For the most current development version please refer to the following repo: https://github.com/eurostat/MNO_mobdensity
Title: On the estimation of spatial density from mobile network operator data
Authors (Article): Fabio Ricciato and Angelo Coluccia
Authors (Code Repository): Marco Ramljak, Fabio Ricciatio and Angelo Coluccia
Abstract: We tackle the problem of estimating the spatial distribution of mobile phones from Mobile Network Operator (MNO) data, namely CDR or signalling data. The process of transforming MNO data to a density map requires to perform geolocation of radio cells to determine their spatial footprint. Traditional geolocation solutions rely on Voronoi tessellations and approximate cell footprints by mutually disjoint regions. Recently, some pioneering work started to consider more elaborated geolocation methods with partially overlapping (non-disjont) cell footprints coupled with a probabilistic model for phone-to-cell association. Estimating the spatial density in such a probabilistic setup is currently an open research problem and is the focus of the present work. We start by reviewing three different estimation methods proposed in literature and provide novel analytical insights that unveil some key aspects of their mutual relationships and properties. Furthermore, we develop a novel estimation approach for which a closed-form solution can be given. Numerical results based on semi-synthetic data are presented to assess the relative accuracy of each method. Our results indicate that the estimators based on overlapping cells have the potential to improve spatial accuracy over traditional approaches based on Voronoi tessellations.
Journal: ... (under review)
Full article (Preprint): https://arxiv.org/pdf/2009.05410.pdf
Supplementary Material and code for reproduction: https://r-ramljak.github.io/MNO_mobdensity/
Selected ressources:
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mobloc: This R-package is used to model cell footprints [1].
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SpatialKWD: This R-package is used to approximate the Kantorovich-Wasserstein distances, comparing the spatial density estimations with the ground truth spatial density [2].
Note: Large code blocks and custom functions are under further development resulting in an R-package this summer. Further development can be followed here: https://github.com/R-ramljak/MNOanalyze
| authors | Ramljak M., Ricciato F., Coluccia, A. |
| version | 1.0 |
| status | 2021 - closed |
| license | EUPL (concerning the source code, please cite this repository) |
References:
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[1] Tennekes M. (2017): R package for mobile location algorithms and tools.
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Bassetti F., Gualandi S., Veneroni M. On the computation of Kantorovich-Wasserstein distances between 2D-histograms by uncapacitated minimum cost flows. SIAM J. Optim., 30(3), 2441–2469, 2020. Preprint on arXiv: 1804.00445.