Hierarchical Spatial Models

Methods for spatial and spatio-temporal modeling are becoming increasingly important in environmental sciences and other sciences where data arise from a process in an inherent spatial setting. Technological advances in remote sensing, monitoring networks, and other methods of collecting spatial data in recent decades have revolutionized scientific endeavor in fields such as agriculture, climatology, ecology, economics, transportation, epidemiology and health management, as well as many other areas. However, such technological advancements require a parallel effort in the development of techniques that enable researchers to make rigorous statistical inference given the wealth of new information at hand. Unfortunately, the application of traditional covariance-based spatial statistical models is either inappropriate or computationally inefficient in many cases. Moreover, conventional methods are often incapable of allowing the researcher to quantify uncertainities corresponding to the model parameters since the parameter space of most complex spatial and spatiotemporal models is very large.