Discussion of the paper “analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan”

Orietta Nicolis, Jorge Mateu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The authors are to be congratulated on a valuable and thought-provoking contribution on the analysis of geo-referenced high-dimensional data describing the use over time of the mobile-phone network in the urban area of Milan, Italy. This is a timely and world-wide problem that opens wide avenues for new methodological contributions. The authors develop a Bagging Voronoi Treelet Analysis which is a non-parametric method for the analysis of spatially dependent functional data. This approach integrates the treelet decomposition with a proper treatment of spatial dependence, obtained through a Bagging Voronoi strategy. In our discussion, we focus on the following points: (i) a mobre general form of the spatio-temporal model proposed in Secchi et al. (Stat Methods Appl, 2015), (ii) alternative methods to approach the smooth temporal functions, (iii) additional methods to reduce the problem of dimension for spatial dependence data, and (iv) comments on the pros and cons of the proposed pre-processing methodology.

Original languageEnglish
Pages (from-to)315-319
Number of pages5
JournalStatistical Methods and Applications
Issue number2
Publication statusPublished - 7 Jul 2015


  • Basis functions
  • Dimension reduction
  • Gaussian random fields
  • Spatially dependent functional data
  • Spatio-temporal stochastic models

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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