A novel formulation for inverse distance weighting from weighted linear regression

Abstract

Inverse Distance Weighting (IDW) is a widely adopted interpolation algorithm. This work presents a novel formulation for IDW which is derived from a weighted linear regression. The novel method is evaluated over study cases related to elevation data, climate and also on synthetic data. Relevant aspects of IDW are preserved while the novel algorithm achieves better results with statistical significance. Artifacts are alleviated in interpolated surfaces generated by the novel approach when compared to the respective surfaces from IDW.