topic Lag size in ArcGIS GeoStatistical Analyst Questions

Lag size

MusfiraJamil — Wed, 13 Feb 2019 20:32:37 GMT

Hi,

Is there a way to select different lag size for a covariate in cokriging? or I can only use the same lag size as that for primary variable?

Re: Lag size

EricKrause — Wed, 13 Feb 2019 20:55:32 GMT

The Lag Size is shared between the primary and secondary variables in cokriging. You cannot set different values for each of them.

However, the lag size is only used in order to estimate the semivariogram parameters (range, nugget, partial sill, etc). The actual interpolation does not directly depend on the lag size. This means that you can experiment with lag size for the primary variable, then note down all of the parameters like range, nugget, sill for a lag size of your choosing. Then switch to the secondary variable and find semivariogram parameters using a different lag size. Note down those parameters, then manually type them all simultaneously.

Re: Lag size

MusfiraJamil — Wed, 13 Feb 2019 22:30:18 GMT

Thank you. I will give it a try.

Another question; what exactly is model 1, 2 and 3. As per my

understanding, I think they represent semivariogram for primary, cross

variogram, and secondary variable, respectively.

Re: Lag size

EricKrause — Thu, 14 Feb 2019 15:04:20 GMT

The "Model #" controls don't refer the primary/secondary dataset. They're actually used to mix several semivariogram models together into a single new semivariogram. For example, you can use the Stable model (which is the default), or you can change it to, for example, Spherical. But you can also create a new semivariogram that is an average between Stable and Spherical (weighted averages of valid semivariograms are themselves valid semivariograms) by providing one semivariogram into Model 1 and the other into Model 2 (and even a third into Model 3). This feature isn't often used, but my understanding is that it is useful in situations where the data are affected by two different processes, one short-range and the other long-range. In that case, you can mix one semivariogram with a short range with another semivariogram with a long range, and the resulting average will usually be better than either of the components individually.

As for how to tell the difference between the primary and secondary datasets, look for "Var 1" and "Var 2" (primary and secondary). For example the semivariogram display for "Var 1 - Var 1" shows the semivariogram for the primary variable. "Var 2 - Var 2" shows the semivariogram for the secondary dataset. "Var 1 - Var 2" shows the cross covariance. There will be similar "Var #" labels for the parameters on the right to distinguish between the three models.