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An enhanced dual IDW method for high-quality geospatial interpolation
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 geosp...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110750/ https://www.ncbi.nlm.nih.gov/pubmed/33972610 http://dx.doi.org/10.1038/s41598-021-89172-w |
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author | Li, Zhanglin |
author_facet | Li, Zhanglin |
author_sort | Li, Zhanglin |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8110750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81107502021-05-12 An enhanced dual IDW method for high-quality geospatial interpolation Li, Zhanglin Sci Rep Article 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. Nature Publishing Group UK 2021-05-10 /pmc/articles/PMC8110750/ /pubmed/33972610 http://dx.doi.org/10.1038/s41598-021-89172-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Li, Zhanglin An enhanced dual IDW method for high-quality geospatial interpolation |
title | 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_short | An enhanced dual IDW method for high-quality geospatial interpolation |
title_sort | enhanced dual idw method for high-quality geospatial interpolation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110750/ https://www.ncbi.nlm.nih.gov/pubmed/33972610 http://dx.doi.org/10.1038/s41598-021-89172-w |
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