Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan
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.
KeywordsSpatial statistics Functional data analysis Treelet analysis Voronoi tessellation Bagging Erlang data
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.
- Banerjee S, Carlin B, Gelfand A (2004) Hierarchical modeling and analysis for spatial data. Monographs on statistics and applied probability. Chapman & Hall, LondonGoogle Scholar
- 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
- OECD (2006b) OECD Territorial reviews: Milan, Italy. OECD Publishing, ParisGoogle Scholar
- 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