## 1 Introduction  
The purpose of this article is to model the unemployment rate, which gives a good indication of the level of socio-economic development of regions. A spatial description of unemployment as a description of negative socio-economic phenomena is an important task of spatial econometrics, while modeling economic processes must be characterized by spatial dependencies [1]. The spatial analysis of unemployment rates has been widely studied, utilizing various modeling techniques [2, 3, 4]. A spatial approach requires the use of exploratory data analysis (EDA) and based on the results of this analysis, selecting the most appropriate visualization methods. This problem has been described in foreign literature [5, 6, 7, 8] and in Polish literature [9, 10]. EDA is the basis for making decisions about the choice of space-modeling tools [11]. According to [PERSON] [12], most of the models presented in the literature (see the comprehensive review in [13]) discuss the existence of unemployment disparities, but do not necessarily relate them to geography. Therefore, in [PERSON]'s research, [PERSON]'s I test is calculated to examine the role of location in shaping the distribution of unemployment. The investigation of spatial clustering effects also requires an analysis of the spatial distribution of unemployment rates [14], as well as the application of statistical tests of spatial dependence to assess the non-random distribution of neighboring regions, as emphasized by the authors. Studying regional unemployment rates necessitates demonstrating positive spatial autocorrelation, revealing clusters of geographically adjacent regions with similar unemployment levels through the use of [PERSON]'s I test [15]. Understanding the persistence of regional unemployment requires the consideration of spatial heterogeneity [16]. Regional models that incorporate spatial factors often provide a deeper insight into unemployment trends, particularly in areas affected by labor market transformations [17]. The analysis of spatial patterns in economic activity helps identify factors that influence regional unemployment rates [18]. In practice, [PERSON]'s I tests are applied to both large [19] and small samples [20], and spatial autocorrelation indices provide evidence of spatial interactions between regions, which must be accounted for in the regression analyses and spatial modeling of unemployment rates, for instance, by using [PERSON]'s scatterplots [21; 22]. To analyze and visualize spatial patterns, researchers employ Local Indicators of Spatial Association (LISA) statistics [23] and Getis-Ord Gi* statistics [24; 25], including their extensions for spatio-temporal analysis [26]. Visualization tools such as choropleth maps and cartograms are used, taking into account the heterogeneity of administrative units, both for regression models and for representing unemployment rates [7; 27; 28]. Additionally, Kernel Density Estimation (KDE) is used for spatial visualization [29]. In this publication, the type of distribution, characteristics, and statistical data such as the unemployment rate for various locational (spatial) approaches will be examined and determined. For this purpose, an exploratory analysis of data will be used, in GIS software (ArcGIS Pro 3.4), in regards to the unemployment rate measured in provinces, districts, and cities in Poland. Then, following the analysis of statistical test results, appropriate spatial modeling methods will be selected and applied. he aim of the article is to examine the unemployment rate in Poland as statistical data and its spatial analysis at the provincial level for the period 2004-2022, and at the district and city level in 2021 using statistically justified data modeling tools in GIS.