KriSp: a Package for Interpolation of Large Datasets Using Covariance Tapering

Reinhard Furrer, GSP post doc

Interpolation of a spatially correlated random process is used in many areas. The best unbiased linear predictor, often called kriging in geostatistical science, requires the solution of a large linear system based on the covariance matrix of the observations. Tapering the correct covariance matrix with an appropriate compactly supported covariance function reduces the computational burden significantly and still has an asymptotic optimal mean squared error. The effect of tapering is to create a sparse approximate linear system that can then be solved using sparse matrix algorithms. Further, the manageable size of the observed and predicted fields can be far bigger than with classical approaches. The net result is the ability to analyze spatial data sets that are several orders of magnitude larger than past work in a high level interactive environment such as R. The talk summarizes briefly the theoretical background and then presents the R package 'KriSp' which supports the taper approach.