Optimizing Spatial Declustering Weights – Comparison of Methods

Analysis of a spatial phenomenon is to a great extent affected by the frequent irregular structures and/or the preferential clustering of the sampling schemes. To obtain representative statistics for an area of interest, the influence of clustered measurements needs to be reduced by attributing them lower weights. In this case study, two standard methods, the polygonal and the cell-declustering methods, are confronted with the recently proposed Coefficient of Representativity. The last combines Thiessen polygons with nearest neighbours distances and is further coupled with information provided by the borders of the region of interest. The relative performance of these methods is assessed through an estimation problem of the average elevation of several subsets of a Digital Elevation Model of Switzerland. The results are showing for this case study that the Coefficient of Representativity generated overall better results when confronted to the other methods.