Examination of the influence of data aggregation and sampling density on spatial estimation

Abstract

Spatial processes may be sampled by point sampling or by aggregate sampling. If aggregate samples are collected over a regular grid and used to represent the central point of each aggregation area, the aggregate sampling functions as a low-pass filter and may eliminate aliasing during spatial estimation. To assess potential accuracy improvements, a numerical procedure for calculating the estimation error variance was developed. Analysis of point and block sampling techniques for kriging and inverse distance interpolation showed that for the same sampling density, block sampling provides better estimation. To achieve the same error levels, over 30%-50% more point samples were required than block samples. Furthermore, interpolation of block sampled data resulted in lower error variability and surfaces with more visual appeal.