2020/02/20 by Till Koebe · 1 citation
Computer Science · Environmental Science · Mathematics · Social Sciences · #Computer science #Data mining #Data science #Geography #Human Mobility and Location-Based Analysis #Impact of Light on Environment and Health #Land Use and Ecosystem Services #Mathematics #Remote sensing #Satellite #Satellite imagery #Spatial analysis #Voronoi diagram #cs.CY #stat.CO #stat.ME
paper · pdf · doi:10.1371/journal.pone.0241981
arxiv created 2020/02/20 · openalex publication_date 2020/11/09 · arxiv updated 2020/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.