  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.
