Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beijesas karsto punktu analīze× | Beijesa lokālie telpiskās asociācijas indikatori (Beijesa LISA)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1987 | 2000s–2010s |
| Autors≠ | Clayton & Kaldor (1987); Lawson (2001 onward) | Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand) |
| Tips≠ | Bayesian spatial cluster detection | Bayesian local spatial statistic |
| Pirmavots≠ | Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). CRC Press. ISBN: 978-1138575424 | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Citi nosaukumi | Bayesian spatial cluster detection, Bayesian disease mapping hot spots, empirical Bayesian hot spot analysis, Bayesian spatial smoothing hot spots | Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA |
| Saistītās≠ | 5 | 6 |
| 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. | Bayesian Local Indicators of Spatial Association extend the classical LISA framework by embedding local spatial association statistics within a Bayesian hierarchical model. Rather than relying on asymptotic permutation-based significance tests, this approach places prior distributions on spatial parameters and derives posterior probabilities that a location is part of a genuine spatial cluster, accounting for uncertainty and borrowing strength across nearby units. |
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