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| dc.contributor.author | Hoz Maestre, Javier Antonio de la | |
| dc.contributor.author | Mendes, Susana Luisa da Custodia Machado | |
| dc.contributor.author | Fernández Gómez, María José | |
| dc.date.accessioned | 2024-01-18T15:26:02Z | |
| dc.date.available | 2024-01-18T15:26:02Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Javier De La Hoz-M, Susana Mendes, María José Fernandez-Gómez, GeoWeightedModel : An R-Shiny package for Geographically Weighted Models, SoftwareX, Volume 20, 2022, 101250, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2022.101250. (https://www.sciencedirect.com/science/article/pii/S2352711022001686) | |
| dc.identifier.issn | 2352-7110 | |
| dc.identifier.uri | http://hdl.handle.net/10366/154398 | |
| dc.description.abstract | [EN]This paper describes GeoWeightedModel, a R package, which provides a graphical user friendly web application 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. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Geographically weighted | es_ES |
| dc.subject | Geographically weighted analysis | es_ES |
| dc.subject | Spatial heterogeneity | es_ES |
| dc.subject | Geographically weighted regression | es_ES |
| dc.subject | Geographically weighted principal component analysis | es_ES |
| dc.subject | Discriminant geographically weighted analysis | es_ES |
| dc.title | GeoWeightedModel : An R-Shiny package for Geographically Weighted Models | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.relation.publishversion | https://doi.org/10.1016/j.softx.2022.101250 | es_ES |
| dc.subject.unesco | 1209 Estadística | es_ES |
| dc.identifier.doi | 10.1016/j.softx.2022.101250 | |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
| dc.journal.title | SoftwareX | es_ES |
| dc.volume.number | 20 | es_ES |
| dc.page.initial | 101250 | es_ES |
| dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |










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