A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression

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 aspe...

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Detalles Bibliográficos
Autores principales: Emmendorfer, Leonardo Ramos, Dimuro, Graçaliz Pereira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302837/
http://dx.doi.org/10.1007/978-3-030-50417-5_43
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author Emmendorfer, Leonardo Ramos
Dimuro, Graçaliz Pereira
author_facet Emmendorfer, Leonardo Ramos
Dimuro, Graçaliz Pereira
author_sort Emmendorfer, Leonardo Ramos
collection PubMed
description 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.
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spelling pubmed-73028372020-06-19 A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression Emmendorfer, Leonardo Ramos Dimuro, Graçaliz Pereira Computational Science – ICCS 2020 Article 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. 2020-06-15 /pmc/articles/PMC7302837/ http://dx.doi.org/10.1007/978-3-030-50417-5_43 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Emmendorfer, Leonardo Ramos
Dimuro, Graçaliz Pereira
A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
title A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
title_full A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
title_fullStr A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
title_full_unstemmed A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
title_short A Novel Formulation for Inverse Distance Weighting from Weighted Linear Regression
title_sort novel formulation for inverse distance weighting from weighted linear regression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302837/
http://dx.doi.org/10.1007/978-3-030-50417-5_43
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AT dimurogracalizpereira novelformulationforinversedistanceweightingfromweightedlinearregression

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