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.

中文翻译：

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探索人口特征的变异性和地理模式：区域和空间角度

摘要人口特征的变异性和地理格局是人文地理学的重要主题。有许多方法可以探索和定量衡量此问题。除了标准的非空间统计方法外，没有将区域和空间方面纳入面数据分析的通用框架。这主要是因为诸如可修改的地域单位问题或棋盘问题之类的复杂性阻碍了分析。在本文中，我们使用两种方法，将变异性分析的区域和空间观点独特地结合在一起。这种结合为探索人口特征的变异性和地理模式带来了新的见解。使用规则网格中的模型研究区域方法与空间方法之间的关系，使用变异性分解（Theil指数）作为区域方法的示例，并使用空间自相关（Moran's I）作为空间方法的示例。当将这些方法应用于基于1980年至2011年捷克人口普查的经验数据时，这两种方法的结合使我们能够根据其变异性的区域和空间性质对研究现象进行分类。这是一个有用的进步，特别是对于评估随时间的演变或不同现象之间的比较而言。这两种方法的结合使我们能够根据其变异性的区域和空间性质对研究现象进行分类。这是一个有用的进步，特别是对于评估随时间的演变或不同现象之间的比较而言。这两种方法的结合使我们能够根据其变异性的区域和空间性质对研究现象进行分类。这是一个有用的进步，特别是对于评估随时间的演变或不同现象之间的比较而言。