  Spatio-temporal areal data can be seen as a collection of time series which
are spatially correlated, according to a specific neighboring structure.
Motivated by a dataset on mobile phone usage in the Metropolitan area of Milan,
Italy, we propose a semi-parametric hierarchical Bayesian model allowing for
time-varying as well as spatial model-based clustering. To accommodate for
changing patterns over work hours and weekdays/weekends, we incorporate a
temporal change-point component that allows the specification of different
hierarchical structures across time points. The model features a random
partition prior that incorporates the desired spatial features and encourages
co-clustering based on areal proximity. We explore properties of the model by
way of extensive simulation studies from which we collect valuable information.
Finally, we discuss the application to the motivating data, where the main goal
is to spatially cluster population patterns of mobile phone usage.
