Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Lokālā Getis-Ord Gi* (Karsto punktu analīze)× | Lokālais Morana I (LISA)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1992–1995 | 1995 |
| Autors≠ | Arthur Getis and J. Keith Ord | Luc Anselin |
| Tips≠ | Local spatial association statistic | Local spatial autocorrelation statistic |
| Pirmavots≠ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Citi nosaukumi | Gi* statistic, Getis-Ord Gi*, local G-star, hot spot statistic | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| Saistītās≠ | 5 | 6 |
| Kopsavilkums≠ | 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. | Local Moran's I, introduced by Luc Anselin in 1995, is a Local Indicator of Spatial Association (LISA) that decomposes global spatial autocorrelation into location-specific contributions. For every observation it produces a signed statistic and a significance value, enabling researchers to identify spatial clusters (high-high, low-low) and spatial outliers (high-low, low-high) on a map. |
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