A new local stochastic method for predicting data with spatial heterogeneity

Detalhes bibliográficos
Autor(a) principal: Silva, Anderson Rodrigo da
Data de Publicação: 2020
Outros Autores: Silva, Ana Paula Alencastro, Tiago Neto, Lauro Joaquim
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta Scientiarum. Agronomy (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947
Resumo: Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict spatial data, but their output is limited for graphical solutions (mapping). We adapted a computer-based prediction method called Circular Variable Radius Moving Window (CVRMW) that is based on two others: moving window kriging (MWK) and inverse squared-distance weighting (ISDW). The algorithm is developed to meet an objective function that minimizes the index of variation of the spatial observations inside the moving window. A code in R language is presented and thoroughly described. The outputs include the range of the spatial dependence as the radius calculated at every target location and the standard error of the predicted values, mapped to provide a useful tool for spatial exploratory analysis. The method does not make any assumptions about the spatial process, and it is an alternative for dealing with spatial heterogeneity.
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spelling A new local stochastic method for predicting data with spatial heterogeneityA new local stochastic method for predicting data with spatial heterogeneitymoving window kriging; spatial prediction; soil nematodes.moving window kriging; spatial prediction; soil nematodes.Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict spatial data, but their output is limited for graphical solutions (mapping). We adapted a computer-based prediction method called Circular Variable Radius Moving Window (CVRMW) that is based on two others: moving window kriging (MWK) and inverse squared-distance weighting (ISDW). The algorithm is developed to meet an objective function that minimizes the index of variation of the spatial observations inside the moving window. A code in R language is presented and thoroughly described. The outputs include the range of the spatial dependence as the radius calculated at every target location and the standard error of the predicted values, mapped to provide a useful tool for spatial exploratory analysis. The method does not make any assumptions about the spatial process, and it is an alternative for dealing with spatial heterogeneity.Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict spatial data, but their output is limited for graphical solutions (mapping). We adapted a computer-based prediction method called Circular Variable Radius Moving Window (CVRMW) that is based on two others: moving window kriging (MWK) and inverse squared-distance weighting (ISDW). The algorithm is developed to meet an objective function that minimizes the index of variation of the spatial observations inside the moving window. A code in R language is presented and thoroughly described. The outputs include the range of the spatial dependence as the radius calculated at every target location and the standard error of the predicted values, mapped to provide a useful tool for spatial exploratory analysis. The method does not make any assumptions about the spatial process, and it is an alternative for dealing with spatial heterogeneity.Universidade Estadual de Maringá2020-11-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/4994710.4025/actasciagron.v43i1.49947Acta Scientiarum. Agronomy; Vol 43 (2021): Publicação contínua; e49947Acta Scientiarum. Agronomy; v. 43 (2021): Publicação contínua; e499471807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947/751375151079Copyright (c) 2021 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessSilva, Anderson Rodrigo daSilva, Ana Paula AlencastroTiago Neto, Lauro Joaquim2021-07-27T17:52:14Zoai:periodicos.uem.br/ojs:article/49947Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2021-07-27T17:52:14Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv A new local stochastic method for predicting data with spatial heterogeneity
A new local stochastic method for predicting data with spatial heterogeneity
title A new local stochastic method for predicting data with spatial heterogeneity
spellingShingle A new local stochastic method for predicting data with spatial heterogeneity
Silva, Anderson Rodrigo da
moving window kriging; spatial prediction; soil nematodes.
moving window kriging; spatial prediction; soil nematodes.
title_short A new local stochastic method for predicting data with spatial heterogeneity
title_full A new local stochastic method for predicting data with spatial heterogeneity
title_fullStr A new local stochastic method for predicting data with spatial heterogeneity
title_full_unstemmed A new local stochastic method for predicting data with spatial heterogeneity
title_sort A new local stochastic method for predicting data with spatial heterogeneity
author Silva, Anderson Rodrigo da
author_facet Silva, Anderson Rodrigo da
Silva, Ana Paula Alencastro
Tiago Neto, Lauro Joaquim
author_role author
author2 Silva, Ana Paula Alencastro
Tiago Neto, Lauro Joaquim
author2_role author
author
dc.contributor.author.fl_str_mv Silva, Anderson Rodrigo da
Silva, Ana Paula Alencastro
Tiago Neto, Lauro Joaquim
dc.subject.por.fl_str_mv moving window kriging; spatial prediction; soil nematodes.
moving window kriging; spatial prediction; soil nematodes.
topic moving window kriging; spatial prediction; soil nematodes.
moving window kriging; spatial prediction; soil nematodes.
description Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict spatial data, but their output is limited for graphical solutions (mapping). We adapted a computer-based prediction method called Circular Variable Radius Moving Window (CVRMW) that is based on two others: moving window kriging (MWK) and inverse squared-distance weighting (ISDW). The algorithm is developed to meet an objective function that minimizes the index of variation of the spatial observations inside the moving window. A code in R language is presented and thoroughly described. The outputs include the range of the spatial dependence as the radius calculated at every target location and the standard error of the predicted values, mapped to provide a useful tool for spatial exploratory analysis. The method does not make any assumptions about the spatial process, and it is an alternative for dealing with spatial heterogeneity.
publishDate 2020
dc.date.none.fl_str_mv 2020-11-05
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947
10.4025/actasciagron.v43i1.49947
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947
identifier_str_mv 10.4025/actasciagron.v43i1.49947
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947/751375151079
dc.rights.driver.fl_str_mv Copyright (c) 2021 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual de Maringá
publisher.none.fl_str_mv Universidade Estadual de Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Agronomy; Vol 43 (2021): Publicação contínua; e49947
Acta Scientiarum. Agronomy; v. 43 (2021): Publicação contínua; e49947
1807-8621
1679-9275
reponame:Acta Scientiarum. Agronomy (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta Scientiarum. Agronomy (Online)
collection Acta Scientiarum. Agronomy (Online)
repository.name.fl_str_mv Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br
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