Сравнение методов

	Байесовский пространственный автокорреляционный анализ	Пространственная автокорреляция
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1991	1950
Автор метода	Besag, York & Mollie	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Тип	Bayesian hierarchical spatial model	Spatial statistic / exploratory spatial data analysis
Основополагающий источник	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.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Другие названия	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Связанные	6	5
Сводка	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.	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.