Comparar métodos

	Autocorrelación espacial C de Geary	Análisis de Puntos Calientes Getis-Ord Gi*	Modelo de Retardo Espacial (SAR / Autoregresivo Espacial)
Campo	Análisis espacial	Análisis espacial	Análisis espacial
Familia	Hypothesis test	Regression model	Regression model
Año de origen	1954	1992	1988
Autor original	Roy C. Geary	Arthur Getis and J. Keith Ord	Anselin (textbook formalisation); LeSage & Pace
Tipo	Global spatial autocorrelation statistic	Local spatial statistic	Spatial autoregressive regression
Fuente seminal	Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–146.	Getis, A. & Ord, J.K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Alias	Geary contiguity ratio, Geary's contiguity ratio, global spatial autocorrelation, Geary C mekânsal otokorelasyon	hot spot analysis, cold spot analysis, Gi* statistic, local Gi statistic	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Relacionados	2	4	5
Resumen	Geary's C is a global measure of spatial autocorrelation — whether nearby locations tend to have similar values — introduced by Roy Geary in 1954. Unlike Moran's I, which is built on the covariation of values around the mean, Geary's C is built on the squared differences between neighbouring values, making it more sensitive to local, short-range variation. Values below 1 indicate positive spatial autocorrelation (similar neighbours), near 1 indicate randomness, and above 1 indicate negative autocorrelation.	Getis-Ord Gi* is a local spatial statistic, introduced by Getis and Ord in 1992 and refined in 1995, that compares the value at each location and its neighbours against the global mean to identify statistically significant clusters of high values (hot spots) and low values (cold spots).	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.