Linganisha mbinu

	Uchambuzi wa Kipeo cha Moto cha Kibayeshi	Bayesian Local Indicators of Spatial Association (Bayesian LISA)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1987	2000s–2010s
Mwanzilishi	Clayton & Kaldor (1987); Lawson (2001 onward)	Extension of Anselin (1995) LISA framework within Bayesian hierarchical modeling traditions (Banerjee, Carlin, Gelfand)
Aina	Bayesian spatial cluster detection	Bayesian local spatial statistic
Chanzo asilia	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.
Majina mbadala	Bayesian spatial cluster detection, Bayesian disease mapping hot spots, empirical Bayesian hot spot analysis, Bayesian spatial smoothing hot spots	Bayesian LISA, Bayesian local spatial autocorrelation, Bayesian local Moran, B-LISA
Zinazohusiana	5	6
Muhtasari	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.	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.