Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Regresión Espacial Local× | Modelo de Durbin Espacial (SDM)× | |
|---|---|---|
| Campo | Análisis espacial | Análisis espacial |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1996 | 2009 |
| Autor original≠ | Brunsdon, Fotheringham & Charlton | LeSage & Pace |
| Tipo≠ | Spatially varying coefficient regression | Spatial regression model |
| Fuente seminal≠ | Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168 | LeSage, J. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press. DOI ↗ |
| Alias≠ | locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression | SDM, spatial mixed model, uzamsal durbin modeli |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | 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 Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special cases. |
| ScholarGateConjunto de datos ↗ |
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