An enhanced dual IDW method for high-quality geospatial interpolation

Abstract Many geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in...

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Autor principal:	Zhanglin Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f685d557e6414b0eb8cd748116d73d8d
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spelling	oai:doaj.org-article:f685d557e6414b0eb8cd748116d73d8d2021-12-02T16:57:26ZAn enhanced dual IDW method for high-quality geospatial interpolation10.1038/s41598-021-89172-w2045-2322https://doaj.org/article/f685d557e6414b0eb8cd748116d73d8d2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89172-whttps://doaj.org/toc/2045-2322Abstract Many geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.Zhanglin LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhanglin Li An enhanced dual IDW method for high-quality geospatial interpolation
description	Abstract Many geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.
format	article
author	Zhanglin Li
author_facet	Zhanglin Li
author_sort	Zhanglin Li
title	An enhanced dual IDW method for high-quality geospatial interpolation
title_short	An enhanced dual IDW method for high-quality geospatial interpolation
title_full	An enhanced dual IDW method for high-quality geospatial interpolation
title_fullStr	An enhanced dual IDW method for high-quality geospatial interpolation
title_full_unstemmed	An enhanced dual IDW method for high-quality geospatial interpolation
title_sort	enhanced dual idw method for high-quality geospatial interpolation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f685d557e6414b0eb8cd748116d73d8d
work_keys_str_mv	AT zhanglinli anenhanceddualidwmethodforhighqualitygeospatialinterpolation AT zhanglinli enhanceddualidwmethodforhighqualitygeospatialinterpolation
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An enhanced dual IDW method for high-quality geospatial interpolation

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