A Bayesian approach to location estimation of mobile devices from mobile network operator data

TL;DR

This paper introduces a Bayesian framework for estimating mobile device locations from network data, incorporating prior knowledge and signal models, improving upon the traditional Voronoi tessellation approach.

Contribution

It develops a modular Bayesian method that integrates various prior modules and signal strength models for more accurate location estimation from mobile network data.

Findings

Bayesian approach outperforms Voronoi tessellation in location accuracy

Incorporating land use as a prior improves estimates

Signal strength models enhance localization precision

Abstract

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.