IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v24y2015i2p315-319.html
   My bibliography  Save this article

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

Author

Listed:
  • Orietta Nicolis
  • Jorge Mateu

Abstract

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Orietta Nicolis & Jorge Mateu, 2015. "Discussion of the paper “analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 315-319, July.
    • Handle: RePEc:spr:stmapp:v:24:y:2015:i:2:p:315-319
    DOI: 10.1007/s10260-015-0311-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10260-015-0311-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10260-015-0311-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Rejoinder to the discussion of “Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 335-338, July.
    2. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 279-300, July.
    3. Sudipto Banerjee & Alan E. Gelfand & Andrew O. Finley & Huiyan Sang, 2008. "Gaussian predictive process models for large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(4), pages 825-848, September.
    4. Matsuo, Tomoko & Nychka, Douglas W. & Paul, Debashis, 2011. "Nonstationary covariance modeling for incomplete data: Monte Carlo EM approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2059-2073, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Piercesare Secchi & Simone Vantini & Valeria Vitelli, 2015. "Rejoinder to the discussion of “Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 335-338, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    2. Claudio Gariazzo & Armando Pelliccioni & Maria Paola Bogliolo, 2019. "Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy," Data, MDPI, vol. 4(1), pages 1-25, January.
    3. Alessandro Fassò & Francesco Finazzi & Ferdinand Ndongo, 2016. "European Population Exposure to Airborne Pollutants Based on a Multivariate Spatio-Temporal Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 492-511, September.
    4. Secchi, Piercesare, 2018. "On the role of statistics in the era of big data: A call for a debate," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 10-14.
    5. Giraldo, Ramón & Dabo-Niang, Sophie & Martínez, Sergio, 2018. "Statistical modeling of spatial big data: An approach from a functional data analysis perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 126-129.
    6. Rodolfo Metulini & Maurizio Carpita, 2021. "A Spatio-Temporal Indicator for City Users Based on Mobile Phone Signals and Administrative Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 761-781, August.
    7. Martha Bohorquez & Ramón Giraldo & Jorge Mateu, 2016. "Optimal sampling for spatial prediction of functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 39-54, March.
    8. Alfredo Cartone & Domenica Panzera, 2021. "Deprivation at local level: Practical problems and policy implications for the province of Milan," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 43-61, February.
    9. Ding, Liang & Huang, Ziqian & Xiao, Chaowei, 2023. "Are human activities consistent with planning? A big data evaluation of master plan implementation in Changchun," Land Use Policy, Elsevier, vol. 126(C).
    10. Pedro Delicado, 2015. "Discussion of “Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan” by Piercesare Secchi, Simone Vantini and Valeria Vitelli," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 329-333, July.
    11. Xinxin Guo & Benyong Wei & Gaozhong Nie & Guiwu Su, 2022. "Application of Mobile Signaling Data in Determining the Seismic Influence Field: A Case Study of the 2017 Mw 6.5 Jiuzhaigou Earthquake, China," IJERPH, MDPI, vol. 19(17), pages 1-23, August.
    12. Matthias Katzfuss & Joseph Guinness & Wenlong Gong & Daniel Zilber, 2020. "Vecchia Approximations of Gaussian-Process Predictions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 383-414, September.
    13. Ranadeep Daw & Christopher K. Wikle, 2023. "REDS: Random ensemble deep spatial prediction," Environmetrics, John Wiley & Sons, Ltd., vol. 34(1), February.
    14. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    15. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    16. Marcelo Cunha & Dani Gamerman & Montserrat Fuentes & Marina Paez, 2017. "A non-stationary spatial model for temperature interpolation applied to the state of Rio de Janeiro," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 919-939, November.
    17. Yasumasa Matsuda, 2014. "Wavelet Analysis Of Spatio-Temporal Data," TERG Discussion Papers 311, Graduate School of Economics and Management, Tohoku University.
    18. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    19. Marchetti, Yuliya & Nguyen, Hai & Braverman, Amy & Cressie, Noel, 2018. "Spatial data compression via adaptive dispersion clustering," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 138-153.
    20. Andrew Hoegh & Marco A. R. Ferreira & Scotland Leman, 2016. "Spatiotemporal model fusion: multiscale modelling of civil unrest," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 529-545, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stmapp:v:24:y:2015:i:2:p:315-319. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.