This paper focuses on spatial aspects of variability and specifically on the relationship between regional decomposition and spatial autocorrelation. These characteristics are often supposed to be interconnected, but the subject has not yet been studied in sufficient detail and spatial methods are often neglected in regional analysis. We start with a brief discussion of a methodology suitable for identifying and quantifying spatial aspects of variability. The key part of the paper focuses on methodological reflections on measuring spatial aspects of variability and the advantages and disadvantages of our chosen methods. We use the Theil index, which is decomposable without residuum, to assess the relative importance of the regional organization of our studied phenomena. To measure spatial autocorrelation, which enables us to quantify the level of spatial concentration of the studied phenomena and reveal spatial clustering, we use Moran’s I (global scale) and LISA (local scale). We explain in depth the properties of these methods, advantages/disadvantages, behaviour in different situations and the potential for them to be combined and used jointly. These methodological findings help to better understand and interpret the results of the subsequent empirical research. We apply the methods in international unemployment research with highly detailed data from Austria, Czechia, Germany, and Poland. Specifically, we are interested in the importance of socio-spatial (regional) organization in relation to unemployment rates, and we present noteworthy results concerning the spatial differentiation of unemployment in the Central European region.