<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Exploring the variability and geographical patterns of population characteristics: Regional and spatial perspectives

Department of Social Geography and Regional Development, Faculty of Science

Moravian Geographical Reports

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The variability and geographical patterns of population characteristics are key topics in Human Geography. There are many approaches to exploring and...

Exploring the variability and geographical patterns of population characteristics: Regional and spatial perspectives

Published Online: Jul 08, 2017

Page range: 85 - 94

Received: Apr 18, 2016

Accepted: Jan 03, 2017

DOI: https://doi.org/10.1515/mgr-2017-0008

Keywords
geographical patterns, regional variability, spatial autocorrelation, Theil index decomposition, Moran’s I, Czech Republic

© 2017 Pavlína Netrdová et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Exploring the variability and geographical patterns of population characteristics: Regional and spatial perspectives

Published Online: Jul 08, 2017

Page range: 85 - 94

Received: Apr 18, 2016

Accepted: Jan 03, 2017

DOI: https://doi.org/10.1515/mgr-2017-0008

Keywordsgeographical patterns, regional variability, spatial autocorrelation, Theil index decomposition, Moran’s I, Czech Republic

© 2017 Pavlína Netrdová et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
geographical patterns, regional variability, spatial autocorrelation, Theil index decomposition, Moran’s I, Czech Republic