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| Bayesian Local Indicators of Spatial Association (Bayesian LISA)× | Lokal Moran's I (LISA)× | |
|---|---|---|
| Fagområde | Rumlig analyse | Rumlig analyse |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2000s–2010s | 1995 |
| Ophavsperson≠ | Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand) | Luc Anselin |
| Type≠ | Bayesian local spatial statistic | Local spatial autocorrelation statistic |
| Oprindelig kilde | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ | Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Aliasser | Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA | Local Indicator of Spatial Association, LISA statistic, Anselin Local Moran, local spatial autocorrelation index |
| Relaterede | 6 | 6 |
| 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. | 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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