Increasingly large volumes of space-time data are collected everywhere by mobile computing applications, and in many of these cases temporal data are obtained by registering events, for example telecommunication or web traffic data. Having both the spatial and temporal dimensions adds substantial complexity to data analysis and inference tasks. In this talk we discuss some hierarchical models to analyse these complex space-time data where Markov chain Monte Carlo techniques are used to make inference. We also discuss a novel method for tackling large data sets with application to daily maximum 8-hour average ground level ozone concentration data from 1997 to 2006 from a large study region in the eastern United States. Finally, we introduce a software package “spTimer” that can model space-time data and is currently available from the R archive network.