## 3 Results  
The research process begins, as shown in Figure 2, with exploratory data analysis (EDA), which serves as the foundation for selecting further spatial analysis and visualization methods. Figure 2 shows the results of [PERSON]'s I test for data on the unemployment rate broken down by voivodship. Figure 2: [PERSON]’s I test report for the unemployment rate in voivodships. Data for the period 2004-2022 were verified. In each case, the result was the same, namely, confirming the truth of hypothesis 0. From this, it follows that there is no autocorrelation of data (random distribution). Figure 3 shows the results of [PERSON]'s I test for data on the unemployment rate broken down by district. For data on the unemployment rate in counties, the null hypothesis was also confirmed, which means that the data are distributed randomly. Also, in this case, no data autocorrelation was found, which limits the possibilities of data visualization using inferred spatial statistics. Figure 4 shows the results of [PERSON]'s I test for urban unemployment rate data. Figure 4: [PERSON]’s I test report for the urban unemployment rate. Figure 3: [PERSON]’s I test report for the unemployment rate in district. Based on Figure 4, we conclude that the data are clustered, and therefore the null hypothesis about the randomness of their distribution should be rejected. In order to examine whether the distribution of data on the unemployment rate is random or not, a test was carried out for the point data of the nearest average neighbor for the city of Poland. Figure 5 shows the results of the nearest neighbor average test. Based on Figure 5, we conclude that the null hypothesis is rejected. A result of 4.601594 was obtained, from which it was confirmed that there is a probability of less than 1% that the scattered samples can be the result of a random variable. In the next stage, the distribution of data was examined. Figure 6 presents the histogram for data on the unemployment rate in cities. Figure 5: Report from the test of the nearest average neighbor for the unemployment rate. Figure 6: Histogram of the unemployment rate in cities. During the study, it was established that parameters close to the normal system are achieved when a logarithmic transformation is used. This is an important indication during geostatistical modeling that should be considered in the process of space modeling. In addition, if the subset validation method is used for the map validation process, it should be examined whether the distribution of data will change significantly after taking the data into the test set. The histogram for data without a test set is shown in Figure 7.  
Based on Figure 7, it can be concluded that performing a logarithmic transformation has brought the data distribution closer to a normal distribution. Therefore, during modeling, the analyst should take this information into account and, particularly when using the Kriging method, apply the logarithmic transformation in the geostatistical wizard. The next step of the EDA is to examine the outliers based on Thiessen polygons and the analysis of the Voronoi diagram, as shown in Figure 8.  
Based on Figure 8, we conclude that there are reflective values of the percentage rate. These data should be carefully analyzed, as there is a higher probability of measurement  
Figure 8: Diagram of Voronoi unemployment rate in cities. Figure 7: Histogram and the unemployment rate in cities. error. However, for a data type such as the percentage rate, the possibility of a spike in distribution is likely; therefore, none of the data will be removed in the main study. Based on the results of research in EDA, data on the percentage rate for the area of districts were presented in the form of a diagram in Figure 9.  
Figure 9 confirms the conclusions drawn from the EDA that assigning statistical characteristics to large areas and drawing strong conclusions on this basis flattens the perspective and the reliability of results.