A Simple Solution for the Inverse Distance Weighting Interpolation (IDW) Clustering Problem

Inverse Distance Weighting (IDW) is a common method for spatial interpolation. Still, its accuracy decreases when there is a cluster of measurement stations or when some measuring stations are hidden behind others. This paper introduces Clusters Unifying Through Hiding Interpolation (CUTHI), a simple approach to enhance IDW accuracy. CUTHI calculates a weight for each station that considers its visibility from the interpolation point, reducing the influence of clustered or hidden stations. The method is tested in three cases: elevation data, rainfall measurements, and a mathematical function. Results demonstrate that CUTHI consistently outperforms traditional IDW, especially in areas with clustered measurement stations. CUTHI also treats the bull’s eye problem. This improved accuracy makes CUTHI a valuable tool for various applications requiring precise spatial interpolation.