Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Ponderação por Distância Inversa (IDW)× | Regressão Geograficamente Ponderada (GWR)× | Krigagem Universal (Krigagem com Tendência)× | |
|---|---|---|---|
| Área | Análise espacial | Análise espacial | Análise espacial |
| Família | Regression model | Regression model | Regression model |
| Ano de origem≠ | 1968 | 2002 | 1969 |
| Autor original≠ | Donald Shepard | Fotheringham, Brunsdon & Charlton | Georges Matheron |
| Tipo≠ | Deterministic spatial interpolation | Local spatial regression | Geostatistical interpolation with spatial trend |
| Fonte seminal≠ | Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 23rd ACM National Conference, 517–524. DOI ↗ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ |
| Outros nomes | IDW, inverse distance interpolation, Shepard's method, ters mesafe ağırlıklı enterpolasyon | GWR, local regression, spatially varying coefficient regression, Coğrafi Ağırlıklı Regresyon (GWR) | kriging with a trend, kriging with drift, trend kriging, evrensel kriging |
| Relacionados≠ | 3 | 5 | 3 |
| Resumo≠ | Inverse distance weighting is a simple, deterministic method for estimating values at unsampled locations by taking a weighted average of nearby measured points, where closer points carry more weight. Introduced by Donald Shepard in 1968, it embodies the first law of geography — near things are more related than distant things — and is one of the most widely used interpolation methods in GIS for mapping continuous fields such as rainfall, elevation, or pollution from scattered samples. | Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relationships. | Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling with latitude or a pollutant gradient, while still returning prediction variances. |
| ScholarGateConjunto de dados ↗ |
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