Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Kuuma ala analüüs (Getis-Ord Gi*)× | Ruumiiline autokorrelatsioon× | |
|---|---|---|
| Valdkond | Ruumianalüüs | Ruumianalüüs |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1992 | 1950 |
| Looja≠ | Arthur Getis and J. Keith Ord | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Tüüp≠ | Local spatial statistic | Spatial statistic / exploratory spatial data analysis |
| 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 | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Seotud | 5 | 5 |
| 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. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
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