Scale is a fundamental geographic concept, and a substantial literature exists discussing the
various roles that scale plays in different geographical contexts. Relatively little work exists …

Modeling spatial and spatio-temporal continuous processes is an important and challenging
problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential …

Among the many uses of hierarchical modeling, their application to the statistical analysis of
spatial and spatio-temporal data from areas such as epidemiology And environmental …

This fully revised third edition introduces geostatistics by emphasising the multivariate
aspects for scientists, engineers and statisticians. Geostatistics offers a variety of models …

In the fields of medicine and public health, a common application of areal data models is the
study of geographical patterns of disease. When we have several measurements recorded …

Spatial data collection increasingly turns to vector valued measurements at spatial locations.
An example is the observation of pollutant measurements. Typically, several different …

With the ready availability of spatial databases and geographical information system
software, statisticians are increasingly encountering multivariate modelling settings featuring …

We propose a Bayesian dynamic factor process convolution model for multivariate spatial
temporal processes and illustrate the utility of this approach in modeling large air quality …

We present a comprehensive review of multivariate geostatistical models, focusing on the
bivariate case. We compare in detail three approaches, the linear model of …

We propose a method for detecting significant interactions in very large multivariate spatial
point patterns. This methodology thus develops high dimensional data understanding in the …