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


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.

SUBMITTER: Koebe T 

PROVIDER: S-EPMC7652289 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Koebe Till T  

PloS one 20201109 11


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 reluct  ...[more]

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