Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Kuuma ala analüüs (Getis-Ord Gi*)× | Morani I – globaalne ruumilise autokorrelatsiooni indeks× | |
|---|---|---|
| Valdkond | Ruumianalüüs | Ruumianalüüs |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1992 | 1950 |
| Looja≠ | Arthur Getis and J. Keith Ord | Patrick A. P. Moran |
| Tüüp≠ | Local spatial statistic | Spatial autocorrelation statistic |
| Algallikas≠ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Rööpnimetused | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA | Moran's I statistic, global Moran's I, spatial autocorrelation index, Moran index |
| Seotud≠ | 5 | 6 |
| Kokkuvõte≠ | Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation. | Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number. |
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