Spatial methods
111 methods in this family.
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CA-MarkovCA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellCo-krigingCo-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secCokrigingCokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a relConditional Geostatistical SimulationConditional Geostatistical Simulation — most commonly implemented as Sequential Gaussian Simulation (SGS) — generates multiple stochastic realizations of a spatial random field thaGaussian ProcessA Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single vaGeary's CGeary's C is a global measure of spatial autocorrelation — whether nearby locations tend to have similar values — introduced by Roy Geary in 1954. Unlike Moran's I, which is built
All methods 111
CA-MarkovCo-krigingCokrigingConditional Geostatistical SimulationGaussian ProcessGeary's CGeary's CGeographically Weighted PCAGeographically Weighted Random ForestGeographically Weighted RegressionGetis-Ord Gi*GIS-MCDAGlobal Co-KrigingGlobal Getis-Ord Gi*Global Hot Spot AnalysisGlobal KrigingGlobal Moran's IGlobal Ordinary KrigingGlobal Remote Sensing ClassificationGlobal Spatial AutocorrelationGlobal Spatial Durbin ModelGlobal Spatial Error ModelGlobal Spatial Panel ModelGlobal Universal KrigingHot Spot AnalysisHuff ModelInverse Distance WeightingKrigingLandscape MetricsLeast-Cost PathLISALocal Geary's CLocal Geographically Weighted RegressionLocal Getis-Ord Gi*Local Hot Spot AnalysisLocal Indicators of Spatial AssociationLocal Kernel Density EstimationLocal KrigingLocal Moran's ILocal Network-Based Spatial AnalysisLocal Ordinary KrigingLocal Spatial AutocorrelationLocal Spatial Durbin ModelLocal Spatial Lag ModelLocal Spatial RegressionLocal Universal KrigingLocation-AllocationMap AlgebraMGWRMoran's IMoran's IMultiscale Geographically Weighted RegressionMultiscale Getis-Ord Gi*Multiscale Moran's IMultiscale Spatial AutocorrelationNetwork-Based Spatial AnalysisOrdinary KrigingPanel Geary's CPanel Geographically Weighted RegressionPanel Hot Spot AnalysisPanel Kernel Density EstimationPanel KrigingPanel Local Indicators of Spatial AssociationPanel Multiscale Geographically Weighted RegressionPanel Network-Based Spatial AnalysisPanel Ordinary KrigingPanel Spatial AutocorrelationPanel Spatial Durbin ModelPanel Spatial Error ModelPanel Spatial RegressionPanel Universal KrigingRadiation ModelRemote Sensing ClassificationRipley K FunctionRobust Co-KrigingRobust Geary's CRobust Getis-Ord Gi*Robust KrigingRobust Local Indicators of Spatial AssociationRobust Moran's IRobust Spatial AutocorrelationRobust Universal KrigingService Area AnalysisSpace-Time Geary's CSpace-Time Getis-Ord Gi*Space-Time Hot Spot AnalysisSpace-Time Kernel Density EstimationSpace-Time KrigingSpace-Time Local Indicators of Spatial AssociationSpace-Time Moran's ISpace-Time Network-Based Spatial AnalysisSpace-Time Ordinary KrigingSpace-Time Remote Sensing ClassificationSpace-Time Spatial AutocorrelationSpace-Time Spatial Durbin ModelSpace-Time Spatial Error ModelSpace-Time Spatial Lag ModelSpace-Time Spatial Panel ModelSpace-Time Spatial RegressionSpace-Time Universal KrigingSpatial AutocorrelationSpatial Difference-in-DifferencesSpatial Durbin ModelSpatial Error ModelSpatial Interaction ModelSpatial Lag ModelSpatial Panel ModelSpatial Propensity Score WeightingSpatial SAC ModelUncertainty QuantificationUniversal Kriging