I have two datasets that have the same information (point values of groundwater level). However one is of 2007 and the other of 2001. I would like to know if there is a method, tool or way to prove statistically that these datasets can be joined to be one or a way that I can prove that they can't be joined.

More specifically, I have 46 points in one data set that intorpolating gives me one map and another with 60 points that give me another map. The points are all in different locations. Ideally I would like to join the 106 points into one data set in order to create a more accurate interpolation. But to do that I would have to prove statistically that these points are from the same population. In common statistical analysis there are several tests that could be done. However with geostatistics I am not sure how to proceed.

Any help in the form of advice, pointing to a previous post, a guide or a solution would be greatly appreciated.

Thank you

More specifically, I have 46 points in one data set that intorpolating gives me one map and another with 60 points that give me another map. The points are all in different locations. Ideally I would like to join the 106 points into one data set in order to create a more accurate interpolation. But to do that I would have to prove statistically that these points are from the same population. In common statistical analysis there are several tests that could be done. However with geostatistics I am not sure how to proceed.

Any help in the form of advice, pointing to a previous post, a guide or a solution would be greatly appreciated.

Thank you

Even if the two datasets do have identical statistical properties (implying you can safely merge them), you won't lose much by performing cokriging compared to merging the datasets and performing univariate kriging. The only disadvantage is that you will have to estimate two semivariograms and one cross-covariance curve rather than just one semivariogram.