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

• URL: http://arxiv.org/abs/2002.11618v1
• Date: Thu, 20 Feb 2020 14:19:19 GMT
• Title: Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling
• Authors: Till Koebe
• Abstract summary: I use human settlement information extracted from publicly available satellite imagery in combination with radio propagation modelling techniques to account for that. I investigate in a simulation study and a real-world application on unemployment estimates in Senegal whether better coverage approximations lead to better outcome predictions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 investigate in a simulation study and a real-world application on unemployment estimates in Senegal whether better coverage approximations lead to better outcome predictions. The good news is: it does not have to be complicated.

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