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Scale, Context, and Heterogeneity

A Spatial Analytical Perspective on the 2016 U.S. Presidential Election

Bibliographic Data

ID7635008
AuthorsA Stewart Fotheringham (0000-0002-0407-1901, Arizona State University), Ziqi Li (0000-0002-6345-4347, University of Illinois Urbana-Champaign), Levi John Wolf (0000-0003-0274-599X, University of Bristol)
Year2021
Pages1-20
Publication date2021-01-11
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueAnnals of the American Association of Geographers (JOURNAL)
Journal identifiersISSN: 2469-4452 • E-ISSN: 2469-4460
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/24694452.2020.1835459
OpenAlexW3120822987
LanguageEN
Citations received22
References cited50

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

Cartography · Democracy · Econometrics · Economics · Geography · Political science · Politics · Population · Presidential election · Presidential system · Socioeconomic status · Sociology · Spatial contextual awareness · Voting · Computer Science · Demography · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Urban, Neighborhood, and Segregation Studies

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Unique citing works22
Citations per year4,4
Citation span2021 - 2025 (5)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 13
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