## 6 Conclusions  
Co-kriging is an advanced interpolation method that supports additional evaluations of datasets and limits the influence of factors, such as data anisotropy, data trends, and nugget effects. These considerations are important in analyses of dispersed data (GNSS) that are affected by a station's geological configuration and \"artificially\" adjusted leveling data.  
The selection of interpolation parameters is largely subjective. The interpolated surface was non-stationary in all datasets. An analysis of the correlations between the semi-major and semi-minor axes of the ellipse vs. RMS and \(\sigma\) produced satisfactory results. The anisotropic direction was correlated with the number of lags, and indirectly, with the minimum possible values of RMS and \(\sigma\).  
The leveling data were anisotropic. The nugget effect was highest for GNSS data. A high nugget effect led to smoother estimates, and consequently, an increase in the value of RMS (Figure 5).  
Numerous datasets can be combined using co-kriging. The initial dataset significantly influences the final model. Models AB, BA, DB, and DB clearly illustrated this trend. A larger amount of data in the successive set did not play a role. The results of this study confirmed that co-kriging is a non-uniform method and that the same initial datasets could produce different models. The mean standard error was lowest for dataset E (combined adjustment of vertical movements based on leveling data and GNSS data).  
Models of the vertical movements of the Earth's crust generated using co-kriging with the use of varied datasets (method and time of measurement) have different isolines. From a statistical point of view, models based on simultaneously adjusted datasets best fit the data. Models generated from data that are merged into a single set are more correlated with the reference models than those based on datasets that are combined using co-kriging. The merger of anisotropic and isotropic datasets produces isotropic datasets. The use of data from various sources can limit the influence of anisotropy on the model's quality.  
The anisotropic directions, data trends, and nugget effects have to be taken into consideration in cartographic models of vertical movements. The application of a model based on GNSS data only will not produce reliable results in analyses evaluating the effects of glacial isostatic adjustment (GIA) (Figure 8). This study demonstrated that co-kriging did not produce satisfactory results when datasets relating to the vertical movements of the Earth's crust were combined. Several networks had to be simultaneously adjusted before the analysis to produce the desired outcome.  
In the future, attempts will be made to determine the causes of the above-average standard error of the estimate in the analyzed datasets.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **AB** & **BA** & **BD** & **DB** \\ \hline Mean standard error & 0.69 & 0.85 & 1.60 & 4.66 & 1.23 & 2.31 & 2.05 & 2.11 & 0.94 & 1.35 & 4.71 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard error of the estimate for the analyzed datasets.