: 5968 (2010b) 1018–1021. doi: 10.1126/science.1177170
  • A. Sridharan and J. Bolot, “Location Patterns of Mobile Users: A Large-Scale Study,” in Proceedings of the 2013 IEEE INFOCOM (2013) 1007–1015.
  • M. Szell, R. Sinatra, G. Petri, S. Thurner, and V. Latora, “Understanding Mobility in a Social Petri Dish,” Scientific Reports 2: 457 (2012) 1–6.
  • Y. Tanahashi, J. R. Rowland, S. North, and K.-L. Ma, “Inferring Human Mobility Patterns from Anonymized Mobile Communication Usage,” in Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia MoMM (2012) 151–160.
  • J. L. Toole, C. Herrera-Yaque, C. M. Schneider, and M. C. Gonzales, “Coupling Human Mobility and Social Ties,” Journal of the Royal Society, Interface 12: 105 (2015) 1–14. doi: 10.1098/rsif.2014.1128
  • M. Vanhoof, S. Combes, and M-P. de Bellefon, “Mining Mobile Phone Data to Detect Urban Areas,” in A. Petrucci and R. Verde, eds, SIS 2017 Statistics and Data Science: New Challenges, New Generations. Proceedings of the Conference of the Italian Statistical Society (Florence: Firenze University Press, 2017) 1005–1012.
  • M. Vanhoof, F. Reis, T. Ploetz, and Z. Smoreda, “Detecting Home Locations from CDR Data: Introducing Spatial Uncertainty to the State-of-the-Art,” paper presented at Mobile Tartu 2016 (Tartu, 29 June–1 July 2016), http://eprint.ncl.ac.uk/author_pubs.aspx?author_id=183527
  • M. Vanhoof, F. Reis, T. Ploetz, and Z. Smoreda, “Assessing the Quality of Home Detection from Mobile Phone Data for Official Statistics,” Journal of Official Statistics (In Press 2018), preprint at http://eprint.ncl.ac.uk/author_pubs.aspx?author_id=183527
  • X.-W. Wang, X.-P. Han, and B.-H. Wang, “Correlations and Scaling Laws in Human Mobility,” PloS ONE 9: 1 (2014ba) e84954, https://doi.org/10.1371/journal.pone.0084954
  • J. Wolf, R. Guensler, and W. Bachman, “Elimination of the Travel Diary: Experiment to Derive Trip Purpose from GPS Travel Data,” Transportation Research Record 1768 (2001) 125–134. doi: 10.3141/1768-15
  • X.-Y. Yan, X.-P. Han, B.-H. Wang, and T. Zhou, “Diversity of Individual Mobility Patterns and Emergence of Aggregated Scaling Laws,” Scientific Reports 3 (2013) 2678, doi:10.1038/srep02678
  • Y. Yuan and M. Raubal, “Analyzing the Distribution of Human Activity Space from Mobile Phone Usage: An Individual and Urban-Oriented Study,” International Journal of Geographical Information Science 30: 8 (2016) 1594–1621. doi: 10.1080/13658816.2016.1143555
  • Y. Yuan, M. Raubal, and Y. Liu, “Correlating Mobile Phone Usage and Travel Behavior: A Case Study of Harbin, China,” Computers, Environment and Urban Systems 36: 2 (2012) 118–130. doi: 10.1016/j.compenvurbsys.2011.07.003
  • Z. Zhao, S.-L. Shaw, Y. Xu, F. Lu, J. Chen, and L. Yin, “Understanding the Bias of Call Detail Records in Human Mobility Research,” International Journal of Geographical Information Science 30: 9 (2016) 1738–1762. doi: 10.1080/13658816.2015.1137298