Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Globālās karsto punktu analīzes (Getisa-Orda G statistika)× | Global Moran's I× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1992 | 1950 |
| Autors≠ | Arthur Getis and J. Keith Ord | Patrick Alfred Pierce Moran |
| Tips≠ | Global spatial concentration test | Global spatial autocorrelation test / index |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | Global G statistic, Getis-Ord G, global spatial clustering test, global concentration statistic | Moran's I, global spatial autocorrelation index, Moran index, GMI |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | Global Hot Spot Analysis uses the Getis-Ord G statistic to determine whether high or low attribute values are spatially concentrated across an entire study area. It answers one question: is there overall clustering of high values (a hot spot tendency) or low values (a cold spot tendency) in the dataset as a whole, producing a single summary test for the full region. | Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering). A significance test determines whether the observed pattern is stronger than random chance. |
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