방법 비교

	베이지안 국지 공간 연관성 지표 (Bayesian LISA)	지역적 모란 I (LISA)
분야	공간분석	공간분석
계열	Regression model	Regression model
기원 연도	2000s–2010s	1995
창시자	Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)	Luc Anselin
유형	Bayesian local spatial statistic	Local spatial autocorrelation statistic
원전	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.	Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.
별칭	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
관련	6	6
요약	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.