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 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.

Predicting cell phone adoption metrics using machine learning and satellite imagery

“…Currently there is considerable research which uses cell phone call records or location data, obtained from MNOs, to metrics of interest, such as population density (Deville et al, 2014), urban growth (Bagan and Yamagata, 2015), cellular network anomalies (Sultan et al, 2018) and socio-economic characteristics (Fernando et al, 2018;Koebe, 2020;Schmid et al, 2017). However, the limitations of this approach relate to there being (i) no call data in areas with no coverage, and (ii) privacy issues associated with this type of data, affecting data sharing.…”