Geostatistical Simulations – Preparing for Worst-Case Scenarios Eric Krause

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

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

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

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

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

Demo Eric Krause Eric Krause