Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Global Moran's I× | Lokālā Getis-Ord Gi* (Karsto punktu analīze)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1950 | 1992–1995 |
| Autors≠ | Patrick Alfred Pierce Moran | Arthur Getis and J. Keith Ord |
| Tips≠ | Global spatial autocorrelation test / index | Local spatial association statistic |
| Pirmavots≠ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206. DOI ↗ |
| Citi nosaukumi | Moran's I, global spatial autocorrelation index, Moran index, GMI | Gi* statistic, Getis-Ord Gi*, local G-star, hot spot statistic |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | The Local Getis-Ord Gi* statistic identifies statistically significant spatial clusters of high values (hot spots) and low values (cold spots) within a study area. Unlike global measures, it produces a z-score for every location, revealing where concentrated clustering occurs and with what statistical confidence. |
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