1. Concepts and Applications of Kriging
Eric Krause

2. Sessions of note… Tuesday
ArcGIS Geostatistical Analyst - An Introduction 8:30-9:45 Room 14 A
Concepts and Applications of Kriging :15-11:30 Room 15 A
Creating Surfaces from Various Data Sources 1:30-2:45 Room 10
EBK – Robust Kriging as a Geoprocessing Tool 5:30-6:15 Demo theater
Wednesday
Geostat. Simulations - Preparing for Worst Case Scenarios 11:30-12:15 Demo theater
ArcGIS Geostatistical Analyst - An Introduction 1:30-2:45 Room 15 B
Concepts and Applications of Kriging :15-4:30 Sapphire I/J
Thursday
Regression kriging… :30-9:45 Room 17B
Surface Interpolation in ArcGIS :30-11:15 Demo theater
Polygon-to-Polygon Predictions using Areal Interpolation 11:30-12:15 Demo theater
Regression kriging… :30-2:45 Sapphire I/J
Creating Surfaces from Various Data Sources 3:15-4:30 Room 16 A

3. Outline
Introduction to interpolation
Exploratory spatial data analysis (ESDA)
Using the Geostatistical Wizard
Validating interpolation results
Empirical Bayesian Kriging and EBK Regression Prediction
Areal Interpolation
Questions

4. What is interpolation?
Predict values at unknown locations using values at measured locations
Many interpolation methods: kriging, IDW, LPI, etc

5. What is autocorrelation?
Tobler’s first law of geography:
"Everything is related to everything else,
but near things are more related than distant things."

6. What is kriging?
Kriging is the optimal interpolation method if the data meets certain conditions.
What are these conditions?
Normally distributed
Stationary
No trends
How do I check these conditions?
Exploratory Spatial Data Analysis (ESDA)

7. What is an “optimal” interpolator?
Estimates the true value, on average
Lowest expected prediction error
Able to use extra information, such as covariates
Filters measurement error
Can be generalized to polygons (Areal interpolation, Geostatistical simulations)
Estimates probability of exceeding a critical threshold

8. Geostatistical workflow
Explore the data
Choose an interpolation method
Fit the interpolation model
Validate the results
Repeat steps 2-4 as necessary
Map the data for decision-making

9. Exploratory Spatial Data Analysis
Where is the data located?
What are the values of the data points?
How does the location of a point relate to its value?

10. Does my data follow a normal distribution?
How do I check?
Histogram
Check for bell-shaped distribution
Look for outliers
Normal QQPlot
Check if data follows 1:1 line
What can I do if my data is not normally distributed?
Apply a transformation
Log, Box Cox, Arcsin, Normal Score Transformation

11. Does my data follow a normal distribution?
What should I look for?
Bell-shaped
No outliers
Mean ≈ Median
Skewness ≈ 0
Kurtosis ≈ 3

12. Does my data follow a normal distribution?

13. Normal Score Transformation
Fits a smooth curve to the data
Performs a quantile transformation to the normal distribution
Performs calculations with transformed data, then transforms back at the end
Simple kriging with normal score transformation is default in ArcGIS and beyond

14. Is my data stationary? What is stationarity?
Something is not changing.
The statistical relationship between two points depends only on the distance between them.
The variance of the data is constant (after trends have been removed)
How do I check for stationarity?
Voronoi Map symbolized by Entropy or Standard Deviation
What can I do if my data is nonstationary?
Transformations can stabilize variances
Empirical Bayesian Kriging

15. Is my data stationary?
When symbolized by Entropy or StDev, look for randomness in the symbolized Thiessen Polygons.

16. Is my data stationary?
When symbolized by Entropy or StDev, look for randomness in the symbolized Thiessen Polygons.

17. Does my data have trends?
What are trends?
Trends are systematic changes in the values of the data across the study area.
How do I check for trends?
Trend Analysis ESDA tool
What can I do if my data has trends?
Use trend removal options
Potential problem – Trends are often indistinguishable from autocorrelation and anisotropy
EBK

18. Demo
ESDA
Eric Krause
Eric Krause, Konstantin Krivoruchko

19. Semivariogram/Covariance Modeling

20. Cross-validation Used to determine the quality of the model
Iteratively discard each sample
Use remaining points to estimate value at measured location
Compare predicted versus measured value

21. Kriging output surface types
Prediction
Error of Predictions
Probability
Quantile

22. Demo
Kriging
Eric Krause
Eric Krause, Konstantin Krivoruchko

23. Empirical Bayesian Kriging
Advantages
Requires minimal interactive modeling, spatial relationships are modeled automatically
Usually more accurate, especially for small or nonstationary datasets
Uses local models to capture small scale effects
Doesn’t assume one model fits the entire data
Standard errors of prediction are more accurate than other kriging methods
Disadvantages
Processing is slower than other kriging methods
Limited customization

24. How does EBK work? Divide the data into subsets of a given size
Controlled by “Subset Size” parameter
Subsets can overlap, controlled by “Overlap Factor”
For each subset, estimate the semivariogram
Simulate data at input point locations and estimate new semivariogram
Repeat step 3 many times. This results in a distribution of semivariograms
Controlled by “Number of Simulations”
Mix the local surfaces together to get the final surface.

25. EBK Regression Prediction
New tool available in ArcGIS Pro 1.2
Allows you to use explanatory variable rasters to improve predictions
Automatically extracts useful information from explanatory variables
Uses Principle Components to handle multicollinearity

26. Empirical Bayesian Kriging and EBK Regression Prediction
Demo
Empirical Bayesian Kriging and EBK Regression Prediction
Eric Krause
Eric Krause, Konstantin Krivoruchko

27. Obesity surface and error surface
Areal Interpolation
Obesity by school zone
Obesity surface and error surface
Obesity by census block
Predict data in a different geometry
School zones to census tracts
Estimate values for missing data
What it does. (cast data)
Uses geostatistical methods.
Important for many analyses that need all of your data to be in the same geometry.
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28. Types of Areal Interpolation
Average (Gaussian)
Median age, average temperature
Rate (Binomial)
Cancer rates, obesity rates, percent of college graduates
Event (Overdispersed Poisson)
Animal counts, crimes

29. Polygon to Polygon Workflow

30. Areal Interpolation Eric Krause Demo
Eric Krause, Konstantin Krivoruchko

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33. ArcGIS Geostatistical Analyst - An Introduction