Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Autocorrelación espacial robusta× | Índice I de Moran× | |
|---|---|---|
| Campo | Análisis espacial | Análisis espacial |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1981–1995 | 1950 |
| Autor original≠ | Cliff & Ord; extended by Anselin and colleagues | Patrick A. P. Moran |
| Tipo≠ | Spatial dependence test (robust variant) | Spatial autocorrelation statistic |
| Fuente seminal≠ | Anselin, L., & Florax, R. J. G. M. (1995). Small sample properties of tests for spatial dependence in regression models: some further results. In Anselin, L. & Florax, R. J. G. M. (Eds.), New Directions in Spatial Econometrics. Springer, Berlin. link ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Alias | robust Moran's I, robust spatial dependence test, outlier-resistant spatial autocorrelation, RSA | Moran's I statistic, global Moran's I, spatial autocorrelation index, Moran index |
| Relacionados≠ | 5 | 6 |
| Resumen≠ | Robust spatial autocorrelation methods measure the degree to which nearby geographic units share similar values, while explicitly controlling for the distorting influence of spatial outliers and extreme observations. They extend classical statistics such as Moran's I by down-weighting or trimming observations that would otherwise inflate or deflate the autocorrelation signal. | Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number. |
| ScholarGateConjunto de datos ↗ |
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