Latest Contributions by EricKrause

1.  If no transformation is applied, the underlying model is an intrinsic random function, specifically a Power semivariogram model.  If a transformation is applied, it is a simple kriging model with an exponential semivariogram. 2.  The prior distribution is estimated from the data using restricted maximum likelihood (REML).  This is the difference between "Bayesian" and "empirical Bayesian."  In empirical Bayesian models, the prior is estimated from the data, and the posterior distribution is generated through simulations. 3.  There are several reasons to think EBK will, in general, be more accurate.  First, REML is known to be a better estimator of semivariogram parameters than weighted least-squares (which is what is used in other kriging methods).  Second, EBK works on subsets, so it eases the stationarity assumption of kriging.  Other kriging methods assume global stationarity.  EBK, however, only assumes stationarity within subsets, so it can work effectively even in the presence of global nonstationarity, as long as the subsets are close to being stationary.  Third, EBK does a better job of estimating kriging variances because it can account for error in estimating the underlying semivariogram (it does this with simulations); other kriging models assume the semivariogram is modeled absolutely correctly, which often results in standard errors that are too small. Here is a reference for EBK (a longer paper with all the details is in the works): Krivoruchko K. and Gribov A. (2014) Pragmatic Bayesian kriging for non-stationary and moderately non-Gaussian data. Submitted. In Mathematics of Planet Earth. Proceedings of the 15th Annual Conference of the International Association for Mathematical Geosciences. Eds: Pardo-Igúzquiza, E.; Guardiola-Albert, C.; Heredia, J.; Moreno-Merino, L.; Durán, J.J.; Vargas-Guzmán, J.A. Springer 2014, pp. 61-64. (We're not sure why the date is 2014 because it's already been published) ... View more

This topic explains what you need to know: http://resources.arcgis.com/en/help/main/10.2/index.html#/Examples_of_using_iterators_in_ModelBuilder/00400000001n000000/ESRI_SECTION1_33EDB1C8829F4CF4BBD5DABAA3ED34DD/ ... View more

I talked this over with the developer that programmed the semivariogram functions, and we're not sure why your red bands aren't matching ours.  We suspect that you have a bug in the part of the algorithm that inflects weights that cross over the axes, but we're not sure. When you're inflecting the weights, make sure that you are rotating them rather than just mirroring them.  That could be the problem. ... View more

You don't need to do any adjustment.  Ozone is a continuous phenomenon, so its value changes everywhere.  When you extract the points from a raster, all you're doing is asking for the ozone value at the closest cell center.  You don't need to do any corrections for the size of the grid cell. Again, you can eliminate any imprecision from using the closest cell center by using GA Layer to Points.  It will calculate the ozone value at the exact point rather than looking to the closest cell center. ... View more

Remember that the vector between two points, A and B, can be defined two ways: from A to B and from B to A.  For this reason, the bins in the 1st and 3rd quadrants are identical, and so are the bins in the 2nd and 4th quadrants.  This is why the semivariogram map is symmetrical about the two diagonals. Keeping this in mind, you need to pick two quadrants to use (we use the 1st and 2nd), then you'll need to inflect any weights that fall into the 3rd and 4th quadrants.  Be careful not to double-count each pair. ... View more

Regarding the boundary cells, the description is on page 6.  Also, there's a picture of the triangular kernels in Figure 6 (in the appendix). And the averaging of the bins is the average of the semivariances of all pairs within that bin.  The trick is that there are actually two binning mechanisms: one involves gridded cells and the other uses concentric circles (Figures 2 and 3 in the appendix).  In the semivariogram UI, the red dots come from the gridded cells, and the blue crosses come from the concentric circles.  These circular bins are what are actually used to fit the semivariogram (the red dots are more for diagnostic information and for calculating the semivariogram map).  The semivariogram parameters are estimated using weighted least-squares, where the weights come from the (modified) number of points that fall within each circular bin. ... View more

Rather than converting the geostatistical layer to a raster and extracting the points, you should just use the "GA Layer to Points" geoprocessing tool.  It will bypass the creation of the raster, and merging all of the outputs will be easy. Also, you might want to consider using Empirical Bayesian Kriging (EBK) instead of Ordinary Kriging.  The results are generally more accurate, and EBK is implemented as a geoprocessing tool, so it is much simpler to automate. ... View more

You can find a full description of binning mechanism and weights in this paper: http://downloads2.esri.com/campus/uploads/library/pdfs/30583.pdf When you hover over a cell in the semivariogram map, the weight you see is the number of pairs of points that fall within that bin.  The reason the weights aren't whole numbers is that the individual weight of each pair is distributed into the surrounding bins.  For example, if a pair of points falls exactly on the corner between 4 square bins, each of those bins will get a weight of 0.25 from that pair.  If the pair falls exactly at the center of a square bin, that bin will get a full weight of 1, and all other bins will get 0 weight.  The "Weight" in the semivariogram map is the sum of all weights for all pairs of points within that bin. If that explanation wasn't clear, please read the above paper. ... View more

Glad to hear the method worked! But I want to give a bit of a disclaimer.  Looking at Surfer's documentation, their Linear semivariogram does not support a Nugget (which is kind of strange), and they default the variogram slope to 1 without setting a range or sill.  This essentially means that the semivariogram starts at 0 and increases with a slope of 1 forever (in ArcGIS, setting nugget=0 and the range and partial sill to the same very large value reproduces this behavior).  However, there is no reason to think, in general, that nugget=0 and a slope of 1 are good values.  So, the workflow I presented will reproduce the defaults of Surfer, but there is no reason to think this model actually fits your data.  You noted this in your first post, but I just want to reiterate it because it's very important. ... View more

I'm not 100% sure, but I think this will reproduce the Surfer output. Choose Ordinary kriging with a Linear semivariogram.  In advanced options, leave the lag size blank, set the nugget to 0, then set the partial sill and major range equal to each other.  Make the value of these two parameters very large; it must be larger than the distance of the diagonal extent of the input points. For the search radius, leave it as "Variable," and set the maximum number of points to the number of points in your dataset.  Leave the maximum distance empty. You'll also need to know the cell size of the raster you created in Surfer, and put that in the output cell size. ... View more

Ok, I see the problem.  We have a known issue with using spaces in output feature class names.  If you remove the spaces from the name of your shapefile, it should work. Sorry for the inconvenience, and we're working on a fix for this problem. ... View more

I cannot reproduce this problem.  Did you get this error when running GA Layer to Points geoprocessing tool? ... View more