Muddy America : Color Balancing The Election Map - Infographic

by   Larry Weru   Larry Weru   in    math 

The Trouble with the County Winner Map, and why this Muddy Map is better for determining vote populations and vote margins in the US election. Infographic.

2020 update: Click here to view the 2020 Muddy Map

Graphs can inform, and informed discussions can be more civil than uninformed ones. But graphs can also mislead, so we need to understand what a graph is saying when we're using it. In 2015 I gave gave a TEDx talk on making clearer election maps. The original recording was lost, then recovered and uploaded to Youtube this summer. As election season ramps up, I'd like to continue the discussion by talking about this often-misleading map.

2016 County-Level Winner-Takes-All map

It’s not what it looks like.

Different graphs are designed for different purposes. The graph above is a county-level winner-takes-all map. I'll call it a County Winner map for short. Scientists use it to quickly see which way the counties went in an election. While there is arguably no better map for seeing who won in which county, this map can be misleading when used for other purposes. We need to be aware of two of its characteristics:

  1. It doesn’t express the relative voting populations between each county. Instead, it can make people feel like each county has the same population.
  2. It’s not designed to express the margin of victory within each county. It shows who won in a county, even if they won by just one vote. It’s a winner-takes-all graph, after all.

Relative Voting Population

Half of the US population lives in these counties:

Half lives in blue. Half lives in gray. Census/Business Insider

America can be described as a collection of densely populated metros buffered by less densely populated communities. Here’s what the “population mountains” look like:

This map of U.S. population density appeared in Time magazine Oct. 30, 2006 issue.

When we take the County Winner map and resize each county’s land-area to be proportionate to its population, here’s how the US looks.

Mark Newman, University of Michigan

One downside to the cartogram, however, is that the shape and location of many territories are distorted beyond recognition. This is maybe one reason why the cartogram isn't very mainstream.

The County Winner map, however, doesn’t convey this relative population information. It's not designed to. But one might think it does.

Margin of Victory

In the general election, there are 50 concurrent presidential races, one for each state. In some of these states, the margin of victory turns out to be very small. In New Hampshire, 743,117 votes were cast for the president in the 2016 general election. Hillary Clinton won New Hampshire by 2,701 votes. We can seat as many people in a set of high school football bleachers.

How New Hampshire was won.

There was a 75.03% turnout in New Hampshire, so more people could have voted that didn’t. If just 2,702 more eligible voters in New Hampshire exercised their right to vote and voted for Trump, then New Hampshire would have gone to Trump instead. With such hair thin margins, New Hampshire is neither Blue nor Red in 2016. It’s 50:50 and leaning red or blue depending on traffic and dinner plans. It would be misleading to have all of New Hampshire colored as either blue or red to represent the statewide popularity of a presidential candidate.

This characteristic also holds true at the county level. The losing candidate in a county can receive a significant number of votes. In many counties the winner won by less than a 25% margin.

Kenneth Tay, Stanford University. Modified to add color to the scale and to highlight 25% range.
A margin of 0 is 50:50. Clinton's county-level percent-win margins are on the left. Trump's county-level percent-win margins are on the right. The yellow area highlights counties won with vote margins within 25%. Note that these are percent vote margins, not absolute vote margins

In general, smaller counties were won by larger percent margins. Larger counties were won by smaller percent margins. So in the counties with the most votes cast, the runner up got a lot of votes, too.

Here’s what the County Winner map looks like when we account for vote margins by blending each red and blue vote together within each county. Purple represents 50:50:

2016 Purple Map

The neutralizing map is designed to express vote margins more clearly. It uses a grey intermediary, adjusting for the way humans perceive purple. Here’s the 2016 neutralizing map:

2016 Neutralizing Map

Rarely are all of the votes in a county cast for one candidate. The County Winner map, however, doesn’t convey this win margin information.

Color-Balancing the Election Map

The contiguous United States aren't very contiguous. The County Winner map's inability to express vote population and margin of victory can be misleading. Cartograms account for population, but they distort the shape of the US, which can add confusion. The neutralizing map accounts for vote margin, but it doesn't account for population.

Can we construct a single map that shows both vote margin and vote population without distorting the shape of the US?

Here's one way:

Animated GIF that goes from the County Winner map to old muddy.

Here's a less-distracting, static version of the graph:

The map leverages Color Theory to express vote margins and vote populations in a 2-dimensional scale.

Here's the key blown up:

Horizontal scale represents vote margins. Vertical scale represents vote totals.

Lightness (Vertical Scale)

The lighter counties had fewer votes. The darker counties had more votes.

Hue + Saturation (Horizontal Scale)

The closer a county gets to gray, the closer the votes were 50:50.

A highly saturated red county was won by Trump with high percent vote margins. A highly saturated blue county was won by Hillary with high percent vote margins.

The mathematics of the muddy map


All colors can be described as a combination of Hue(°), Saturation(%), and Lightness(%).

The HSL color model.

We can leverage these individual components of the HSL color model to faithfully express 2-dimensional data such as vote totals vs margin on a 2d color scale.

County Fill Colors

The fill-color of each county is constructed using the MuddyColor algorithm, which is expressed as the following mathematical formula:

Formula for county fill color

This produces the following two dimensional scale, which also doubles as a map key, with the upper fence labeled for the 2016 data set:

Color scale produced by MuddyColor, used for county fill colors.

County Border Colors

For the borders of each county, I use the same formula, but just give them a constant lightness (L) of 50%.

Formula for county border color

This results in a 1-dimensional scale which we use for the county borders. It's the same color-scale scale used in the Neutralizing Map, which is designed to more-accurately express vote margins.  Left = higher DEM %margin. Right = higher GOP %margin.

Color scale produced by MuddyBorderColor, used for county border colors.

Giving each county an opaque border color allows even the lightest-filled counties to be recognized, including their vote margins.

With opaque county borders, you can see the counties with even the lowest vote totals.

You don't need to look at the whole nation to see where one county's vote total lies on the overall lightness scale. The  county border color and the  county fill color differ only by lightness, so the greater the contrast between a county's border color and its fill color, the lower its vote total.

Upper Fence

A few counties have enough votes to skew the vote totals scale. Here's how the graph looks when the vote totals scale maxes out at 2,514,055, the maximum number of votes in a county:

A few counties have enough votes to skew the whole vote scale.

One may suggest using a logarithmic scale to bring the sky-high outliers down to Earth. However, this would be visually misleading. A logarithmic scale flattens the min:max vote totals proportion from 1:39,282 closer to1:3.5, visually equating population mountains with population plains.

In this log-scale graph of northwest Texas, can you distinguish a county with 1,000 votes from one with 50,000 votes?

We maintain a linear vote totals scale and use the statistical upper fence to account for outliers. The statistical upper fence can be calculated using the formula Q3 + 1.5 * IQR. Since the county vote margins are only concerned with %DEM|%GOP, county vote totals are DEM+GOP. For vote totals, we calculate the statistical upper fence to be 59,828 DEM+GOP votes. We need to keep in mind that 432 counties have vote totals ≥ 59,828 DEM+GOP and are fully opaque in the Muddy Map.

Practical uses of the Muddy Map

The formulas give the Muddy Map graph some interesting characteristics. For each county, both the percent vote margin, as well as the vote totals (≤ the statistical upper fence), are embedded in the colors of the graph.

The Practical Characteristics of the Muddy Map Algorithm:

  • There are only two hues, red (hue#0 aka hue#360) and blue (hue#240). Red indicates GOP win, blue indicates DEM win.
  • A county with a vote margin of 100% will have 100% saturation.
  • A county with a vote margin of 0% will have 0% saturation. Such a county will be a pure gray, since gray appears at 0% saturation. So the closer a county gets to 50:50, the more gray it appears. No county in the 2016 election had a vote margin of 0%.
  • A county with 0 total votes will have 100% lightness. Pure white appears at 100% lightness, so the closer the vote totals get to 0, the more white the counties appear.  No county in the 2016 election had 0 votes.
  • A county with ≥ 59,828 total votes (the statistical upper fence) has 50% lightness.  432 counties in the 2016 election have this property.

If a computer can faithfully render all of the colors described by the formula, then you can use a color picker to get accurate vote margins and vote totals from counties in the graph. To the human eye, the Muddy Map provides a more faithful picture of the US political landscape.

In a world of deceptive graphs, I hope that a muddy map can make things a bit clearer.

This story was outlined using Columns, the Cornell Notes App

Larry Weru

Lawrence is a consultant and digital storyteller. He illustrates the sciences for a more just and sustainable world.

📺 He is a TEDx speaker and contributing writer for Vox and Slate. His work is featured by Gizmodo, Fast Company, and TechMeme.

🎓 He's a "30 Under 30" alumnus of Florida State University with dual degrees in Studio Art and Biological Science, and is certified in Media and Medicine by Harvard Medical School.