Often data are collected in such a way that there is a space or time index attached to the observations, and this is often relevant to the analysis conducted. There are a number of researchers in the Department who are very active in the analysis of this kind of data. In spatial statistics, research interests of group members include methods for spatial smoothing and prediction, including geoadditive models and estimation of non-stationary spatial covariance structure. Analysis of image data is another active field of study, as is the analysis of spatial count data. In the area of time series modelling, the analysis of time series of counts is a particular focus of research, as well as the analysis of linear mixed models methodology. Markov chain Monte Carlo methods provide a fruitful approach to the computational difficulties which arise in inference for these kind of models, and there are a number of researchers in the Department with expertise in the development of Markov chain Monte Carlo algorithms.