It is well established that house prices are dynamic. It is also axiomatic that location influences such selling prices, motivating our objective of incorporating spatial information in explaining the evolution of house prices over time. In this paper, we propose a rich class of spatio-temporal...

Understanding spatial patterns of species diversity and the distributions of individ-ual species is a consuming problem in biogeography and conservation. The Cape floristic region of South Africa is a global hot spot of diversity and endemism, and the Protea atlas project, with about 60 000...

Many models for the study of point-referenced data explicitly introduce spatial random effects to capture residual spatial association. These spatial effects are customarily modelled as a zero-mean stationary Gaussian process. The spatial Dirichlet process introduced by Gelfand et al. (2005)...

In many modern experimental settings, observations are obtained in the form of functions and interest focuses on inferences about a collection of such functions. We propose a hierarchical model that allows us simultaneously to estimate multiple curves nonparametrically by using dependent...

Advances in Geographical Information Systems (GIS) and Global Positioning Systems (GPS) enable accurate geocoding of locations where scientific data are collected. This has encouraged collection of large spatial datasets in many fields and has generated considerable interest in statistical...

With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial...