Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Ripley K-funksjon× | Getis-Ord Gi* Hot Spot-analyse× | |
|---|---|---|
| Fagfelt | Romlig analyse | Romlig analyse |
| Familie≠ | Hypothesis test | Regression model |
| Opprinnelsesår≠ | 1977 | 1992 |
| Opphavsperson≠ | Brian Ripley | Arthur Getis and J. Keith Ord |
| Type≠ | Spatial point pattern test | Local spatial statistic |
| Opprinnelig kilde≠ | Ripley, B. D. (1977). Modelling spatial patterns. Journal of the Royal Statistical Society: Series B, 39(2), 172–212. DOI ↗ | Getis, A. & Ord, J.K. (1992). The Analysis of Spatial Association by Use of Distance Statistics. Geographical Analysis, 24(3), 189–206. DOI ↗ |
| Alias≠ | Ripley's K Function, Second-Order Intensity Function, K(d) Function, Ripley K Fonksiyonu | hot spot analysis, cold spot analysis, Gi* statistic, local Gi statistic |
| Relaterte≠ | 2 | 4 |
| Sammendrag≠ | 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. | 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). |
| ScholarGateDatasett ↗ |
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