## 2 Materials and Methods
The results of EDA determine the further selection of visualization methods and spatial analyses. This approach ensures that the applied tools are statistically justified and aligned with the nature of the data. Unfortunately, this methodological rigor is not always maintained, and the validation parameters are often omitted, which significantly reduces the credibility and reliability of such data. The first stage is to perform a spatial autocorrelation analysis (using Global Moran's I). Next, the nearest neighbor analysis is applied to evaluate spatial clustering tendencies. Data distribution analysis, using charts, histograms, and QQ plots, helps assess whether the data meet normality assumptions. Finally, outlier detection is performed using Thiessen polygons and Voronoi diagrams. The visualization and modeling method is selected based on the results of these analyses. If the null hypothesis of random data distribution is confirmed, the recommended visualizations include choropleth maps, diagrams, graduated symbols, and proportional symbols. However, if the null hypothesis is rejected, spatial interpolation techniques (both geostatistical and deterministic) can be applied without methodological concerns. Additionally, model optimization is performed using the parametric estimation quality assessment method (MPQE), and advanced spatial analyses such as the hot spot analysis and Kernel Density Maps are recommended to identify significant spatial patterns. A spatial dependency analysis was conducted in ArcGIS Pro, and appropriate methods for visualizing the unemployment rate were selected based on the results of EDA, which comprises a set of statistical techniques used to characterize observations exhibiting spatial relationships. In the first step, the EDA will be performed using a spatial autocorrelation analysis (Global Moran's I). This is based on the binary matrix of weights, and the significance of Moran's I statistic is carried out by means of a test in which the following hypotheses are verified [39]:  
**H0**. : _Spatial autocorrelation does not occur._
**H1**. : _There is a spatial autocorrelation._  
In the second step of the EDA, the average test of the nearest neighbor will be performed. If the index is less than 1, the pattern shows grouping; if the index is greater than 1, the tendency is towards dispersion or competition [40; 41]. In the third step of the EDA, the type of data distribution instruments of the histogram and QQ diagram will be examined. Understanding data distribution is an important step in the data-mining process [42]. For comparison, you can add a normal distribution overlay to the histogram [43]. Normal QQ plots, like histograms, make it possible to study the effect of logarithmic transformations and square roots on the distribution of data while comparing them with the normal distribution [44]. In the fourth step of the EDA, outliers will be tested using Thiessen polygons. Each Thiessen polygon contains only one point entry function. Each location in a Thiessen polygon is closer  
Figure 1: Data modeling diagram. to its associated point than to any other input function of the point. The resulting Voronoi diagrams are used to determine the outliers. A global outlier is a measured sampling point that has a very high or very low value relative to all values in the dataset. A local outlier is a measured sample point that has a value within the normal range for the entire dataset, but if you look at the surrounding points, it is unusually high or low [45]. The next stage of work will be the selection of spatial statistics tools for visualization. Statistics are divided into descriptive and inferred statistics (statistical tests). In the case of descriptive statistics, classification depends more on the choices and methods of the analyst, rather than the results of statistical tests.