Regression modelGIS / spatial
Spatial Autocorrelation
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.
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Sources
- Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI: 10.2307/2332142 ↗
- Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic Publishers. ISBN: 978-9024737322
Related methods
Referenced by
Bayesian Kernel Density EstimationBayesian KrigingBayesian Local Indicators of Spatial AssociationBayesian Ordinary KrigingBayesian Spatial AutocorrelationBayesian Spatial Error ModelBayesian Spatial Lag ModelBayesian Universal KrigingCo-krigingGeary's CGlobal Co-KrigingGlobal Getis-Ord Gi*Global Hot Spot AnalysisGlobal KrigingGlobal Moran's IGlobal Ordinary KrigingGlobal Remote Sensing ClassificationGlobal Spatial AutocorrelationGlobal Spatial Error ModelHot Spot AnalysisLocal Geary's CLocal Getis-Ord Gi*Local Indicators of Spatial AssociationLocal Kernel Density EstimationLocal Moran's ILocal Spatial AutocorrelationLocal Spatial Lag ModelMoran's IMultiscale Getis-Ord Gi*Multiscale Moran's IMultiscale Spatial AutocorrelationNetwork-Based Spatial AnalysisOrdinary KrigingPanel KrigingPanel Network-Based Spatial AnalysisPanel Ordinary KrigingPanel Spatial AutocorrelationRobust Geary's CRobust KrigingRobust Local Indicators of Spatial AssociationRobust Moran's IRobust Spatial AutocorrelationSpace-Time Spatial Lag Model