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

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  • Piercesare Secchi
  • Simone Vantini
  • Valeria Vitelli

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:24:y:2015:i:2:p:279-300
    DOI: 10.1007/s10260-014-0294-3
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    References listed on IDEAS

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    1. Sangalli, Laura M. & Secchi, Piercesare & Vantini, Simone & Veneziani, Alessandro, 2009. "A Case Study in Exploratory Functional Data Analysis: Geometrical Features of the Internal Carotid Artery," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 37-48.
    2. Kaziska, David & Srivastava, Anuj, 2007. "Gait-Based Human Recognition by Classification of Cyclostationary Processes on Nonlinear Shape Manifolds," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1114-1124, December.
    3. Ke C. & Wang Y., 2001. "Semiparametric Nonlinear Mixed-Effects Models and Their Applications," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1272-1298, December.
    4. Sangalli, Laura M. & Secchi, Piercesare & Vantini, Simone & Vitelli, Valeria, 2010. "k-mean alignment for curve clustering," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1219-1233, May.
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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Yan Huang & Wei Lang & Tingting Chen & Jiemin Wu, 2023. "Regional Coordinated Development in the Megacity Regions: Spatial Pattern and Driving Forces of the Guangzhou-Foshan Cross-Border Area in China," Land, MDPI, vol. 12(4), pages 1-27, March.
    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. Selene Perazzini & Rodolfo Metulini & Maurizio Carpita, 2024. "Statistical indicators based on mobile phone and street maps data for risk management in small urban areas," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1051-1078, September.
    12. 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.
    13. 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.
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