## 4 Discussion and Conclusions  
The innovativeness of the proposed approach lies in the application of spatial analysis methods to the unemployment rate calculated by classical methods at the city level, instead of relying solely on traditional averages computed for administrative units. The conventional approach, widely used in spatial studies, often leads to data oversimplification, resulting in artificially homogeneous representations of socio-economic conditions. However, the analyses conducted in this study clearly demonstrate that administrative areas are, in fact, spatially heterogeneous, and this local variability should be properly considered both in scientific research and in policy-making processes. The proposed methodology, combining classical unemployment rate calculation with spatial analysis techniques, allows this spatial heterogeneity to be captured. Furthermore, the data modeling scheme described in the article, based on exploratory data analysis (EDA), ensures that the selection of visualization and modeling methods is statistically justified. This enables spatial interpolation, allowing for the estimation of unemployment rates in towns and cities where direct measurements are unavailable. Importantly, statistical tests confirmed the reliability of these estimations, since the spatial distribution of the unemployment rate exhibits clear patterns rather than randomness. An additional benefit of this approach is the identification of local anomalies and spatial outliers, which can be of particular relevance for labor market policies and regional development strategies. The conducted analyses confirmed that spatial analyses applied to unemployment rate data at the city level provide a much more detailed and reliable picture of spatial disparities than traditional approaches based on aggregated administrative units. [PERSON]'s I test showed a statistically significant spatial autocorrelation, justifying the use of spatial statistics. In contrast, when using polygon data for provinces or districts, descriptive  
Figure 13: Modeling the unemployment rate in Poland in 2021 using the EBK method. statistics may be more appropriate. This highlights a key methodological contribution of this study--demonstrating that spatial unemployment patterns within administrative units are often masked when only averaged values per unit are considered. Moreover, the removal of artificial administrative boundaries in the visualization process allows for more accurate spatial targeting of anti-unemployment policies. This refined approach could help allocate financial support more precisely, directing funds not to entire administrative units, but to the specific cities and subregions where interventions are most urgently needed. This practical application further highlights the relevance of integrating spatial statistics and classical economic indicators in labor market research. The conducted analyses confirmed hypothesis 1, demonstrating that traditional methods of unemployment analysis, based on the aggregation of administrative data, can distort the actual representation of this phenomenon. Hypothesis 2 was also confirmed, whereby exploratory data analysis (EDA) enabled the optimal selection of modeling and visualization methods, revealing spatial unemployment patterns that were not detectable through traditional statistical analyses. Furthermore, the results validated hypothesis 3, indicating that the integration of GIS-based spatial analysis methods with classical economic indicators allows for a more precise identification of areas requiring intervention, emphasizing the crucial role of spatial analysis in modeling socio-economic phenomena. The presented approach to spatial unemployment analysis can be applied in a wide range of socio-economic studies, including labor market monitoring, modeling the impact of transportation accessibility on employment, assessing the effectiveness of activation policies, and optimizing the allocation of support funds in regions with the highest unemployment risk.