A Simple Solution for the Inverse Distance Weighting Interpolation (IDW) Clustering Problem
Year: 2025
Type: article
Abstract: 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.
Source: Sci
DOI: 10.3390/sci7010030
Author: Nir Benmoshe
Institution: Israel Institute for Biological Research
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,448
OpenAlex ID: W4408198372