Flowerdew Robin
ESRC Centre for Population Change, School of Geography and Geosciences University of St Andrews.
Popul Trends. 2011 Autumn(145):102-14. doi: 10.1057/pt.2011.20.
Population data are often collected or presented for geographical areas which may have little or no connection to the processes generating the data. Such areal units are termed 'modifiable'. However analysis undertaken on such data is not independent of how these areal units are configured. Indeed, Openshaw (1984) and others have shown that the results of statistical analysis may differ wildly according to the scale and pattern of the areal units used. This phenomenon is called the modifiable areal unit problem (MAUP). It is clear that the MAUP exists, but far from clear about how often it occurs, how often it affects the conclusions from empirical data analysis, and in what contexts it makes most (or least) difference. British census data are well suited for investigating these issues, being available for different geographies which neatly nest within each other, and for a range of different variables of interest to central and local government and to many academic disciplines. This article is concerned with bivariate correlations (using Pearson's r) between pairs of variables. The aim is to see if any variables seem particularly liable to display MAUP effects, and if so, why. The conclusion is that MAUP in many cases makes little or no difference to the results, but there are some variable pairs where the effect is substantial.
人口数据通常是针对地理区域收集或呈现的，这些地理区域可能与生成数据的过程几乎没有关联或完全没有关联。这样的区域单元被称为“可修改的”。然而，对这些数据进行的分析并非独立于这些区域单元的配置方式。事实上，奥彭肖（1984年）等人已经表明，统计分析的结果可能会因所使用的区域单元的规模和模式而大相径庭。这种现象被称为可修改区域单元问题（MAUP）。很明显，MAUP是存在的，但远不清楚它出现的频率、影响实证数据分析结论的频率，以及在哪些情况下它的影响最大（或最小）。英国人口普查数据非常适合用于研究这些问题，因为有不同地理区域的数据，这些区域彼此嵌套得很好，并且有一系列中央和地方政府以及许多学术学科感兴趣的不同变量。本文关注的是变量对之间的双变量相关性（使用皮尔逊相关系数r）。目的是看看是否有任何变量似乎特别容易显示出MAUP效应，如果是这样，原因是什么。结论是，在许多情况下，MAUP对结果的影响很小或没有影响，但有一些变量对的影响很大。
