## 4 Discussion and Conclusions
Additionally, the proposed methodology enhances the adaptability of labor market analyses by allowing for dynamic updates as new data become available, ensuring that policy responses can be more timely and data-driven. By integrating spatial interpolation with exploratory data analysis, this approach also provides a robust framework for detecting emerging labor market trends and forecasting potential shifts in unemployment distribution. Furthermore, the combination of classical economic indicators with spatial modeling opens new possibilities for comparative studies across different countries and regions, facilitating cross-border labor market assessments. The flexibility of this method makes it suitable for analyzing not only unemployment, but also other socio-economic phenomena, such as income disparities, workforce migration, and regional economic resilience, providing a comprehensive tool for decision-makers and researchers alike. Conceptualization, M.O. ; methodology, M.O. ; software, M.O. ; validation, M.O. ; formal analysis, M.O. ; investigation, M.O. ; resources, M.O. ; data curation, writing--original draft preparation, M.O. and M.J.; writing--review and editing, M.O. and M.J.; visualization, M.O. and M.J.; supervision, M.O. and M.J.; project administration, M.O. ; funding acquisition, M.O. and M.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not new data were created or analyzed in this study. The authors declare no conflict of interest. * (18) [PERSON] Spatial modelling of disparity in economic activity and unemployment in southern and oromia regional states of Ethiopia. _Am. J. Theor. Appl. Stat._**2015**, \(4\), 347. [CrossRef]
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