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| Robust Geary's C× | Indicadors Locals Robustos d'Associació Espacial (Robust LISA)× | |
|---|---|---|
| Camp | Anàlisi espacial | Anàlisi espacial |
| Família | Regression model | Regression model |
| Any d'origen≠ | 1954 (base); robust variants: 1990s–2000s | 1995–2000s |
| Autor original≠ | Geary (1954); robust extensions by Anselin and spatial statisticians | Anselin (LISA, 1995); robust extensions by Assuncao & Reis and subsequent spatial statisticians |
| Tipus≠ | Robust spatial autocorrelation statistic | Local spatial autocorrelation statistic (robust variant) |
| Font seminal≠ | Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–145. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Àlies | robust Geary contiguity ratio, outlier-resistant Geary's C, robust spatial contiguity statistic, robust Geary C | Robust LISA, outlier-resistant LISA, robust local spatial autocorrelation, LISA with robust weights |
| Relacionats | 6 | 6 |
| Resum≠ | Robust Geary's C adapts the classical Geary contiguity ratio — a measure of spatial autocorrelation based on pairwise squared differences between neighbouring locations — to resist distortion by spatial outliers and influential observations. It retains the local sensitivity of Geary's C while producing more reliable inferences when the spatial data contain extreme values or non-normal distributions. | Robust Local Indicators of Spatial Association extend Anselin's LISA framework to handle outliers, extreme values, and spatially heterogeneous populations. By applying outlier-resistant adjustments to the spatial weights or the standardised values, Robust LISA identifies statistically significant local clusters and spatial outliers without the distortions caused by highly influential observations. |
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