Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ripley K funkcija× | Geary C globālais telpiskās autokorelācijas mērs× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Hypothesis test | Hypothesis test |
| Izcelsmes gads≠ | 1977 | 1954 |
| Autors≠ | Brian Ripley | Roy C. Geary |
| Tips≠ | Spatial point pattern test | Global spatial autocorrelation statistic |
| Pirmavots≠ | Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B, 39(2), 172–212. DOI ↗ | Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5(3), 115–146. DOI ↗ |
| Citi nosaukumi | 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 |
| Saistītās | 2 | 2 |
| Kopsavilkums≠ | 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. |
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