  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.
