方法对比

	Ripley K 函数	Geary's C空间自相关	Getis-Ord Gi* 热点分析
领域	空间分析	空间分析	空间分析
方法族	Hypothesis test	Hypothesis test	Regression model
起源年份	1977	1954	1992
提出者	Brian Ripley	Roy C. Geary	Arthur Getis and J. Keith Ord
类型	Spatial point pattern test	Global spatial autocorrelation statistic	Local spatial statistic
开创性文献	Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B, 39(2), 172–212.	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.
别名	Ripley's K Function, Second-Order Intensity Function, K(d) Function, Ripley K Fonksiyonu	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
相关	2	2	4
摘要	The Ripley K function, introduced by Brian Ripley in 1977, is a second-order summary statistic for spatial point patterns. It measures how the number of points within a given distance d of a typical point compares to what would be expected under complete spatial randomness (CSR). Widely used in ecology, epidemiology, criminology, and geography, the K function reveals whether events cluster, disperse, or distribute randomly across a study area at multiple spatial scales simultaneously.	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).