The Impacts of the Modifiable Areal Unit Problem (MAUP) on Omission Error
The Impacts of the Modifiable Areal Unit Problem (MAUP) on Omission Error
复制标题
可修改面积单位问题 (MAUP) 对遗漏误差的影响
DOI：
10.1111/gean.12269
复制
发表时间：
2021
影响因子：
3.6
通讯作者：
P. Rogerson
中科院分区：
文献类型：
--
作者：
Xiang Ye;P. Rogerson
当回归模型中缺少自变量时，就会出现遗漏错误。当单个观测值不可用时，空间聚合数据集会出现可修改面积单位问题 (MAUP)。在回归分析中，遗漏误差和 MAUP 可能同时出现。特别是，对于线性回归模型来说，MAUP 会导致由于遗漏误差而导致的偏差难以预测，并且它会因不同的空间配置而不同程度地扭曲偏差。本文分析了 MAUP 对遗漏误差的影响，表明总体水平上系数估计的期望可以分解为三个部分：真实系数、个体水平偏差和总体水平偏差。研究结果填补了地理学实证研究与计量经济学理论结果之间的空白，并表明 MAUP 的传统方法（例如来自多个空间配置的报告分析）无助于确定正确的系数。
An omission error occurs when independent variables are missing from a regression model. When individual observations are not available, the modifiable areal unit problem (MAUP) appears with spatially aggregated data sets. Both omission error and the MAUP can occur simultaneously in regression analyses. In particular, the MAUP causes the bias due to an omission error to be less predictable for linear regression models, and it distorts bias differently with different spatial configurations. This article analyses the impacts of the MAUP on omission error and shows that the expectation of coefficient estimates at the aggregate level can be decomposed into three parts: the true coefficient, individual‐level bias, and aggregate‐level bias. The findings fill the gap between empirical studies in geography and theoretical results in econometrics, and show that the traditional approaches to the MAUP, such as reporting analyses from multiple spatial configurations, are unhelpful in identifying the correct coefficients.