Advertisement

Statistical Methods & Applications

, Volume 24, Issue 2, pp 279–300 | Cite as

Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan

  • Piercesare Secchi
  • Simone Vantini
  • Valeria Vitelli
Article

Abstract

We analyze geo-referenced high-dimensional data describing the use over time of the mobile-phone network in the urban area of Milan, Italy. Aim of the analysis is to identify subregions of the metropolitan area of Milan sharing a similar pattern along time, and possibly related to activities taking place in specific locations and/or times within the city. To tackle this problem, we develop a non-parametric method for the analysis of spatially dependent functional data, named Bagging Voronoi Treelet analysis. This novel approach integrates the treelet decomposition with a proper treatment of spatial dependence, obtained through a Bagging Voronoi strategy. The latter relies on the aggregation of different replicates of the analysis, each involving a set of functional local representatives associated to random Voronoi-based neighborhoods covering the investigated area. Results clearly point out some interesting temporal patterns interpretable in terms of population density mobility (e.g., daily work activities in the tertiary district, leisure activities in residential areas in the evenings and in the weekend, commuters movements along the highways during rush hours, and localized mob concentrations related to occasional events). Moreover we perform simulation studies, aimed at investigating the properties and performances of the method, and whose description is available online as Supplementary material.

Keywords

Spatial statistics Functional data analysis Treelet analysis  Voronoi tessellation Bagging Erlang data 

Notes

Acknowledgments

This research has been carried out within the Green Move Project, a joint research program involving MOX Laboratory for Modeling and Scientific Computing (Department of Mathematics, Politecnico di Milano) and funded by Regione Lombardia. We thank Convenzione di Ricerca DiAP–Politecnico di Milano and Telecom Italia that provided the data. We would also like to thank Paola Pucci, Fabio Mafredini and Paolo Tagliolato (Department of Architecture and Urban Studies, Politecnico di Milano) for the interesting discussions on the interpretation of the outcomes of the statistical analysis described in this paper.

Supplementary material

10260_2014_294_MOESM1_ESM.pdf (479 kb)
Supplementary material 1 (pdf 479 KB)

References

  1. Banerjee S, Carlin B, Gelfand A (2004) Hierarchical modeling and analysis for spatial data. Monographs on statistics and applied probability. Chapman & Hall, LondonGoogle Scholar
  2. Becker RA, Caceres R, Hanson K, Loh JM, Urbanek S, Varshavsky A, Volinsky C (2011) A tale of one city: using cellular network data for urban planning. IEEE Pervasive Comput 10(4):18–26CrossRefGoogle Scholar
  3. Calabrese F, Lorenzo GD, Liu L, Ratti C (2011) Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput 10(4):36–44CrossRefGoogle Scholar
  4. James GM (2007) Curve alignment by moments. Ann Appl Stat 1:480–501MathSciNetCrossRefzbMATHGoogle Scholar
  5. Kaziska D, Srivastava A (2007) Gait-based human recognition by classification of cyclostationary processes on nonlinear shape manifolds. J Am Stat Assoc 102:1114–1128MathSciNetCrossRefzbMATHGoogle Scholar
  6. Ke C, Wang Y (2001) Semiparametric nonlinear mixed-effects models and their applications. J Am Stat Assoc 96:1272–1298MathSciNetCrossRefzbMATHGoogle Scholar
  7. Kunsch H, Geman S, Kehagias A (1995) Hidden markov random fields. Ann Appl Probab 5(3):577–602MathSciNetCrossRefGoogle Scholar
  8. Lee AB, Nadler B, Wasserman L (2008) Treelets—an adaptive multi-scale basis for sparse unordered data. Ann Appl Stat 2(2):435–471MathSciNetCrossRefzbMATHGoogle Scholar
  9. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693CrossRefzbMATHGoogle Scholar
  10. Manfredini F, Pucci P, Secchi P, Tagliolato P, Vantini S, Vitelli V (2015) Treelet decomposition of mobile phone data for deriving city usage and mobility pattern in the Milan urban region. In: Paganoni AM, Secchi P (eds) Advances in complex data modeling and computational methods in statistics., Contributions to statisticsSpringer, Berlin, pp 133–147Google Scholar
  11. OECD (2006a) OECD Territorial reviews: competitive cities in the global economy. OECD Publishing, ParisCrossRefGoogle Scholar
  12. OECD (2006b) OECD Territorial reviews: Milan, Italy. OECD Publishing, ParisGoogle Scholar
  13. Ramsay JO, Li X (1998) Curve registration. J R Stat Soc Ser B Stat Methodol 60:351–363MathSciNetCrossRefzbMATHGoogle Scholar
  14. Ramsay JO, Silverman BW (2005) Functional data analysis. Springer, BerlinCrossRefGoogle Scholar
  15. Sangalli LM, Secchi P, Vantini S, Veneziani A (2009) A case study in exploratory functional data analysis: geometrical features of the internal carotid artery. J Am Stat Assoc 104:37–48MathSciNetCrossRefGoogle Scholar
  16. Sangalli LM, Secchi P, Vantini S, Vitelli V (2010) K-mean alignment for curve clustering. Comput Stat Data Anal 54:1219–1233MathSciNetCrossRefzbMATHGoogle Scholar
  17. Secchi P, Vantini S, Vitelli V (2013) Bagging voronoi classifiers for clustering spatial functional data. Int J Appl Earth Obs Geoinf 22:53–64CrossRefGoogle Scholar
  18. Secchi P, Vantini S, Zanini P (2014) Hierarchical independent component analysis: a multi-resolution non-orthogonal data-driven basis. In: Tech Rep 01/2014, MOX—Dipartimento di Matematica, Politecnico di MilanoGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Piercesare Secchi
    • 1
  • Simone Vantini
    • 1
  • Valeria Vitelli
    • 2
  1. 1.MOX - Dipartimento di MatematicaPolitecnico di MilanoMilanItaly
  2. 2.Oslo Center for Biostatistics and Epidemiology, Department of BiostatisticsUniversity of OsloOsloNorway

Personalised recommendations