Mobile network operator (MNO) data are a rich data source for official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use the Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell. This paper uses a modular Bayesian approach, allowing for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. We show that the Voronoi tessellation can be used as a likelihood module. Alternatively, we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate for the geographic location, which can be used for further statistical inference. We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.

翻译：移动网络操作员(MNO)数据是官方统计的丰富数据来源,如目前的人口、流动性、移徙和旅游。估计移动设备的地理位置是统计推理的一个必要步骤。大多数研究为此使用Voronoi 脉冲模型,该模型所依据的假设是移动设备总是与最近的无线电电池连接。本文采用模块式贝叶斯式方法,允许对装置的预期位置有不同的先前知识模块,以及根据地理位置进行连接可能性的不同模块。我们讨论和比较以前几个模块的使用情况,包括基于土地使用的模块。我们显示Voronoi 脉冲可用作可能性模块。或者,我们提出使用无线电细胞特性的信号强度模型,如天线高度、传播方向和功率。使用Bayes规则,我们得出一个外缘概率分布,这是对地理位置的估计,可用于进一步的统计推论。我们描述了这种方法,并提供了类似于现实世界状况的虚构示例。我们简单介绍了在R组合中采用的这一方法。