ABSTRACT
Exploratory Data Analysis (EDA) is not only useful in the pre-interpolation phase, but it is also helpful for validating the results. The quality of kriging predictions is largely determined by the choice of variogram model, which can be seriously affected by a few extreme attribute values if these are closely located in space. The use of EDA for detecting spatial anomalies that can affect geostatistical interpolation is demonstrated using data on polluted floodplain soils in The Netherlands. The interpolation of patterns of nutrients or pollutants in soil and sediments is an application of increasing importance in geographic information systems. Geostatistical methods of interpolation are designed to overcome the problems of dealing with errors of interpolation. The form of the model variogram is critical for the quality of the interpolation and its shape near the origin is often sensitive to the presence of large and small data values in close spatial proximity.
