AI_GEOSTATS and the polygonal-method: a re-examination
H. Burger, F. Birkenhake
The main advantage of geostatistics is the fact that it allows the quantification of errors for any spatial estimate. This estimation error can be used for optimizing additional sampling efford, for classification of reserves or for analyzing the propagation of error during spatial GIS-operations. Geostatistical public domain software packages exist which offer standard and advanced kriging methods for local and global reserve estimation, but nothing comparable for the global estimation error. But confidence limits for the global mean are the basis for reserve classification: Along the horizontal axes of the well known McKelvey
Box reserves are ordered according to decreasing geologic assurance of existence whereas the other axis shows the economic feasibility of their recovery.
In this paper a proposal is made which combines ideas from the very beginning of geostatistics and objective methods for estimating sampling accuracy using the unique tessellation of a 2D-domain by Voronoi polygons. A Visual Basic program is presented which demonstrates the capability of this approach with regard to global reserve estimation and improved sampling design in environmental research.
Keywords: Voronoi polygons, extension error, reserve classification, global estimation error
BURGER, H. & BIRKENHAKE, F. (1994) :
AI_GEOSTATS and the polygonal method - a reexamination.- Proc 1994 Int. Assoc. Math. Geol. Ann. Conf. IAMG'94, Mont Tremblant, Qu?. Oct. 3-5, 1994, 50-55, Int. Assoc. Math. Geol., New York, NY