Kriging in a Parallel Environment

In spatial data modelling and analysis there are a variety of techniques to perform prediction. The goal of these techniques is to take spatially located data and to establish estimates of data values at unknown locations. Of these techniques, the attractive aspects of kriging are often overshadowed by the slow speed of the calculation. Unfortunately the calculations necessary to perform kriging have a high computational complexity (i.e., for simple, ordinary and universal kriging). Even if algorithms requiring the theoretical minimum complexity become available, kriging will still be too slow to be an interactive process. Faster paradigms are required to advance the use of this technique in modern interactive data analysis. As a part of the Parallel and Distributed Geomatics Project the goal of this work is to provide insight into kriging.