cannot find kriging geostatistical layer after kriging

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03-13-2015 01:20 AM
SuiTao
by
New Contributor II

hi everyone, I created a simple model builder that iterates through several point shapefiles and conducts kriging (empirical bayesian kriging) to each set of the points. However, after conducting the model, I cannot find the resulting geostatistical layer in my previous designated folder (or a gdb). So where do I actually find these results?

many thanks,

Sui

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SteveLynch
Esri Regular Contributor

The geostatistical layer is in-memory and can only be persisted via the SaveToLayer geoprocessing tool or saved in the Map document (.mxd).

-Steve

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6 Replies
JayantaPoddar
MVP Esteemed Contributor

Hi Sui,

Instead of defining output geostatistical layer (out_ga_layer), you could define the output raster dataset (out_raster) in Empirical Bayesian kriging tool. This raster dataset will be stored in the desired GDB.

Thanks,

Jay



Think Location
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SuiTao
by
New Contributor II

Hi Jayanta,

Thank you for the reply. And sorry for not being thorough. The reason I want to keep geostatistical layer is that I want to use another tool, that is GA layer to points later to spatially join the kriging results to another point features. Not sure whether I can do that with raster. Please inform me if there is a way to achieve what I want?

Best,

Sui

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JayantaPoddar
MVP Esteemed Contributor

If you already have the point layers with you that overlay on the same extent as the above interpolated raster dataset, you could use extract values by points to extract the cell values to the point features.



Think Location
MonicaTamayo
New Contributor

The use of Enrich layer could improve the quality of the analysis which has been done

SteveLynch
Esri Regular Contributor

The geostatistical layer is in-memory and can only be persisted via the SaveToLayer geoprocessing tool or saved in the Map document (.mxd).

-Steve

SuiTao
by
New Contributor II

Jayanta, Monica and Steve, thanks a lot for the helpful reply. But Steve's answer is the closest one.

Cheers,

Sui

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