Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Espacial Local× | Modelo de Lag Espacial Local× | |
|---|---|---|
| Área | Análise espacial | Análise espacial |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1996 | 1988 (global); 2000s (local extensions) |
| Autor original≠ | Brunsdon, Fotheringham & Charlton | Anselin (global SLM, 1988); local extension via Fotheringham, Brunsdon & Charlton (GWR framework, 2002) |
| Tipo≠ | Spatially varying coefficient regression | Spatially varying regression model |
| Fonte seminal≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737215 |
| Outros nomes | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | local SLM, geographically weighted spatial lag model, GW-SLM, spatially varying lag model |
| 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. | The Local Spatial Lag Model extends the classical spatial lag model by allowing both the spatial autocorrelation parameter and the regression coefficients to vary across geographic locations. Instead of one global estimate of how neighboring outcomes influence each observation, the model fits location-specific parameters using kernel-weighted local estimation, revealing spatial heterogeneity in spatial dependence. |
| ScholarGateConjunto de dados ↗ |
|
|