## 2 Materials and Methods  
The methodological innovation in this study lies in its shift from traditional unemployment rate analyses, which typically focus on identifying causal factors, to an approach prioritizing the spatial representation of the phenomenon. The study employs exploratory data analysis (EDA) as the foundation for spatial analysis, first testing the null hypothesis of randomness to determine whether unemployment exhibits significant spatial patterns. If the results confirm that unemployment is not randomly distributed, the application of spatial modeling techniques is justified instead of simple visualizations of the average unemployment rate based on administrative boundaries. Interpolation methods, particularly Kriging, were used to create a continuous spatial surface from discrete data, optimizing model selection based on approximation errors compared to actual measurements. This innovative approach enhances precision in representing unemployment distribution, offering a more accurate and detailed spatial depiction of the phenomenon. The unemployment rate data analyzed in this study were obtained from the official statistics provided by the Polish Central Statistical Office (GUS). The data are calculated using the classical method, based on the official Eurostat definition, ensuring consistency and comparability with other European statistical data. According to the official statistics of the Polish Central Statistical Office (GUS) and Eurostat, the unemployment rate is defined as the ratio of the number of unemployed individuals actively seeking work to the number of economically active individuals. This definition is widely accepted in economic research and ensures comparability with international studies. The dataset covers the period 2004-2022 and is publicly available in the Local Data Bank ([[https://bdl.stat.gov.pl/BDL/start](https://bdl.stat.gov.pl/BDL/start)]([https://bdl.stat.gov.pl/BDL/start](https://bdl.stat.gov.pl/BDL/start)), 20 January 2025) and the Geostatistical Portal ([[https://geo.stat.gov.pl/](https://geo.stat.gov.pl/)]([https://geo.stat.gov.pl/](https://geo.stat.gov.pl/)), 20 January 2025), as well as in the annual Statistical Yearbooks published by GUS. This ensures the reliability and transparency of the input data used for spatial analyses presented in this paper. According to [PERSON], this is the most commonly used measure of unemployment, defined as the ratio of the number of people who are unemployed but actively seeking work to the number of economically active individuals [30]. In the literature, numerous studies focus on modeling unemployment changes in Poland [31], which can be classified into those utilizing spatial analyses and those employing GIS for visualizing the results of econometric research. All of these studies address the spatial variation of unemployment levels in Poland [32,33], with some specifically dedicated to the application of spatial analyses [34]. Spatial models extend classical econometric models by incorporating so-called spatial effects: spatial dependence and spatial heterogeneity [35,36]. Although these studies most commonly address the spatial aspect in a modeling context [37] or focus on correlations with potential factors [22,38]. The authors formulated the following research hypotheses:  
* _Traditional methods of unemployment analysis, based on administrative divisions, may distort the actual spatial structure of this phenomenon, justifying the need for spatial analysis methods._
* _Exploratory data analysis (EDA) facilitates the optimal selection of modeling and visualization methods for unemployment rate analysis, improving result interpretation accuracy and enhancing its application in labor market policy._
* _The application of spatial analysis techniques allows for a more precise identification of unemployment distribution patterns, enabling better-targeted policy interventions and resource allocation in labor market planning._  
The data modeling process applied in this study follows a universal scheme (Figure 1).