Paper Info
Title
Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan
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.
Year
DOI
Venue
2015
10.1007/s10260-014-0294-3
Statistical Methods and Applications
Keywords
Field
DocType
Spatial statistics, Functional data analysis, Treelet analysis, Voronoi tessellation, Bagging, Erlang data
Spatial analysis,Functional data analysis,Data mining,Probability and statistics,Spatial dependence,Voronoi diagram,Mobile phone,Statistics,Metropolitan area,Urban area,Mathematics
Journal
Volume
Issue
ISSN
24
2
1613-981X
Citations 
PageRank 
References 
9
0.83
6
Authors
3
Name
Order
Citations
PageRank
Piercesare Secchi17011.12
Simone Vantini2609.26
Valeria Vitelli3726.93