I thought I had my head around cross-validation in Geospatial Analyst, but I find the following apparent discrepancy confusing/troubling.
I have split my input data into training and test data sets using "Subset Features".
I have built a simple kriging model using the training data set.
I use GA Layer to Points using the training data set kriging model layer, and select the test data set as Input Point Observation Locations, creating a "Standard Error" field (among others). My understanding is this is the square root of the kriging variance at those points.
I right-click on my training data set kriging model layer and select "Change Output to Prediction Standard Error". I thought this was also displaying the square root of the kriging variance.
- The values on the "Prediction Standard Error Map" are significantly lower at my test point locations than the "Standard Error" values from the GA Layer to Points step (loosely 1/5 of the value).
- I've confirmed this by converting the Prediction Standard Error Map to a Raster, and extracting values to the same test data set I used GA Layer to Points on, and comparing both values at all the points.
Are the "Standard Error" values from "GA Layer to Points" not the same as the "Prediction Standard Error" from the "Prediction Standard Error Maps"?
Am I using one of the tools wrong?
Any clarification greatly appreciated. Let me know if any better description/explanation/elaboration from my end will help. I'm using ArcGIS 10.1 SP1