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.
Pavel Klapka, Marián Halás, Pavlína Netrdová and Vojtěch Nosek
An attempt to provide a procedure for the assessment of the efficiency of various regional systems for the purposes of spatial analysis is presented in this paper. Functional regions as well as approximated functional regions and the existing administrative regions in the Czech Republic are evaluated, as examples of regional systems to be compared and assessed. Functional regions and approximated functional regions are defined according to the adjusted third variant of the CURDS regionalisation algorithm, using the latest knowledge on the operation of the constraint function. The comparisons of individual regional systems are based on LISA maps and particularly on the assessment of regional variability, including the measures of internal homogeneity and external variability in the regional systems.