Measuring Geographic Distribution for Political Research

Abstract

Political scientists are increasingly interested in the geographic distribution of political and economic phenomena. Unlike distribution measures at the individual level, geographic distributions depend on the “unit question” in which researchers choose the appropriate political subdivision to analyze, such as nations, subnational regions, urban and rural areas, or electoral districts. We identify concerns with measuring geographic distribution and comparing distributions within and across political units. In particular, we highlight the potential for threats to inference based on the modifiable areal unit problem (MAUP), whereby measuring concepts at different unit aggregations alters the observed value. We offer tangible options for researchers to improve their research design and data analysis to limit the MAUP. To help manage the measurement error when the unit of observation is unclear or appropriate data are not available, we introduce a new measure of geographic distribution that accounts for fluctuations in the scale and number of political units considered. We demonstrate using Monte Carlo simulations that our measure is more reliable and stable across political units than commonly used indicators because it reduces measurement fluctuations associated with the MAUP.

Beyond the City–County Divide: Examining Consolidation Referenda Since 2000

“…Additionally, it should be acknowledged that while this study attempted to derive the "smallest" possible estimates of voters' characteristics in consolidation referenda, these data present an inherent modifiable areal unit problem (MAUP) (Amos, McDonald, and Watkins 2017;Lee and Rogers 2019;Nemerever and Rogers 2021). When deriving estimates from overlapping boundaries based on an assumed even dispersion of residents (or voters), changing boundaries (in this case, voting precincts) to another geographic or administrative unit could alter these results.…”

When to use commuting zones? An empirical description of spatial autocorrelation in U.S. counties versus commuting zones

“…By activating prior knowledge in the operationalization process, researchers are less likely to overlook an ecological fallacy of aggregation. Past efforts have been made to educate researchers in child welfare [ 28 , 29 ], public health [ 30 ], and political science [ 31 ]. We believe the next step is to offer an exploratory narrative to help researchers purposefully select between U.S. counties and commuting zones for social science research.…”

Uncertainty in Grid Data: A Theory and Comprehensive Robustness Test

“…Over the last decade or so, political and conflict research has significantly developed geo-referenced data and analytical technique for such data (e.g., Lee and Rogers 2019;Kikuta 2022;Pickering 2016;Schutte and Donnay 2014;Shaver, Carter, and Shawa 2019;Sundberg and Melander 2013;Tollefsen, Strand, and Buhaug 2012). The gist of georeferenced data is to disaggregate macro-level data into micro-level ones.…”

“…As known, a measurement error affects statistical and causal inference (Pearl 2010), while the first two sources of uncertainty specific to grid data are related to the socalled Modifiable Areal Unit Problem: the choice of a geographic unit affects inference (Lee and Rogers 2019;Lee, Rogers, and Soifer 2020;Openshaw 1983;Soifer 2019). For political geographic units, this problem can sometimes be addressed based on a theoretical justification; for example, the norm of national sovereignty may be a good justification for why nation-states are a plausible unit for the study of foreign policy.…”