Department of Economics, Freie Universität, Berlin, Germany.
PLoS One. 2020 Nov 9;15(11):e0241981. doi: 10.1371/journal.pone.0241981. eCollection 2020.
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.
移动感应数据已成为地理空间分析的热门数据源，但将其准确映射到统计数据等其他信息源仍然是一个挑战。流行的映射方法，如点分配或 Voronoi 镶嵌，仅提供移动网络覆盖的粗略近似，因为它们不考虑孔、重叠和小区内异质性。更精细的映射方案通常需要额外的专有数据，而运营商非常不愿意共享。在本文中，我使用从公开卫星图像中提取的人类住区信息，并结合随机无线电传播建模技术来解决这个问题。我在塞内加尔的失业估计模拟研究和实际应用中表明，更好的覆盖近似不一定会导致更好的结果预测。
