Regression modelGIS / spatial
Hot Spot Analysis (Getis-Ord Gi*)
Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation.
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Sources
- Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI: 10.1111/j.1538-4632.1992.tb00261.x ↗
- Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286-306. DOI: 10.1111/j.1538-4632.1995.tb00912.x ↗
Related methods
Referenced by
Bayesian Hot Spot AnalysisBayesian Kernel Density EstimationGlobal Getis-Ord Gi*Global Hot Spot AnalysisGlobal Remote Sensing ClassificationGlobal Spatial AutocorrelationLocal Getis-Ord Gi*Local Hot Spot AnalysisLocal Indicators of Spatial AssociationLocal Kernel Density EstimationLocal Moran's ILocal Network-Based Spatial AnalysisLocal Spatial AutocorrelationMultiscale Getis-Ord Gi*Network-Based Spatial AnalysisPanel Hot Spot AnalysisRemote Sensing ClassificationRobust Getis-Ord Gi*Space-Time Getis-Ord Gi*Space-Time Hot Spot AnalysisSpace-Time Kernel Density EstimationSpace-Time Local Indicators of Spatial AssociationSpace-Time Remote Sensing Classification