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Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling

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  • Till Koebe

Abstract

Mobile sensing data has become a popular data source for geo-spatial analysis, however, mapping it accurately to other sources of information such as statistical data remains a challenge. Popular mapping approaches such as point allocation or voronoi tessellation provide only crude approximations of the mobile network coverage as they do not consider holes, overlaps and within-cell heterogeneity. More elaborate mapping schemes often require additional proprietary data operators are highly reluctant to share. In this paper, I use human settlement information extracted from publicly available satellite imagery in combination with stochastic radio propagation modelling techniques to account for that. I show in a simulation study and a real-world application on unemployment estimates in Senegal that better coverage approximations do not necessarily lead to better outcome predictions.

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  • Till Koebe, 2020. "Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-28, November.
    • Handle: RePEc:plo:pone00:0241981
    DOI: 10.1371/journal.pone.0241981
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    References listed on IDEAS

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    1. Joshua Blumenstock & Michael Callen & Tarek Ghani, 2018. "Why Do Defaults Affect Behavior? Experimental Evidence from Afghanistan," American Economic Review, American Economic Association, vol. 108(10), pages 2868-2901, October.
    2. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    3. Timo Schmid & Fabian Bruckschen & Nicola Salvati & Till Zbiranski, 2017. "Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1163-1190, October.
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    Cited by:

    1. D. Woods & A. Cunningham & C. E. Utazi & M. Bondarenko & L. Shengjie & G. E. Rogers & P. Koper & C. W. Ruktanonchai & E. zu Erbach-Schoenberg & A. J. Tatem & J. Steele & A. Sorichetta, 2022. "Exploring methods for mapping seasonal population changes using mobile phone data," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-17, December.
    2. Till Koebe & Alejandra Arias-Salazar & Timo Schmid, 2023. "Releasing survey microdata with exact cluster locations and additional privacy safeguards," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    3. Till Koebe & Alejandra Arias‐Salazar & Natalia Rojas‐Perilla & Timo Schmid, 2022. "Intercensal updating using structure‐preserving methods and satellite imagery," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 170-196, December.
    4. Edward J. Oughton & Jatin Mathur, 2020. "Predicting cell phone adoption metrics using satellite imagery," Papers 2006.07311, arXiv.org, revised Jun 2021.

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