Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beijesas karsto punktu analīze× | Lokālā Getis-Ord Gi* (Karsto punktu analīze)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1987 | 1992–1995 |
| Autors≠ | Clayton & Kaldor (1987); Lawson (2001 onward) | Arthur Getis and J. Keith Ord |
| Tips≠ | Bayesian spatial cluster detection | Local spatial association statistic |
| Pirmavots≠ | Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). CRC Press. ISBN: 978-1138575424 | 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 | Bayesian spatial cluster detection, Bayesian disease mapping hot spots, empirical Bayesian hot spot analysis, Bayesian spatial smoothing hot spots | Gi* statistic, Getis-Ord Gi*, local G-star, hot spot statistic |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Bayesian Hot Spot Analysis identifies spatial clusters of elevated risk or intensity by combining observed data with prior beliefs about spatial structure. It uses Bayesian smoothing — pooling information across neighboring areas — to stabilize estimates in small areas and then flags locations where the posterior probability of exceeding a risk threshold is high. | 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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