Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Geograficamente Ponderada Multiescala Bayesiana× | Modelo de Lag Espacial (SAR / Autoregressivo Espacial)× | |
|---|---|---|
| Área | Análise espacial | Análise espacial |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2017-2020 | 1988 |
| Autor original≠ | Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors | Anselin (textbook formalisation); LeSage & Pace |
| Tipo≠ | Spatially varying coefficient regression | Spatial autoregressive regression |
| Fonte seminal≠ | 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 ↗ | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic. DOI ↗ |
| Outros nomes | Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model | SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag) |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface. | The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts. |
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