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Scale, Context, and Heterogeneity: A Spatial Analytical Perspective on the 2016 U.S. Presidential Election

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  • A. Stewart Fotheringham
  • Ziqi Li
  • Levi John Wolf

Abstract

This article attempts to identify and separate the role of spatial “context” in shaping voter preferences from the role of other socioeconomic determinants. It does this by calibrating a multiscale geographically weighted regression (MGWR) model of county-level data on percentages voting for the Democratic Party in the 2016 U.S. presidential election. This model yields information on both the spatially heterogeneous nature of the determinants of voter preferences and the geographical scale over which the effects of these determinants are relatively stable. The article, perhaps for the first time, is able to quantify the relative effects of context versus other effects on voter preferences and is able to demonstrate what would have happened in the 2016 election in two scenarios: (1) if context were irrelevant and (2) if every county had exactly the same population composition. In addition, the article sheds light on the nature of the determinants of voter choice in the 2016 U.S. presidential election and presents strong evidence that these determinants have spatially varying impacts on voter preferences.

Suggested Citation

  • A. Stewart Fotheringham & Ziqi Li & Levi John Wolf, 2021. "Scale, Context, and Heterogeneity: A Spatial Analytical Perspective on the 2016 U.S. Presidential Election," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 111(6), pages 1602-1621, September.
    • Handle: RePEc:taf:raagxx:v:111:y:2021:i:6:p:1602-1621
    DOI: 10.1080/24694452.2020.1835459
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    Cited by:

    1. Dylan S Connor & Aleksander K BergArizona & Tom Kemeny & Peter J Kedron, 2024. "Who gets left behind by left behind places?," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 17(1), pages 37-58.
    2. Wolf, Levi John, 2022. "The Right to Rule by Thumb: A Comment on Epistemology in "A Route Map for Successful Applications of Geographically-Weighted Regression"," SocArXiv qzjwb, Center for Open Science.
    3. Taylor M. Oshan & Levi J. Wolf & Mehak Sachdeva & Sarah Bardin & A. Stewart Fotheringham, 2022. "A scoping review on the multiplicity of scale in spatial analysis," Journal of Geographical Systems, Springer, vol. 24(3), pages 293-324, July.

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