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

Till Koebe

PLOS ONE, 2020, vol. 15, issue 11, 1-28

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0241981

DOI: 10.1371/journal.pone.0241981

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