Feb 08, 2021 Examining Changes in U.S. Presidential Voting Patterns Over 100-Years
By Justin Sorensen
Examining the animation accompanying this week’s Map Monday release depicts how American voting trends change over time, with majority support alternating between the Democratic or Republican parties. While this information provides a general interpretation of each election outcome, visualizing the data provides a method for bringing light to underlying questions about why trends and variations in voting patterns occur. In this week’s Map Monday release, we will visualize and identify some of these questions through an examination of American voting trends over the past 100-years.
Examining Changes in U.S. Presidential Voting Patterns Over 100-Years
For this project, we will focus on (2) types of U.S. Presidential Election data obtained through Social Explorer for each election from 1920 to 2020: These datasets include:
- U.S. Presidential Election Votes by Party
- Election Competitiveness Vote Differences Between Parties
Let’s begin by examining each of the presidential election maps depicting party votes (left-side maps). Represented as county-level, dot-density maps with individual points representing 2,500 votes, the analysis provides the ability to visualize areas of high voter turnout for a particular political candidate. Additionally, the same analysis provides a method for depicting variations in voting patterns during a particular election. For example, the 1992 election between Democrat Bill Clinton and Republican George H. W. Bush shows that voters deviated from the standard Democratic or Republican vote, with many locations reporting a high number of votes for other major party candidates (see example 1).
- Why might this be?
- What is the underlying story behind the unique voting pattern?
Questions such as these can be derived from all types of data…take for instance the maps depicting competitiveness changes between parties (right-side maps). Represented as state-level, choropleth maps with varying percentages of winning votes over the opposing candidate, the analysis provides an interpretation of the victory strength for each candidate within a particular state. Furthermore, the information depicts the popularity of each candidate and overall level of support from American voters. For example, the 1932 and 1984 elections presented clear winners for both Democrat Franklin D. Roosevelt and Republican Ronald Reagan, respectively (see examples 2 and 3).
- What underlying factors may have resulted in high levels of voter support?
- What current events or economic factors might have played a key role influencing variations in voting patterns?
Overall, visualizing data through a geospatial perspective has the ability to identify questions and perceive variations in data that might otherwise go unnoticed. What questions or insights might you discover when visualizing and analyzing your data?
Happy Mapping!
Justin Sorensen | GIS Specialist
Creativity & Innovation Services / GIS Services
justin.sorensen@utah.edu
- Interested in learning more about past Presidents of the United States of America? Check out this past Map Monday Release: Exploring the History of United States Presidents
- Did you know as students, staff, and faculty of the University of Utah, you have free access to the tools and datasets available through Social Explorer? Check out this past blog post for additional information: Exploring Demographic Data Through Social Explorer
- Interested in creating or collaborating on a similar project? Please visit the GIS Services website to connect with me.
About Map Monday Releases from GIS Services
Throughout the semester, GIS Services will be releasing bi-weekly maps on a variety of topics, demonstrating ideas and uses for incorporating geospatial technology into projects and research you are developing. To view our collection of maps, projects, or to learn more about the geospatial services offered through the J. Willard Marriott Library, please visit the GIS Services website @ www.lib.utah.edu/services/geospatial
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