A Geographical Perspective on Simpson's Paradox

IF 1.8 Q2 GEOGRAPHY Journal of Spatial Information Science Pub Date : 2023-05-17 DOI:10.5311/josis.2023.26.212
M. Sachdeva, A. Fotheringham
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引用次数: 2

Abstract

The concept of scale is inherent to, and consequential for, the modeling of geographical processes. However, scale also causes huge problems because the results of many types of spatial analysis appear to be dependent on the scale of the units for which data are reported (measurement scale). Consequently, when the same spatial models are calibrated at different scales of aggregations, the results are often vastly different (the well-known Modifiable Areal Unit Problem or MAUP). With the advent of local models and the fundamental difference in their scale of application compared to global models, this issue is further exacerbated in unexpected ways. For example, a global model and local model calibrated using data measured at the same aggregation scale can also result in different and sometimes contradictory inferences (the classic Simpson's Paradox). Here we provide a geographical perspective on why and how contrasting inferences might result from the calibration of a local and global model using the same data. Further, we examine the viability of such an occurrence using a synthetic experiment and two empirical examples. Finally, we discuss how such a perspective might inform the analyst’s conundrum: when the respective inferences run counter to one another, do we believe the local or global model results?
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辛普森悖论的地理学透视
规模的概念是地理过程建模所固有的,也是地理过程建模的结果。然而,尺度也会带来巨大的问题,因为许多类型的空间分析的结果似乎取决于报告数据的单位的尺度(测量尺度)。因此,当在不同的聚集尺度上校准相同的空间模型时,结果往往大不相同(众所周知的可修改面积单元问题或MAUP)。随着局部模型的出现以及与全球模型相比其应用规模的根本差异,这一问题以意想不到的方式进一步加剧。例如,使用在相同聚合尺度上测量的数据校准的全局模型和局部模型也可能导致不同的、有时是矛盾的推断(经典的辛普森悖论)。在这里,我们提供了一个地理视角,说明使用相同数据校准局部和全局模型可能会产生对比推断的原因和方式。此外,我们使用一个综合实验和两个经验例子来检验这种情况的可行性。最后,我们讨论了这样一个视角如何为分析师的难题提供信息:当各自的推断相互矛盾时,我们相信局部或全局模型的结果吗?
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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