Linganisha mbinu

	Uchambuzi wa maeneo yenye joto (hot spot) ya Multiscale Getis-Ord Gi*	Uhusiano wa Kiasilia
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1995 (Gi* basis); multiscale application 2000s onward	1950
Mwanzilishi	Ord & Getis (1995); multiscale extension developed in applied spatial analysis practice	P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995)
Aina	Local spatial statistic (multiscale)	Spatial statistic / exploratory spatial data analysis
Chanzo asilia	Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306.	Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23.
Majina mbadala	multi-distance Gi*, multiscale hot spot analysis, multi-bandwidth Getis-Ord, scale-varying Gi*	spatial dependence, geographic autocorrelation, spatial clustering measure, SA
Zinazohusiana	5	5
Muhtasari	Multiscale Getis-Ord Gi* extends the classic local hot spot statistic by computing Gi* z-scores across a range of spatial distance bands or neighborhood sizes. This reveals whether clusters of high or low values are scale-dependent — appearing only at fine local scales, only at broad regional scales, or persistently across all scales — providing richer spatial intelligence than a single-bandwidth analysis.	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.