OPTIMIZING INVERSE DISTANCE WEIGHTED INTERPOLATION WITH CROSS-VALIDATION.

Inverse distance weighted interpolation can be easily optimized with commercially available software by selecting distance exponent values that minimize cross-validation (V[CROSS]) errors of prediction. The effectiveness of this approach has not been critically evaluated but is of concern because of known limitations of the V[CROSS] procedure. To evaluate this optimization procedure, it should be validated with an independent data set (V[IDS]). The objectives of this study were (1) to develop and test an optimization procedure based on V[CROSS] and V[IDS] analyses, (2) to test the accuracy of optimization of the V[CROSS] procedure, and (3) to evaluate the quality of maps created with V[CROSS]. Soil fertility and bulk soil electrical conductivity data from two previously published studies were used for the analyses. These included prediction and validation data sets for multiple locations. Our optimization procedure compared well with those obtained with a commercially available software program. The use of V[CROSS] resulted in overprediction of optimal distance exponent values and a substantial reduction in map quality. In many cases, the maps produced with V[CROSS] optimization were blocky and unrealistic. The V[CROSS] procedure should not be used to optimize distance exponent values for data collected on regular grids or along transects. Instead, distance exponent values between 1.5 and 2.0 should be used. Software developers should consider creating a V[IDS] optimizing procedure.