What is geostatistics?The geostatistical workflowWhat is the ArcGIS Geostatistical Analyst extension?Essential vocabulary for Geostatistical AnalystA quick tour of Geostatistical AnalystEnabling the Geostatistical Analyst extensionAdding the Geostatistical Analyst toolbar to ArcMapAn introduction to interpolation methodsClassification trees of the interpolation methods offered in Geostatistical AnalystThe importance of knowing your dataMap the dataExploratory Spatial Data Analysis (ESDA)Examine the distribution of your dataExamining the distribution of your data using histograms and normal QQ plotsHistogramsNormal QQ plot and general QQ plotBox-Cox, arcsine, and log transformationsUsing Box-Cox, arcsine, and log transformationsNormal score transformationUsing normal score transformationsComparing normal score transformations to other transformationsLooking for global and local outliersIdentifying global outliers using the Histogram toolIdentifying local outliers using the Semivariogram/Covariance Cloud toolIdentifying local outliers using the Voronoi Map toolTrend analysisExamining global trends through trend analysisLooking for global trendsModeling global trendsExamining local variationExamining spatial autocorrelation and directional variationThe Semivariogram/Covariance Cloud toolExamining spatial autocorrelation and directional variation using the Semivariogram/Covariance Cloud toolThe Crosscovariance Cloud toolExamining covariation among multiple datasetsExamining covariation among multiple datasets using the Crosscovariance Cloud toolGeostatistical Analyst example applicationsAnalyzing the surface properties of nearby locationsSearch neighborhoodsAltering the search neighborhood by changing the number of neighborsAltering the search neighborhood by changing its size and shapeAltering the map view in Geostatistical WizardDetermining the prediction for a specific locationAccounting for directional influencesSmooth interpolationHow coincident data is handled in the Geostatistical Analyst extensionHow different input data formats are handledParameter optimizationWorking with large datasets in Geostatistical AnalystParallel processing with multiple CPUsDeterministic methods for spatial interpolationHow global polynomial interpolation worksVisualizing global polynomial interpolationCreating maps using global polynomial interpolationHow local polynomial interpolation worksVisualizing local polynomial interpolationCreating maps using local polynomial interpolationHow inverse distance weighted interpolation worksCreating maps using inverse distance weighted interpolationHow radial basis functions workVisualizing radial basis functionsCreating maps using Radial Basis FunctionsHow Diffusion Interpolation With Barriers worksCreating maps using diffusion interpolation with barriersHow Kernel Interpolation With Barriers worksCreating maps using kernel interpolation with barriersWhat are geostatistical interpolation techniques?Understanding geostatistical analysisKriging in Geostatistical AnalystRandom processes with dependenceComponents of geostatistical modelsEmpirical semivariogram and covariance functionsCreating empirical semivariogramsBinning empirical semivariogramsChoosing a lag sizeEmpirical semivariograms for different directionsSemivariogram and covariance functionsUnderstanding a semivariogram: The range, sill, and nuggetModeling a semivariogramFitting a model to the empirical semivariogramCombining semivariogram modelsAccounting for anisotropy using directional semivariogram and covariance functionsEstimating cross-covariance models for cokrigingModeling semivariogram and covariance functions: Selecting a modelModeling semivariogram and covariance functions: Exploring directional autocorrelationModeling semivariogram and covariance functions: Accounting for anisotropyAltering the anisotropy parameters when modeling semivariogram and covariance functionsModeling semivariogram and covariance functions: Changing the lag size and number of lagsModeling semivariogram and covariance functions: Changing the partial sill and nuggetHandling measurement error when modeling semivariogram and covariance functionsUnderstanding transformations and trendsUnderstanding how to remove trends from the dataRemoving global and local trends from the data: DetrendingAdjusting for preferential sampling by declustering the dataPerforming declustering to adjust for preferential samplingBivariate normal distributionsChecking for a bivariate normal distributionUnderstanding how to create surfaces using geostatistical techniquesWhat are the different kriging models?What output surface types can the kriging models generate?Understanding ordinary krigingUsing ordinary kriging to create a prediction mapUsing ordinary kriging to create a prediction standard error mapCreating a prediction map using ordinary kriging with a data transformationUsing ordinary kriging with detrending to create a prediction mapUnderstanding simple krigingUsing simple kriging to create a prediction mapUsing simple kriging to create a quantile mapUsing simple kriging to create a probability mapUsing simple kriging to create a prediction standard error mapUsing simple kriging with a data transformation to create a prediction mapUsing simple kriging with a data transformation and declustering to create a prediction mapUnderstanding universal krigingUsing universal kriging to create a prediction mapUsing universal kriging to create a prediction standard error mapUnderstanding indicator krigingUnderstanding thresholdsUsing indicator kriging to create a probability mapUnderstanding probability krigingUsing probability kriging to create a probability mapUnderstanding disjunctive krigingUsing disjunctive kriging to create a prediction mapUsing disjunctive kriging to create a prediction standard error mapUsing disjunctive kriging to create a probability mapUsing disjunctive kriging to create a standard error of indicators mapUsing disjunctive kriging with declustering to create a prediction mapUnderstanding cokrigingCreating a prediction map using cokrigingCreating a kriging map using the default optionsUnderstanding measurement errorHow Moving Window Kriging worksHow semivariogram sensitivity worksWhat is geostatistical simulation?Key concepts of geostatistical simulationHow Gaussian Geostatistical Simulations worksHow Extract Values To Table worksWhat is areal interpolation?Using areal interpolation to perform polygon-to-polygon predictionsWhat is Empirical Bayesian Kriging?Performing cross-validation and validationUsing cross-validation to assess parameter valuesSaving the cross-validation statistics to a fileComparing modelsUsing validation to assess modelsHow Subset Features worksAn introduction to sampling/monitoring networksHow Create Spatially Balanced Points worksHow Densify Sampling Network worksWhat is a geostatistical layer?Managing geostatistical layersRepresenting geostatistical layersRepresenting geostatistical layers as contoursRepresenting geostatistical layers as filled contoursRepresenting geostatistical layers as gridsRepresenting geostatistical layers as hillshadesData classificationClassifying data by setting a predefined classification methodClassifying data by manually altering the class breaksSaving and exporting geostatistical layersExporting geostatistical layers to a vector formatExporting geostatistical layers to a raster formatFundamentals of creating a raster from a geostatistical layerRecovering geostatistical layers with broken linksChanging the extent of a geostatistical layerPredicting values for specific locationsChanging the parameters of a geostatistical layerIntroduction to the ArcGIS Geostatistical Analyst TutorialExercise 1: Creating a surface using default parametersExercise 2: Exploring your dataExercise 3: Mapping ozone concentrationExercise 4: Comparing modelsExercise 5: Mapping the probability of ozone exceeding a critical threshold