Hi,

I am using geostat analysis to particularly kriging to interpolate my data but I cant seem to get a better result. I am new to kriging and geostat and am doing some readings and research about it but its taking so much time that I cant finish the interpolation as early as planned.

I would highly appreciate if anyone could give me an advise on this. The data that I am interpolating is bimodal and I wonder if there is an appropriate procedure for this. Thank u so much for any advise you can give.

Eif

I am using geostat analysis to particularly kriging to interpolate my data but I cant seem to get a better result. I am new to kriging and geostat and am doing some readings and research about it but its taking so much time that I cant finish the interpolation as early as planned.

I would highly appreciate if anyone could give me an advise on this. The data that I am interpolating is bimodal and I wonder if there is an appropriate procedure for this. Thank u so much for any advise you can give.

Eif

Thanks for the help Eric.

I got a result showing a high root mean square (239) and average standard error (234.16), mean error is also high (-7). I wonder if there is a technique to reduce these. I have tried changing the model type like Hole effect and other types hoping to at least reduce the errors but they are all resulting to high error values. Would there be some other trick here to reduce the error? My apology as I am not familiar with this method. I would be glad if there is any procedure on how to reduce errors in this type of processing.

I would highly appreciate any help on this. Thanks.

Best regards,

Eif

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Have you tried the Optimize Model button at the top? Try using K-Bessel or Stable semivariogram types, then press the optimize model button. Also, try changing Variable to semivariogram and optimize the model again.

If that doesn't work, you may want to try to remove trends (the option appears on the Wizard page right before the semivariogram).

The datasets that I am working with has a trend and the plan is to remove it. The trend is a U shape which can be removed using the second order polynomial. However, I have confusion in identifying the directional influence in the datasets. For instance, in page 104 of the manual of geostat analyst (Which I just found and is very useful indeed), the image (attached here) shows a strong influence on the southeast to northwest. I wonder how the strength of directional influence was detected. X axis is west-east (left-right) direction while Y-axis is the north-south (up-down) heading. Directional info on the image says: Location - 30 degrees; Horizontal - 120 degrees; Vertical - -27degrees. Please kindly elaborate on this. Thank you.

Regards,

Eif

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When you use trend removal in the Geostatistical Wizard, these directional trends will be automatically detected using local polynomial interpolation, and it will do its best to remove them before fitting the semivariogram.

One caveat is that it is often difficult (if not impossible) to differentiate trend, autocorrelation, and anisotropy. They can all present themselves in ways that look identical.

Does that answer your question?

Pardon me for the many questions, but it's actually taking me months already before I can finalize the kriging procedure as I am doing also some readings and research about the geostat and variograms. I am new to this geostat and semivariogram stuff. I don't want to give up on these and am keen to learn how I could minimize the errors so I could end up with the very good kriging results.

Cheers,

Eif

ERRATA ....OR WE NEED TO DO SOME FURTHER MODIFICATIONS...

Eif

Cheers,

Eif

For investigating stationarity, I suggest using the Voronoi Map ESDA tool with Type set to Entropy or StDev. One advantage of the Voronoi Map is that it works with quantiles, so it's nonparametric. Local Moran's I comes with distributional assumptions.

Thanks for the reply Jevans. I have not checked yet for the "stationarity" of the dataset. The dataset is actually annual growth of trees. Pardon me but I am not yet that well verse in GIS processing. What is LISA model?

Thanks,

Eif

Thanks Eric. I am not familiar with how the Voronoi map works and this is sort of abstract to me. Is there a paper on how this works? I have attached the image of the voronoi for both entropy and stDev and I am confused on how to interpret it.

Any help is highly appreciated.

Thanks,

Eif

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He is referring to this geoprocessing tool:

http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#//005p0000000z000000.htm

If your data is stationary, you expect to see randomness in the colors of the Voronoi polygons when they're symbolized by entropy or standard deviation. The idea is that the local variation should be roughly constant across the surface; you should not have areas with much more erratic data than others.

Looking at your two Voronoi maps, it looks like you have some nonstationarity, but it doesn't look very drastic. The StDev symbolization seems more clustered, but the Entropy symbolization doesn't look too bad, and I prefer to use Entropy when looking for stationarity.

If you want, you can send your data to ekrause@esri.com, and I'll see if I can fit a good kriging model.

I have sent you the file via email. I highly appreciate the help.

Best regards,

Eif

I am curious to why you prefer entropy as a measure of nonstationarity? Since the the empirical semivariogram is based on binned variance/2 and the standard model of nonstationarity is based on mean and variance, would not the standard deviation better represent how this assumption is violated?

Without looking at the data this is pure conjecture, but I am wondering if the data is limited to two distinct modes is there a possibility that it is assuming a binomial form? There is a possibility that indicator Kriging may be more appropriate. Since indicator Kriging is nonparametric, how sensitive is it to nonstationarity and distributional assumptions, compared to the linear form?

I took a look at the data, and it isn't binomial data. I couldn't fit a good kriging model to the data in the graphics, but I found a seemingly good model using Kernel Smoothing.