Geostatistical analyst layer

[ErnestDunwoody]

can any one tell me how to create a geostatistical analyst layer?

Sorry to have to ask such a dumb Q but ArcMAP help was helpless, yet again, on this topic.

Using the Create Geostatistical layer Toollbox requires a Geostatistical model source and an input layer.

What is this Model source? A misleading name for an ordinary layer or is it something else? Where do you get or create it?

The next box wants an Input dataset but I can't get anything to go in there, probably because the Layer I put in the first box doesn't have what it is needed. 

Sure would be helpful if the Arc Map tool help files contained helpful information.

Sincerely,
edi151

[EricKrause]

Geostatistical layers are fundamentally tied to interpolation methods and their associated parameters.  Interpolation is generally done in the Geostatistical Wizard, and the output will be a geostatistical layer.

The Create Geostatistical Layer tool is used for creating a new geostatistical layer from an existing one.  It requires a model source because the tool needs to know which interpolation method and parameters you are using.  It sounds like you don't have an existing geostatistical layer, so the tool isn't going to work for you until you make one in the Geostatistical Wizard.

If you tell me what exactly you're trying to do (some kind of interpolation, I assume), I can provide more help.

[XIANWANG]

Hi, Eric

When I read your post, I am confused about these two layers, what is the difference between two layers: the existing geostatistical layer and the new one, which we create using the Create Geostatistical Layer tool. I mean we already created on using Geostatistical Wizard, why we need to use Create Geostatistical Layer tool to create a new one.

Thank you.

[EricKrause]

The key is that you can apply the interpolation parameters to a new dataset.  Indeed, it doesn't make much sense to apply it to the same data (this would just make a copy of the layer).  Here is a typical scenario that shows how this tool can be useful:

You have a monitoring network set up to take daily measurements of pollution levels.  Each day, these sensors measure the pollution and store it into a database, and each day you want to create an interpolated map of pollution levels.  On the first day, you take a lot of time to create a quality kriging model in the Geostatistical Wizard and output a geostatistical layer.  On the next day, you can use this layer as the model source in Create Geostatistical Layer and apply the kriging parameters to the measurements of the new day.  By reusing the original geostatistical layer for the data of each new day, you can fully automate this process and not have to manually go into the Geostatistical Wizard every day to create your map.

[XIANWANG]

Thanks a lot. Here I have a question. How can I apply the geostatistical layer which created by Create Geostatistical Layer to the measurements of the new day?

[EricKrause]

You'll have to create the first geostatistical layer in the Geostatistical Wizard (or from a geoprocessing tool in the Interpolation toolset).  To interpolate a new dataset, open Create Geostatistical Layer and provide the first geostatistical layer as the model source.  The tool will analyze the model and decide what kinds of datasets are required.  In the simplest case, this would be just a dataset and a field.  In a more complex case like cokriging, it might require multiple datasets and multiple fields.  You then put the new data into the "Input datasets" parameter of the tool.  Give the output geostatistical layer a name and run the tool.  It will then create a new geostatistical layer that applies the parameters from the model source to the new dataset.

[XIANWANG]

Thanks a lot.

To create the template layer is to use the Geostatistical Wizard. In the Wizard, you can specify the interpolation parameters for the template layer. With ArcGIS 10, you can even use the Wizard to optimize various interpolation parameters.

1. Now if I am using Geostatistical Wizard to create a template layer, and I use optimize various interpolation parameters. Then I use this template layer as model resource to create a geostatistical layer for the measurements of the new day. These optimize various interpolation parameters also works for the measurements of the new day.

2. In Geostatistical Wizard, you optimize various interpolation parameters for the variance and semivariogram. What is the difference, and how do I know which one should be chosen.

[EricKrause]

This is difficult question to answer in an online setting.  The differences are very technical and mathematical, but the key difference is that the covariance view requires knowledge of the mean value of the kriging surface, and the semivariogram does not.  If you are unsure which one to use, I would suggest the semivariogram.

Both methods are designed to estimate covariance matrices that are used in the kriging equations, but they both do this estimation in different ways, and their "optimal" parameters will be different (though hopefully not very different).

[XIANWANG]

Hi,

When I use the Geostatistical Wizard, I set some parameters, which confused me a lot.

1. In step 2, the order of trend removal. my data include precipitation data from several years. In Some months, the data has trends, but in some data, there is no trends. When I do the interpolation, for these data, I should use the some criteria. I mean for all the data, I should choose the some parameters. Here, should I choose to remove the trend? If so, it will influence the data which don't have any trend.
2. In step 3, General Properties, there is Kerenel Function, which is a local polynomial and is a qick interpolator. And in step 4, we have semivariogran model which is also a interpolator. Can anyone tell me difference between them.

Thanks a lot!

[EricKrause]

This workflow is really only suggested when you know that the new datasets will have the same general structure.  Applying the same parameters to datasets that are different (ie, the presence and absence of trends) is not a good idea.

As for trend removal, the idea is to first do a very basic polynomial interpolation to remove the general trends, then you do kriging on the autocorrelation that is left over.  The idea is that there is a general trend in the data, but there is also correlated variability around that trend.  Trend removal takes care of the first part, and kriging takes care of the second part.  In essence, you are interpolating twice, but the first time is only for general trends in the data.