Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Espacial Local× | Regressão Geograficamente Ponderada Multiescala (MGWR)× | |
|---|---|---|
| Área | Análise espacial | Análise espacial |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1996 | 2017 |
| Autor original≠ | Brunsdon, Fotheringham & Charlton | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tipo≠ | Spatially varying coefficient regression | Local spatial regression |
| Fonte seminal≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | 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 | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number. | Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply. |
| ScholarGateConjunto de dados ↗ |
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