1. Geostatistical Simulations – Preparing for Worst-Case Scenarios
Eric Krause

2. What is interpolation?
Predict values at unknown locations using values at measured locations
Many interpolation methods: kriging, IDW, LPI, etc
All interpolation methods assume spatial autocorrelation
Eric Krause

3. Why should I perform geostatistical simulations?
There are many different surfaces that all pass through the same input points and have the same statistical properties.
Any of these surfaces could be correct
Kriging attempts to find the average of all these surfaces in order to give the best guess of the value at new locations.
But what if we’re not interested in the best prediction? What if we want to know the worst-case scenario?
Answer: Simulate many surfaces and pick out the worst cases
Eric Krause

4. What else can geostatistical simulations do?
Predict the average value in polygons, for example, predict average temperature within a county
Better than using Zonal Statistics with a raster
Calculate statistics from all the simulated rasters
For worst-case scenarios, Quantiles are useful
Use heterogeneous measurement errors
Measurements are often imprecise. Sometimes this precision is known, and it can be incorporated into simulations.
Eric Krause

5. How do I perform geostatistical simulations?
Create a Simple Kriging geostatistical layer in the Geostatistical Wizard
Use this layer as input into the Gaussian Geostatistical Simulations geoprocessing tool.
Condition on the points you used to build the Simple Kriging geostatistical layer
Eric Krause

6. Example applications
Interpolate pollution, give recommendations to cities and counties about worst-case scenarios
Climate data comes from multiple data sources with different accuracies.
Interpolate rainfall data to determine whether levies will breach in worst-case
Eric Krause

7. Demo
Eric Krause
Eric Krause