Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Interpolação Espacial por Krigagem× | Regressão Geograficamente Ponderada Multiescala (MGWR)× | |
|---|---|---|
| Área | Análise espacial | Análise espacial |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1963 | 2017 |
| Autor original≠ | Georges Matheron (formalised geostatistics) | Fotheringham, Yang & Kang |
| Tipo≠ | Geostatistical spatial interpolation | Spatially varying coefficient regression |
| Fonte seminal≠ | Matheron, G. (1963). Principles of Geostatistics. Economic Geology, 58(8), 1246–1266. DOI ↗ | Fotheringham, A. S., Yang, W. & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. DOI ↗ |
| Outros nomes≠ | geostatistical interpolation, Gaussian process regression (geostatistics), ordinary kriging, Kriging (Mekânsal Enterpolasyon) | multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR) |
| Relacionados | 5 | 5 |
| Resumo≠ | Kriging is a geostatistical method that predicts the value of a continuous variable at unmeasured locations from nearby measurements, using the spatial correlation structure captured by a variogram. Formalised by Georges Matheron in 1963, it is the best linear unbiased predictor (BLUP) for spatial data and comes in Ordinary, Universal, and Co-Kriging forms. | Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally. |
| ScholarGateConjunto de dados ↗ |
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