On May 11, 2017, a reporter named Trey Yingst, who covers the White House for the conservative news network OANN, tweeted a photo of a framed map of the United States being carried into the West Wing. The map depicted the 2016 election results county-by-county, as a blanket of red, marked with flecks of blue and peachy pink along the West Coast and a thin snake of blue extending from the northeast to Louisiana.
It was a portrait of the country on election night, but on Twitter, it was also a Rorschach test.
Conservatives replying to Yingst’s tweet interpreted the expanse of red as proof of their party’s dominance throughout all levels of government. Liberals viewed the map as a distortion, masking the fact that most of that redness covers sparsely populated land, with relatively few voters.
In reality, both sides are right, says Ken Field. A self-proclaimed “cartonerd,” Field is a product engineer at the mapping software company Esri and author of a guidebook for mapmakers called Cartography. The problem, he says, isn’t with people’s partisan interpretation of the map. The problem is believing that any single map can ever tell the whole story. “People see maps of any type, and particularly election maps, as the result, the outcome, but there are so many different types of maps available that can portray results in shades of the truth,” Field says. “It’s a question of the level of detail that people are interested in understanding.”
It stands to reason that President Trump would want that particular map hung in the West Wing. There is an awful lot of red on it. But focusing on that map alone could lead Republicans to overestimate their advantage, and lead Democrats to misunderstand the best ways to catch up. That’s one reason why Field recently published an extensive gallery of more than 30 alternative maps designed to tell markedly different stories about what happened on election night 2016. (Well, that and he just really loves maps.)
“All of these maps show different versions of the truth,” he says. “None are right, and none are wrong, but they all allow you to interpret the results differently.”
Take the map Yingst shared, for example. In the language of mapmakers, it’s a “choropleth diverging hue map.” The term “choropleth” refers to maps that use color or shading to visualize a given measurement. In this case, the map uses either the color red or blue to indicate which party won a given county. It’s accurate, and it’s familiar. These colored county-level or state-level maps are some of the most commonly used to illustrate the results of an election. But, Field says, they also lack nuance. There’s nothing on that map to indicate to the viewer, for instance, that fewer votes were cast in the rural mountainous regions of Montana than in Manhattan.
Understanding that nuance—or lack thereof—is key heading into the 2018 midterms, when amateur cartographers will no doubt climb out of Twitter’s recesses to proclaim their definitive readings of electoral maps. Here’s what we can learn from just a few of Field’s examples:
The Pointillism Approach
To Field, there’s no such thing as a totally comprehensive map, but he says, “Some are more truthful than others.” The so-called dasymetric dot density map is one of them. The term “dasymetric” refers to a map that accounts for population density in a given area. Instead of filling an entire state or county with the color red or blue to indicate which party won, Field uses red and blue dots to represent every vote that was cast. On this particular map from 2016, there are roughly 135 million dots. Then, rather than distributing the dots evenly around a county, he distributes them proportionally according to where people actually live, based on the US government’s National Land Cover Database. That’s to avoid placing lots of dots in, say, the middle of a forest, and to account for dense population in cities.
Taken together, Field says, these methods offer a far more detailed illustration of voter turnout than, say, the map in Yingst’s tweet. That map uses different shades of red and blue to indicate whether candidates won by a wide or slim margin. But by completely coloring in all the counties, it gives counties where only a few hundred votes were cast the same visual weight as counties where hundreds of thousands of votes were cast. So, the map looks red. But on the dasymetric dot density map, it’s the blue that stands out, conveying the difference between the popular vote, which Clinton won, and the electoral college vote, which Trump won.
Shades of Red and Blue
The value-by-alpha map is similar to the dasymetric dot density map, and in some ways, even simpler. It doesn’t account for where votes were most likely cast within a county. Instead, it uses color to indicate the party’s vote share in each county, and opacity (in mapmaking, it’s called the “alpha channel,” hence, value-by-alpha) to indicate the population in a given area of the county. A bright, vibrant blue indicates a high Democratic vote share in a densely populated area. A light pink indicates a high Republican vote share in a sparsely populated area. Purples portray areas where one party or another won by a narrow margin.
What you notice first when you look at the map is that the solid red wall extending from North Dakota to Texas on the map Yingst shared is almost white in this rendering. What you notice second is just how much purple there is everywhere else. It’s a good reminder of what people often forget about the 2016 election: “It was very close,” Field says. President Trump won Michigan, Wisconsin, and Pennsylvania, the three states that clinched his victory, by about one percentage point or less.
Field, who moved to the United States from the United Kingdom seven years ago, doesn’t pretend to be an American political insider. But if he were, he says this map—the choropleth highlights map—is the one he’d study closest. This map uses color to highlight vote share in counties that flipped from red to blue or blue to red between the 2012 and 2016 elections. This time, brighter colors indicate a bigger margin for a given party, while lighter shades indicate a narrow victory.
As you might expect, given even a cursory understanding of Trump’s impact on the electoral map, there are far more counties that turned red in 2016 than turned blue. They also happen to be clustered in key parts of the country like Iowa and Wisconsin. But Field was surprised to see about 20 counties that flipped red to blue. “I would never have imagined that,” Field says.
The question of what motivated voters in those places has, no doubt, been a point of focus for campaigns on both sides of the aisle these last two years. Of course, that’s a question no map can really answer.
The View from Above
What Field likes most about the 3D prism map is how people react to it. “It’s just cool. People like 3D stuff,” he says. But it also illustrates an important point. Counties are colored red or blue, based on which party won, but the vote totals are portrayed in three dimensions, where the height is equal to the number of votes cast for the winning party. Because Clinton predominantly won big cities, where more votes are cast, it creates a map that looks a bit like a city itself, with dozens of mile-high blue skyscrapers jutting out from between red row-homes and strip malls.
Click around the map and you’ll see that viewed from above, it looks not unlike Trump’s map—all in red. But click to tilt the map and it’s mostly blue spikes. It demonstrates perhaps more effectively than any of the other maps how President Trump won in 2016, Field says. “You had a Republican who was very successful in getting the smaller areas to vote Republican, while the larger populated major cities went Democrat,” he says.
In many ways, the United States is more divided than ever before. Few maps have illustrated that fact as effectively as the one Tim Wallace created for The New York Times shortly after the election. It visualizes Trump’s America and Hillary Clinton’s America as two distinct, imaginary nations, based on the land area that each candidate won. Trump’s nation covers almost all of what we know as the United States. Clinton’s nation is an archipelago stretching from Boston to San Francisco, with what Wallace calls the “Great American Ocean” filling the American heartland.
The maps are accurate—and disheartening. They show that like-minded liberals have clustered in their own islands, creating invisible borders between themselves and the vast expanse that is Trump’s America. But the story Wallace wrote also inspired Field’s colleague at Esri, John Nelson, to create a map of his own.
Rather than illustrating where the like-minded people live, Nelson wanted to create a map that would show where the vote was closest—where lots of people with different viewpoints live side-by-side. So he selected counties where the 2016 vote came down to plus or minus 5 percentage points. “I’ve heard these places called ‘battlegrounds,’ but I much prefer ‘blended,’” Nelson wrote in a blog post about the map.
To make the land masses look more like islands and less like box-shaped counties, he used the counties’ Zip Code Tabulation Areas, which have more natural-looking borders, as a proxy. Zip Code Tabulation Areas are tools used by the Census Bureau to roughly approximate the land area associated with zip codes. Nelson labeled the big cities closest to these areas to orient viewers and called the map “The Blended America of 2016.” It includes towns all across America, including Buffalo, Fresno, Grand Rapids, and Phoenix.
Of all the maps in Field’s gallery, it may not be the most instructive. It strips away the particulars around who won and by how much. And yet, it may also be the most hopeful. “It’s not like you can tease out strong partisan geographies,” he says. “When I look at that map I see the whole shape of the US.”
The idea that there may be more bright patches of bipartisanship than we imagine is a nice thought, if that’s what you want to see. Of course, just because people may have neighbors they disagree with politically doesn’t mean they’re coexisting peacefully. Such narrow margins could also indicate that these areas are deeply divided, perhaps even bitterly so. As with all maps, it hardly tells the whole story.