Abstract
The variability and geographical patterns of population characteristics are key topics in Human Geography. There are many approaches to exploring and quantitatively measuring this issue. Besides standard aspatial statistical methods, there is no universal framework for incorporating regional and spatial aspects into the analysis of areal data. This is mainly because complications, such as the Modifiable Areal Unit Problem or the checkerboard problem, hinder analysis. In this paper, we use two approaches which uniquely combine regional and spatial perspectives of the analysis of variability. This combination brings new insights into the exploration of the variability and geographical patterns of population characteristics. The relationship between regional and spatial approaches is studied with models in a regular grid, using variability decomposition (Theil index) as an example of the regional approach, and spatial autocorrelation (Moran’s I) as an example of the spatial approach. When applied to empirical data based on the Czech censuses between 1980 and 2011, the combination of these two approaches enables us to categorise the studied phenomena according to the regional and spatial nature of their variability. This is a useful advance, especially for assessing evolution over time or comparisons between different phenomena.