Fast kriging of large data sets with Gaussian Markov random fields

Authors:
Linda Hartman;Ola Hössjer
Affiliations:
Centre for Mathematical Sciences, Lund University, Box 118, SE-221 00 Lund, Sweden;Department of Mathematics, Stockholm University, SE-106 91 Stockholm, Sweden
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

Spatial data sets are analysed in many scientific disciplines. Kriging, i.e. minimum mean squared error linear prediction, is probably the most widely used method of spatial prediction. Computation time and memory requirement can be an obstacle for kriging for data sets with many observations. Calculations are accelerated and memory requirements decreased by using a Gaussian Markov random field on a lattice as an approximation of a Gaussian field. The algorithms are well suited also for nonlattice data when exploiting a bilinear interpolation at nonlattice locations.