Salīdzināt metodes

	Bayesian Spatial Autocorrelation	Lokālās telpiskās asociācijas indikatori (LISA)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1991	1995
Autors	Besag, York & Mollie	Luc Anselin
Tips	Bayesian hierarchical spatial model	Local spatial statistic
Pirmavots	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.	Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115.
Citi nosaukumi	Bayesian spatial dependence, Bayesian LISA, Bayesian spatial clustering, BSA	LISA, local spatial autocorrelation statistics, local Moran's I, Anselin LISA
Saistītās	6	6
Kopsavilkums	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.	LISA, introduced by Luc Anselin in 1995, decomposes a global spatial autocorrelation index into a location-specific statistic for every observation. It identifies where statistically significant spatial clusters and outliers occur on a map, enabling researchers to move beyond a single global summary and pinpoint the geographic sources of spatial dependence.