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| Bayesian Local Indicators of Spatial Association (Bayesian LISA)× | Rumlig Autokorrelation× | |
|---|---|---|
| Fagområde | Rumlig analyse | Rumlig analyse |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2000s–2010s | 1950 |
| Ophavsperson≠ | Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand) | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Type≠ | Bayesian local spatial statistic | Spatial statistic / exploratory spatial data analysis |
| Oprindelig kilde≠ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Aliasser | Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | 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. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
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