What would cause "ga layer to grid" to create negative values?

StacyStrickland

I have used the Geostatistical Analyst Wizard to interpolate point data using the radial basis function. The GA layer created has a minimum value of "0" and a maximum value of "114" which correlates to the point data values used. However, when I use the tool "GA Layer to Grid", I am getting a minimum value of "-4.04". I understand the mins and maxes for the raster aren't going to match because the values are averaged within the cell created, but how can a negative value be returned when none exist in the original point data or the GA layer?

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Eric6346

Hi Stacy,

The raster that comes out of GA Layer to Grid will nearly always have a different max/min than the geostatistical layer.  This is because they build their classification intervals based on different things.  The geostatistical layer classifies based on the min/max of the input data field, and the raster classifies based on the histogram of the output raster.  This can get confusing when some of the predicted values are larger than the max or lower than the min (which can happen with Radial Basis Functions).  The geostatistical layer will always set the minimum class break to the minimum of the input data and the maximum class break to the maximum of the data even if some of the predicted values are outside of that range.  In your case, the minimum of the raster is less than the minimum of the input data, and the maximum of the raster is less than the maximum of the input data.

The reason that they classify differently is that geostatistical layers are basically functions.  They contain references to the input data and all of the interpolation parameters, and all predictions are done on the fly.  In this sense, they have basically infinite resolution and are not constrained by a cell size like a raster.  Every unique (x,y) location will receive a unique predicted value.  This is very convenient for post-processing because the geostatistical layer itself contains all of the information about the interpolation model, so it can be directly passed into post-processing tools without needing to explicitly reference the data or the interpolation parameters (the layer itself already contains them).  However, the downside is that you cannot create a histogram of a geostatistical layer (since it has an infinite number of possible predictions), so we must fall back to the input data to build the classification intervals. 

At the end of the day, the raster is a much better representation of what the surface actually looks like.  As a rule, we generally suggest using geostatistical layers throughout your workflow, then export to a raster at the very end.  Geostatistical layers are very useful for long workflows, but their visual representation will never be as accurate as the raster.

Let me know if you need any clarifications or have any other questions.