Latest Contributions by EricKrause

If you're on the semivariogram screen, you can edit the Major Range by clicking the calculator button directly on its right.  In order for Minor Range to be exposed, you need to set Anisotropy to True. If you're on the Search Neighborhood screen (where you can preview the prediction surface), set "Copy from Variogram" to False.  Then you should be able to alter the Major and Minor Semiaxes. ... View more

If the semivariogram was different for two exponential models compared to one exponential model, then they must have had different Major Semiaxes (or Minor Semiaxes if you corrected for anisotropy).  When you supply more than one model, the semivariogram curve is generated by a weighted average of the multiple models (where the weights are determined by least-squares).  Using multiple models is beneficial when your data is the result of multiple underlying processes (wind patterns and temperature, for example).  Each model can be used to account for a different underlying process.  Sometimes these underlying processes are known, and sometimes they aren't.  Using multiple models when you don't know the processes that generated your data can be suspect, but if the cross-covariance cloud and empirical semivariogram suggest that different distances follow different semivariograms (for example, one model fits the data up to 1000 meters, and another model fits beyond that), it may be justified to use two models with different search neighborhoods. ... View more

I don't see the dot pattern in the pdf you posted.  It's certainly nowhere near as distinct as either of the topics you've recently posted on the subject of strange rasters. ... View more

In the Attribute Table, use "Select by Attributes" in the Table Options pull-down menu.  Use the equation "X > -9999", where X is the field of your precipitation values.  This will select all points with values above -9999.  With those points selected, run the kriging tool; only the selected points will be used in the interpolation. There are other ways of doing it, but I think that's the simplest. ... View more

You can send it to ekrause@esri.com ... View more

If you're willing to send your GA layer and the data used to create it, we'll take a look and try to figure out what's going on. ... View more

On the semivariogram screen in the Wizard, what is your value for "Measurement Error"? ... View more

Glad I could help. ... View more

At the Search Neighborhood screen inside the Wizard, try changing "Neighborhood type" to Smooth. If that doesn't work (or if your version does not have the Smooth option), try increasing the Major and Minor semiaxes. If that still doesn't fix it, let me know. Do the geostatistical layers look smooth before you convert them to raster? ... View more

The default parameters for the SA tool and the GA wizard are not computed in the same way, and I don't know of any way to export the parameters from the wizard to the SA tool.  You also need to understand that Kriging is our bread-and-butter here in Geostatistical Analyst; the wizard offers many more parameters and options than the Spatial Analyst tool.  So even if you could get the wizard parameters, many of them could not be used in the SA tool. Again, I think the best (and simplest) thing to do here is to use the Create Geostatistical Layer tool in conjunction with an altered xml.  When you click Finish in the wizard, a "Method Summary" window will pop up.  On that window, click "Save", and you will be able to save the xml on your harddrive.  Go in with a text editor, and change all "auto" flags to "true".  Feed this altered xml into the Create Geostatistical Layer tool as the model source, and give it a new set of data.  The "auto=true" flag will tell the CGL tool to update default parameters for the new dataset.  Then convert this geostatistical layer to raster.  When looping through your datasets in python, just keep running the CGL tool with the original altered xml and converting the output to raster.  As for why the SA tool is giving this geometrical pattern, I'm not sure, but I doubt it has much to do with the interpolation parameters like range, nugget, and sill.  My intuition is that it has to do with the search radius and the spatial orientation of the dataset. ... View more

Here is my recommendation, and I wish there was a simpler way to do this.  Use the Wizard to create a kriging layer.  On the Method Properties screen in the Wizard, save the xml file on your harddrive.  Then open the xml with a text editor, and wherever you see auto="false", change it to auto="true", and save the xml file.  The "auto" flag tells the software whether to update with new default values. Run the Create Geostatistical Layer tool using the altered xml as the model source, and point to a new set of data.  This will create a kriging layer with the default parameters for the new data, and you can convert this layer to raster. You should be able to automate this within Python, just keep using the original altered xml file in your loop. ... View more

At the Search Neighborhood screen inside the Wizard, try changing "Neighborhood type" to Smooth.  If that doesn't work (or if your version does not have the Smooth option), try increasing the Major and Minor semiaxes.  If that still doesn't fix it, let me know. For reference, which version of ArcGIS are you using? ... View more

Can you elaborate on what you mean by "structure"?  Your kriging surfaces look quite smooth, so I'm not sure what artifact you're talking about. Also, the semivariogram appears to have three distinct strata... any idea where that might be coming from? You might also be interested in this recent blog post: http://blogs.esri.com/Dev/blogs/geoprocessing/archive/2010/06/18/Automating-geostatistical-interpolation-using-template-layers.aspx If you want to use the same kriging parameters (range, sill, lag, etc) for all of your interpolations, that post will help. ... View more

The points are binned in the following way: For each pair of points, a vector is created between them.  One point in the pair is plotted at the origin, and the other point in the pair is plotted according to it's distance in the x and y direction (the point chosen to be at the origin is the one that will keep the vector in the first or second quadrant).  For example, if one point was located at (5,5), and the other point was at (8,9), then the semivariance between the two points would be plotted at (3,4).  After all pairs are plotted this way, the result is a scatterplot of semivariance values in the first and second quadrants.  These semivariance values are then binned together using a grid of cells that are each 1 lag by 1 lag.  The average semivariance is calculated for each cell.  Then, each of these binned averages is plotted on the semivariogram in the wizard.  We use binned values because plotting a point for every pair would overburden the semivariogram; with that many points, it would be hard to visually determine what is going on.  I've attached a rough picture of how the semivariances are binned.  Let me know if you need more detail or clarification. ... View more

Hi Deep, The Wizard help describes the points as: "Binned points (the red dots) are generated by grouping (binning) empirical semivariogram/covariance points together using square cells that are one lag wide. Average points (the blue crosses, which are new in ArcGIS 10) are generated by binning empirical semivariogram/covariance points that fall within angular sectors. The graph can display binned points, average points, or both. Binned points show local variation in the semivariogram/covariance values, whereas average values show smooth semivariogram/covariance value variation." As for the h.10^-5 on x axis, it is using the formula: x = h(10^-5).  Scientific notation often takes the form h = x(10^5).  The difference is whether you are writing the scale relative to h or relative to the value on the x-axis.  We chose this way because that is historically how the scale of the semivariogram is written. If you require further information or clarification, let me know.  I've attached an image of a typical semivariogram from the Geostatistical Wizard in ArcGIS 10. ... View more