Kriging Problem!

Question

I used the same rainfall data to do ordinary kriging in arcmap 9.3 and arcmap10. 
In arcmap 9.3, the default parameters give a better predition map than the optimized parameters in arcmap10.
Does anyone know why this happens?

Eric6346 · Answer

Hi Steve,

Could you please a brief explanation about "optimize model "  in Arcgis 10 geostatistical analysis.

Is it sufficient to choose this option to provide best map to the user.

In kriging, the optimize button uses weighted least-squares.  The algorithm iterates through the range and fits the nugget and sill using weight least-squares for each iteration.  It then calculates the combination with the minimum root-mean-square.  Because the covariance matrix between the three parameters is not estimated, this process will not necessarily find the global root-mean-square minimum.  If you play with the parameters enough, you may find a better root-mean-square.

Looking at your cross-validation statistics, you can justify either model.  Your model has a better root-mean-square, but it has a worse mean prediction error and rmse-standardized.

Fevziakbas · Answer

Hi Steve;
here is an  example.
Surface soil clay content semivariogram modelling and cross validation result.
Model :spherical
Model  -------------     Mean Pre. Err.-------- Root Mean. Sq.  -----    Root M sq Stand.
1 (optimized by program)     0.03453  --------     7.521  ------------ 1.036
2 (my semivariogram model)   0.0556 ---------  7.437  ------------  1.0741

Which one I should choose?

Fevziakbas · Answer

Hi Steve,

Could you please a brief explanation about "optimize model "  in Arcgis 10 geostatistical analysis.

Is it sufficient to choose this option to provide best map to the user.