What is geostatistics?
The geostatistical workflow
What is ArcGIS Geostatistical Analyst?
Essential vocabulary for Geostatistical Analyst
A quick tour of Geostatistical Analyst
Enabling the Geostatistical Analyst extension
Adding the Geostatistical Analyst toolbar to ArcMap
An introduction to interpolation methods
Classification trees of the interpolation methods offered in Geostatistical Analyst
The importance of knowing your data
Map the data
Exploratory Spatial Data Analysis (ESDA)
Examine the distribution of your data
Examining the distribution of your data using histograms and normal QQ plots
Histograms
Normal QQ plot and general QQ plot
Box-Cox arcsine and log transformations
Using Box-Cox arcsine and log transformations
Normal score transformation
Using normal score transformations
Comparing normal score transformations to other transformations
Looking for global and local outliers
Identifying global outliers using the Histogram tool
Identifying local outliers using the Semivariogram Covariance Cloud tool
Identifying local outliers using the Voronoi Map tool
Trend analysis
Examining global trends through trend analysis
Looking for global trends
Modeling global trends
Examining local variation
Examining spatial autocorrelation and directional variation
The Semivariogram Covariance Cloud tool
Examining spatial autocorrelation and directional variation using the Semivariogram/Covariance Cloud tool
The Crosscovariance Cloud tool
Examining covariation among multiple datasets
Examining covariation among multiple datasets using the Crosscovariance Cloud tool
Geostatistical Analyst example applications
Analyzing the surface properties of nearby locations
Search neighborhoods
Altering the search neighborhood by changing the number of neighbors
Altering the search neighborhood by changing its size and shape
Altering the map view in Geostatistical Wizard
Determining the prediction for a specific location
Accounting for directional influences
Smooth interpolation
What output surface types can the interpolation models generate?
How coincident data are handled
How different input data formats are handled
Parameter optimization
Working with large datasets
Parallel processing with multiple CPUs
Deterministic methods for spatial interpolation
How Global Polynomial Interpolation works
Visualizing Global Polynomial Interpolation
Creating maps using Global Polynomial Interpolation
How Local Polynomial Interpolation works
Visualizing Local Polynomial Interpolation
Creating maps using Local Polynomial Interpolation
How Inverse Distance Weighted Interpolation works
Creating maps using Inverse Distance Weighted Interpolation
How Radial Basis Functions work
Visualizing Radial Basis Functions
Creating maps using Radial Basis Functions
How Diffusion Interpolation with Barriers works
Creating maps using Diffusion Interpolation with Barriers
How Kernel Interpolation with Barriers works
Creating maps using Kernel Interpolation with Barriers
What are geostatistical interpolation techniques?
Understanding geostatistical analysis
Kriging in Geostatistical Analyst
Random processes with dependence
Components of geostatistical models
Empirical semivariogram and covariance functions
Creating empirical semivariograms
Binning empirical semivariograms
Choosing a lag size
Empirical semivariograms for different directions
Semivariogram and covariance functions
Understanding a semivariogram the range sill and nugget
Modeling a semivariogram
Fitting a model to the empirical semivariogram
Combining semivariogram models
Accounting for anisotropy using directional semivariogram and covariance functions
Estimating crosscovariance models for cokriging
Modeling semivariogram and covariance functions Selecting a model
Modeling semivariogram and covariance functions Exploring directional autocorrelation
Modeling semivariogram and covariance functions Accounting for anisotropy
Altering the anisotropy parameters when modeling semivariogram and covariance functions
Modeling semivariogram and covariance functions Changing the lag size and number of lags
Modeling semivariogram and covariance functions Changing the partial sill and nugget
Modeling semivariogram and covariance functions Handling measurement error
Understanding transformations and trends
Understanding how to remove trends from the data
Removing global and local trends from the data Detrending
Adjusting for preferential sampling by declustering the data
Performing declustering to adjust for preferential sampling
Bivariate normal distributions
Checking for a bivariate normal distribution
Understanding how to create surfaces using geostatistical techniques
What are the different kriging models?
Understanding ordinary kriging
Using ordinary kriging to create a prediction map
Using ordinary kriging to create a prediction standard error map
Creating a prediction map using ordinary kriging with a data transformation
Using ordinary kriging with detrending to create a prediction map
Understanding simple kriging
Using simple kriging to create a prediction map
Using simple kriging to create a quantile map
Using simple kriging to create a probability map
Using simple kriging to create a prediction standard error map
Using simple kriging with a data transformation to create a prediction map
Using simple kriging with a data transformation and declustering to create a prediction map
Understanding universal kriging
Using universal kriging to create a prediction map
Using universal kriging to create a prediction standard error map
Understanding indicator kriging
Understanding thresholds
Using indicator kriging to create a probability map
Understanding probability kriging
Using probability kriging to create a probability map
Understanding disjunctive kriging
Using disjunctive kriging to create a prediction map
Using disjunctive kriging to create a prediction standard error map
Using disjunctive kriging to create a probability map
Using disjunctive kriging to create a standard error of indicators map
Using disjunctive kriging with declustering to create a prediction map
Understanding cokriging
Using cokriging to create a prediction map
Creating a kriging map using the default options
Understanding measurement error
How Moving Window Kriging works
How Semivariogram Sensitivity works
What is geostatistical simulation?
Key concepts of geostatistical simulation
How Gaussian Geostatistical Simulations works
How Extract Values to Table works
What is Areal Interpolation
Using areal interpolation to predict to new polygons
What is Empirical Bayesian Kriging?
Performing cross validation and validation
Using cross validation to assess parameter values
Saving the cross-validation statistics to a file
Comparing models
Using validation to assess models
How Subset Features works
An introduction to sampling monitoring networks
How Create Spatially Balanced Points works
How Densify Sampling Network works
What is a geostatistical layer?
Managing geostatistical layers
Representing geostatistical layers
Representing geostatistical layers as contours
Representing geostatistical layers as filled contours
Representing geostatistical layers as grids
Representing geostatistical layers as hillshades
Data classification
Data classification setting a predefined classification method
Data classification manually altering the class breaks
Saving and exporting geostatistical layers
Exporting geostatistical layers to a vector format
Exporting geostatistical layers to a raster format
Fundamentals of creating a raster from a geostatistical layer
Recovering geostatistical layers with broken links
Changing the extent of a geostatistical layer
Predicting values for specific locations
Changing the parameters of a geostatistical layer
Introduction to the ArcGIS Geostatistical Analyst Tutorial
Exercise 1 Creating a surface using default parameters
Exercise 2 Exploring your data
Exercise 3 Mapping ozone concentration
Exercise 4 Comparing models
Exercise 5 Mapping the probability of ozone