A Bayesian time-varying random partition model for large spatio-temporal datasets

Music City Center 

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 change-points might occur within fixed time windows over the day. The model features a novel random partition prior that incorporates the desired spatial features and encourages co-clustering based on areal proximity. We discuss the application to the motivating data, where the main goal is to spatially cluster population patterns of mobile phone usage.

Bayesian Nonparametrics

Mobile data

Population density dynamics

Spatio-temporal clustering