Comparar métodos

	Robust Geary's C	Indicadores Locais Robustos de Associação Espacial (Robust LISA)
Área	Análise espacial	Análise espacial
Família	Regression model	Regression model
Ano de origem	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
Tipo	Robust spatial autocorrelation statistic	Local spatial autocorrelation statistic (robust variant)
Fonte seminal	Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–145.	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
Outros nomes	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
Relacionados	6	6
Resumo	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.