Advanced spatial statistics for analysing and visualizing geo-referenced data

Abstract

Spatial statistics supplies advanced methods for analysing environmental data, and copes with observational interdependencies similar to the way principal components analysis treats multicollinearity. The U.S. Environmental Protection Agency’s Environmental Monitoring and Assessment Program (EMAP) utilizes kriging from geostatistics for mapping and visualizing environmental data. A conceptual framework is articulated between the interpolation problem in kriging and the missing data problem in spatial statistics, with special reference to relations between the exponential semi-variogram and the conditional autoregressive models. Supercomputing experiments are summarized that simplify numerically the probability density function normalizing factor, which is of particular relevance to estimation tasks for the EMAP project.