## 2 Materials and Methods
In statistical tests, on the other hand, the analysis is more likely to result in clusters whose size, shape or distribution cannot be influenced by the analyst because they are formed automatically according to the algorithm assigned to the analysis technique. If the null hypothesis with a random distribution of data is confirmed, then cartograms, diagrams, graduated, and proportional symbols will be made, while if the null hypothesis is rejected, data interpolations and the optimization of models will be performed using the parametric estimation quality assessment method. In addition, hot spot analyses and density maps using the Kernel method will be performed. A cartogram represents a phenomenon within the boundaries of a given territorial unit by color, shade or hatching. Depending on the situation and the intention of the analyst, this fact can make the cartogram overly general, which can be seen as a disadvantage. Another disadvantage of the cartogram is the freedom to choose a scale that can change the appearance of a given phenomenon [46]. Pie charts are often used to represent the overall value of a phenomenon (expressed in the area of a circle) and the percentage of its components, giving the reader a quick idea of the proportional distribution of data [47]. In the next step, maps will be performed using geostatistical methods. In this work, maps will be carried out using the Kriging method. Each measurement z(x) is the implementation of the function of the random variable Z(x) (where x- means the location in the area), i.e., if the measurement was performed several times at point x, the measurement results will be different. However, in any pair of points xn and xn + h, the random variables Z(xn) and Z(xn + h) are bound by a certain correlation relationship resulting from the spatial continuity of the studied phenomenon [48]. Data modeling is carried out using the semivariogram analysis. The course of the semivariogram function indicates at what rate the interaction of variables decreases with the increasing distance [49]. In terms of spatial statistics, measurement values are treated as a regionalized variable (also called spatial or localized), which is defined as a continuous coordinate function of space. The values of this regionalized variable are known only at the place of measurement, and there are a few of them compared to the studied space. In order to be able to perform a statistical interpretation on a regionalized variable, certain restrictions are imposed on it. In particular, the hypothesis of a weak stationary is assumed, which means that the expected value of the variable does not depend on the place of measurement and its covariance is only a function of the distance between the measurement points. In practice, because the hypothesis of a weak stationary in relation to real parameters is very strict, a much milder limitation is applied, which assumes that poor stationary is not from the regionalized variable itself, but from its increments. Of significant importance here is the variance of increments, which defines the basic characteristic function of geostatistics, known as the semivariogram [50]. In addition, when modeling a space using a semivariogram, the condition of positive definiteness must be met, or more precisely, the semivariance function must meet this condition. This mathematical condition is met by the function families used in ArcGIS PRO (Nugget effect model, Spherical model, Gaussian model, Power model, Exponential model, and Linear model). In these function models, the Kriging function equations can be solved. The indicators that were considered when comparing the results of spatial analyses are the statistical characteristics of the interpolation error:1. Mean prediction error (MSE);
2. Root mean square prediction error (RMSE);
3. Average standard error (ASE);
4. Mean standardized prediction error (MSE);
5. Root mean square standardized prediction error (RMSSE).