论文标题
更好的覆盖范围，更好的结果？使用卫星图像和无线电传播建模将移动网络数据映射到官方统计数据
Better coverage, better outcomes? Mapping mobile network data to official statistics using satellite imagery and radio propagation modelling
论文作者
论文摘要
移动传感数据已成为地理空间分析的流行数据源，但是将其准确地映射到其他信息来源（例如统计数据）仍然是一个挑战。流行的映射方法，例如点分配或Voronoi Tessellation，仅提供移动网络覆盖的粗略近似值，因为它们不考虑孔，重叠和细胞内异质性。更精细的映射方案通常需要其他专有数据运营商高度不愿共享。在本文中，我使用从公开可用的卫星图像中提取的人类定居信息，并结合随机无线电传播建模技术来说明这一点。我在一项仿真研究中调查了塞内加尔的失业估计的现实应用，是否可以更好地覆盖近似值，是否会带来更好的结果预测。好消息是：它不必很复杂。
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.
