The workshop will begin by illustrating the diversity of spatial data and the questions that these data need to address. Next, an introduction to spatial probability models will present the main features of such models in the context of applications ranging from the location of stars in the sky, to crop surveys, to maps of air pollution. The application of probability models to the analysis of spatial data then leads us to problems of spatial interpolation including kriging, areal averaging, spatial classification, and related topics. Next, a discussion of model-based error estimates provides a framework for problems of sampling design for spatial fields.

The workshop concludes with an introduction to multivariate spatial statistics where multiple attributes are measured at the same spatial locations, and temporal-spatial statistics where spatial fields are evolving in time.