Authors: Xiang Ye*, Research Institute for Smart Cities, Shenzhen University, Peter A. Rogerson, Department of Geography, University at Buffalo
Topics: Spatial Analysis & Modeling, Quantitative Methods, Geographic Theory
Keywords: modifiable areal unit problem, linear regression, omission error
Session Type: Virtual Paper
Start / End Time: 8:00 AM / 9:15 AM
Room: Virtual 9
Presentation File: No File Uploaded
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 study analyses the impacts of the MAUP on omission error and shows that 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.