GeoWeightedModel: An R-Shiny package for geographically weighted models
| Main Author: | |
|---|---|
| Publication Date: | 2022 |
| Other Authors: | , |
| Format: | Article |
| Language: | eng |
| Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Download full: | http://hdl.handle.net/10400.8/8723 |
Summary: | This paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web pplication to perform techniques from a subarea of spatial Statistics known as Geographically Weighted (GW) models, such as Geographically Weighted Regression (GWR) and its extensions: Robust GWR, Generalized GWR, Heteroskedastic GWR, Mixed GWR, and ‘‘Scalable GWR), Geographically Weighted Principal Component Analysis, and Geographically Weighted Discriminant analysis. It also allows calculating a basic and robust Geographically weighted summary. The main goal of GeoWeightedModel package was to make the workflow easier to use, especially for those who are not familiar with the R environment. With GeoWeightedModel, analyses can be performed interactively (point-and-click way) in a web browser, making the applications easier for many more researchers. In addition with this tool, the results of the analyses can be mapped providing a valuable tool for exploring the spatial heterogeneity of the data. |
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GeoWeightedModel: An R-Shiny package for geographically weighted modelsGeographically weightedGeographically weighted analysisSpatial heterogeneityGeographically weighted regressionGeographically weighted principal component analysisDiscriminant geographically weighted analysisThis paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web pplication to perform techniques from a subarea of spatial Statistics known as Geographically Weighted (GW) models, such as Geographically Weighted Regression (GWR) and its extensions: Robust GWR, Generalized GWR, Heteroskedastic GWR, Mixed GWR, and ‘‘Scalable GWR), Geographically Weighted Principal Component Analysis, and Geographically Weighted Discriminant analysis. It also allows calculating a basic and robust Geographically weighted summary. The main goal of GeoWeightedModel package was to make the workflow easier to use, especially for those who are not familiar with the R environment. With GeoWeightedModel, analyses can be performed interactively (point-and-click way) in a web browser, making the applications easier for many more researchers. In addition with this tool, the results of the analyses can be mapped providing a valuable tool for exploring the spatial heterogeneity of the data.ElsevierRepositório IC-OnlineDe la Hoz-M, JavierMendes, SusanaFernandez-Gómez, María José2023-08-23T10:29:40Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.8/8723eng2352-711010.1016/j.softx.2022.101250info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-25T15:19:58Zoai:iconline.ipleiria.pt:10400.8/8723Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:58:48.979032Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| title |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| spellingShingle |
GeoWeightedModel: An R-Shiny package for geographically weighted models De la Hoz-M, Javier Geographically weighted Geographically weighted analysis Spatial heterogeneity Geographically weighted regression Geographically weighted principal component analysis Discriminant geographically weighted analysis |
| title_short |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| title_full |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| title_fullStr |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| title_full_unstemmed |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| title_sort |
GeoWeightedModel: An R-Shiny package for geographically weighted models |
| author |
De la Hoz-M, Javier |
| author_facet |
De la Hoz-M, Javier Mendes, Susana Fernandez-Gómez, María José |
| author_role |
author |
| author2 |
Mendes, Susana Fernandez-Gómez, María José |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
Repositório IC-Online |
| dc.contributor.author.fl_str_mv |
De la Hoz-M, Javier Mendes, Susana Fernandez-Gómez, María José |
| dc.subject.por.fl_str_mv |
Geographically weighted Geographically weighted analysis Spatial heterogeneity Geographically weighted regression Geographically weighted principal component analysis Discriminant geographically weighted analysis |
| topic |
Geographically weighted Geographically weighted analysis Spatial heterogeneity Geographically weighted regression Geographically weighted principal component analysis Discriminant geographically weighted analysis |
| description |
This paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web pplication to perform techniques from a subarea of spatial Statistics known as Geographically Weighted (GW) models, such as Geographically Weighted Regression (GWR) and its extensions: Robust GWR, Generalized GWR, Heteroskedastic GWR, Mixed GWR, and ‘‘Scalable GWR), Geographically Weighted Principal Component Analysis, and Geographically Weighted Discriminant analysis. It also allows calculating a basic and robust Geographically weighted summary. The main goal of GeoWeightedModel package was to make the workflow easier to use, especially for those who are not familiar with the R environment. With GeoWeightedModel, analyses can be performed interactively (point-and-click way) in a web browser, making the applications easier for many more researchers. In addition with this tool, the results of the analyses can be mapped providing a valuable tool for exploring the spatial heterogeneity of the data. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-08-23T10:29:40Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
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publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.8/8723 |
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http://hdl.handle.net/10400.8/8723 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
2352-7110 10.1016/j.softx.2022.101250 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Elsevier |
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Elsevier |
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