Latest Contributions by EricKrause

http://dusk.geo.orst.edu/gis/geostat_analyst.pdf It's on page 120. ... View more

There aren't any explicit modules or add-ons or anything for non-linear models.  However, you may be able to manually transform your predictor variables to linearize them (Box-Cox or logarithmic transformations, for example).  You would need to create a new field in your feature class, then use the Field Calculator to manually apply the transformation. ... View more

The histogram and QQ plot look pretty good without a transformation; you'll probably be fine without one.  That being said, I would still try Simple Kriging with a Normal Score Transformation. ... View more

The back-transformation needs to be built into the kriging step.  You can't simply apply a transformation, perform kriging, then apply the back-transformation to the results.  In other words, you should stick to the transformations that we've developed and integrated into the kriging: Log, Box-Cox, ArcSin, and Normal Score. The Normal Score is the most functional type: http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//00310000000v000000.htm For ArcGIS 10, Gaussian Kernels is the most functional approximation type for Normal Score Transformation. ... View more

From that Word doc, it doesn't look like a Log transformation is a good choice.  Instead, try a Normal Score Transformation. In the Geostatistical Wizard, use Kriging, then on the second page, use "Simple" as the kriging type.  The default transformation will be "Normal Score."  Click Next.  On this screen, change "Type" to Gaussian Kernels.  See if the default fit seems to fit your histogram.  You may need to try changing the number of kernels if the default fit doesn't look good.  If you find a transformation that fits your histogram, the software will automatically do the back-transformation for you, so the result of the kriging will be in the original units (not transformed units). The Normal Score transformation is the most powerful and functional transformation, but it's only available for Simple, Probability, and Disjunctive kriging. ... View more

The default Kernel Parameter in RBF is calculated such that it minimizes the Root-Mean-Square error (RMS) during crossvalidation.  Comparing the RMS values from different models is the most common method of deciding which model is better.  Comparing crossvalidation statistics between models can get complicated, but the rule-of-thumb is to use the model with the lowest RMS. All interpolation methods will calculate RMS during crossvalidation, and it has the same meaning for all interpolators, so it can be compared across the board to decide which interpolation method (and which parameters) to use. Theoretically, the kernel parameter can take any real number, but computer limitations keep the possible range between +/- 1.79769 x 10^308. ... View more

In the areas where you have license information, do the points line up with the store type points?  Can we assume the license information is complete in the areas that you have them, or are there still locations that sell tobacco legally that aren't in the license data? You can find the proportion of license points that correspond to store locations.  You can also find the proportion of stores that are not in the licensed dataset.  If the first proportion is very high, and the second proportion is very low, that's a good indicator that store type can be used as a proxy for licensed tobacco sellers. ... View more

Did you navigate to the folder in Windows Explorer, ArcCatalog, or from "Add Data" in ArcMap? If you tried to use Windows Explorer, you'll run into problems.  To add feature classes from a geodatabase, you need to navigate within ArcMap, either through ArcCatalog or from "Add Data". If using Add Data or ArcCatalog still shows that the geodatabase is empty, please contact customer support: https://service.esri.com/index.cfm?f...er_serviceform Good luck. ... View more

Most parameters apply only for that particular interpolator.  The Power in IDW, for example, has no analogous parameter in any other interpolators. Global Polynomial Interpolation, Local Polynomial Interpolation, and Kernel Smoothing are all based on polynomials, so the Order of Polynomial means the same thing in each tool.  Additionally, LPI and KS share Kernel types because they're based on local kernels. The only parameters that are shared across the board are related to the searching neighborhood (except when you introduce barriers). The domain of each parameter depends on what the parameter does (for example, the range in kriging has to be greater than 0).  These domains are all documented, and if you try to use a value outside that domain, the software will throw an error telling you that it is outside the parameter domain. You should always consider trend removal and transformations, but that doesn't mean you have to use them.  Only use them if they help create a better model (which you can judge with crossvalidation statistics). ... View more

You'll have to set up a classified manual renderer on the first layer.  You can specify the break points between colors.  On the Symbology tab, click "Classified" on the left, then click the "Classify..." button.  Decide how many classes you want, then set the classification method to Manual.  You can then manually change the Break Values on the right to define the intervals.  Click OK.  You can then change the color ramp, or define your own colors for each classification interval. Once you get all this manually set up on one raster, import the symbology to the rest. ... View more

Are those images of rasters or geostatistical layers? Either way, you can right-click the layer and choose "Properties" then go to the Symbology tab.  You can manually set the classification range in this tab.  Once you get one of the layers how you want, you can import the Symbology to other layers.  To do this, again, open Properties on a second layer and go to the Symbology tab.  There will be an Import button where you can carry over the symbology from the first layer.  Do this for all 12 layers. The interface is a little different for rasters and geostatistical layers, but the idea is the same: manually set the symbology on one layer, then import that symbology to all the other layers. If you have any difficulties, let me know and I'll try to help. Also, you might find this blog helpful; it explains the difference between a raster and a geostatistical layer: http://blogs.esri.com/dev/blogs/geoprocessing/archive/2008/09/29/understanding-geostatistical-analyst-layers.aspx ... View more

https://service.esri.com/index.cfm?fa=homepage.feedback.customer_serviceform If you ever need to contact customer support, that page has the info. ... View more

The biggest problem I see is that you're trying to model two different processes: one for areas outside of the canopy area, and one for areas within the canopy area.  Kriging assumes that there is only one underlying process, so it isn't going to work. My first thought is analyze the two processes independently.  First, use kriging to predict the hardness for all areas that are not under a tree canopy.  Second, build some kind of linear model for the areas under a canopy.  You could use distance from the tree and the tree diameter as predictor variables for the snow hardness.  With some work, you might be able to use the Ordinary Least Squares tool in the Spatial Statistics toolbox to perform the second analysis.  There are some problems with that approach (like assuming the two processes are independent, and mixing a spatial model with a nonspatial model), but that's the only technique that comes to mind.  If I think of anything else, I'll let you know. ... View more

Did you enable the extensions? To do this, open ArcMap, and open the "Customize" pulldown menu, and choose "Extensions."  Check the box next to each extension that you want to enable. If that isn't the problem, please contact customer support. ... View more