Сравнение методов

	Байесовский анализ горячих точек	Локальная пространственная автокорреляция
Область	Пространственный анализ	Пространственный анализ
Семейство	Regression model	Regression model
Год появления	1987	1995
Автор метода	Clayton & Kaldor (1987); Lawson (2001 onward)	Luc Anselin
Тип	Bayesian spatial cluster detection	Spatial association analysis
Основополагающий источник	Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). CRC Press. ISBN: 978-1138575424	Anselin, L. (1995). Local indicators of spatial association — LISA. Geographical Analysis, 27(2), 93–115.
Другие названия	Bayesian spatial cluster detection, Bayesian disease mapping hot spots, empirical Bayesian hot spot analysis, Bayesian spatial smoothing hot spots	local spatial association, local SA, LISA methods, local spatial clustering
Связанные	5	6
Сводка	Bayesian Hot Spot Analysis identifies spatial clusters of elevated risk or intensity by combining observed data with prior beliefs about spatial structure. It uses Bayesian smoothing — pooling information across neighboring areas — to stabilize estimates in small areas and then flags locations where the posterior probability of exceeding a risk threshold is high.	Local Spatial Autocorrelation methods decompose global spatial clustering into location-specific statistics, revealing where in a study area significant clustering or dispersion occurs. Each observation receives its own association score and significance value, enabling the detection of spatial hot spots, cold spots, and spatial outliers rather than reporting a single summary statistic.