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| Байесов пространствен автокорелационен анализ× | Moran's I× | |
|---|---|---|
| Област | Пространствен анализ | Пространствен анализ |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 1991 | 1950 |
| Създател≠ | Besag, York & Mollie | Patrick A. P. Moran |
| Тип≠ | Bayesian hierarchical spatial model | Spatial autocorrelation statistic |
| Основополагащ източник≠ | Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43(1), 1–20. DOI ↗ | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Други названия | Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA | Moran's I statistic, global Moran's I, spatial autocorrelation index, Moran index |
| Свързани | 6 | 6 |
| Резюме≠ | Bayesian Spatial Autocorrelation embeds spatial dependence directly into a Bayesian hierarchical model. A Conditional Autoregressive (CAR) prior encodes the expectation that neighboring areas are more similar than distant ones, and posterior inference is obtained via MCMC. This approach is especially valuable in disease mapping, ecology, and regional science, where small-area estimates need borrowing strength across neighbors. | Moran's I is the standard global statistic for detecting spatial autocorrelation: whether nearby locations tend to share similar values. The index ranges from approximately −1 (perfect dispersion) through 0 (spatial randomness) to +1 (perfect clustering), allowing researchers to test whether a geographic pattern differs from complete spatial randomness with a single, interpretable number. |
| ScholarGateНабор от данни ↗ |
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