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?
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
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.
