It looks like a bloodbath. If you stare at the 2016 presidential county map without any context, you’d swear one candidate didn't even show up. It’s almost entirely crimson. From the high plains of Montana down to the tip of the Florida panhandle, the visual is overwhelming. Donald Trump won 2,626 counties. Hillary Clinton won 487.
But maps are liars. Or, at the very least, they’re masters of misdirection.
Geography doesn't vote. People do. That’s the core tension that broke the American psyche in November 2016 and honestly, we haven't really recovered from the shock. We saw a map that looked like a landslide, yet the popular vote told a story of a divided nation where the "loser" was actually up by nearly three million votes. It’s a cartographic paradox.
The Great Rural Re-Alignment
The real story of the 2016 presidential county map isn't just that Trump won rural areas. It’s how he won them. Republicans have always done well in the "empty" spaces, but 2016 was a fundamental shift in the margins. In places like Elliott County, Kentucky, the streak was broken. That county had voted Democrat in every single election since it was formed in 1869. Every. Single. One. Then 2016 happened, and it swung nearly 70 points to the right.
It wasn't just a fluke.
You saw this pattern repeat across the "Blue Wall" states of Pennsylvania, Michigan, and Wisconsin. These weren't just narrow losses for Clinton; they were systemic collapses in counties that had been labor strongholds for decades. Look at Luzerne County, Pennsylvania. Obama won it twice. Trump took it by 20 points. Why? Because the map was finally reflecting a cultural and economic decoupling that had been simmering since the late 90s.
Small towns felt invisible.
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When you look at the 2016 map, you’re looking at a visualization of resentment. It’s a map of places that felt the globalized economy had moved on without them. The "Rust Belt" tag gets thrown around a lot, but in 2016, it became a literal electoral reality. The red wasn't just "Republican"—it was an anti-establishment middle finger rendered in pixels.
Size Matters (But Not the Way You Think)
We have to talk about the "Blue Dots."
If you zoom into the 2016 presidential county map, you’ll see these tiny islands of blue surrounded by vast oceans of red. These are the metros. Places like Cook County (Chicago), Los Angeles County, and New York County. These dots are tiny on a map, but they are dense.
Honestly, the way we visualize elections is kinda broken. A county with 5,000 people takes up ten times more physical space on a screen than a city with 5 million. This leads to what geographers call "the illusion of dominance." If you look at a cartogram—where county size is scaled by population—the 2016 map starts to look like a purple sponge rather than a red wall.
- Los Angeles County, CA: Clinton won by 1.2 million votes.
- Loving County, TX: Trump won by 54 votes.
On a standard map, Loving County looks substantial. In reality, it’s a handful of people.
This disparity explains why Clinton could lose the 2016 presidential county map by a ratio of 5-to-1 and still walk away with 65.8 million votes. It's the ultimate evidence of the "Two Americas" theory. We don't just disagree on policy anymore; we live in entirely different geographic realities. One America is dense, diverse, and vertical. The other is sprawling, homogenous, and horizontal.
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The Suburban Shifting Ground
While the rural areas went deep red, the suburbs started to twitch. This is the nuance people usually miss. In the 2016 presidential county map, you can see the very early stages of the GOP’s "college-educated" problem.
Take Orange County, California. This was the birthplace of the Reagan revolution. It was a GOP fortress for generations. In 2016, it went blue for the first time since the Great Depression. You saw similar, albeit smaller, shifts in the suburbs of Atlanta and Houston.
Trump traded high-income suburbanites for working-class rural voters.
It was a gamble that paid off in the short term because of how the Electoral College is weighted. The 2016 map proved that you don't need the most votes; you just need the right votes in the right zip codes. By running up the score in rural counties that hadn't seen a presidential candidate in years, the GOP managed to flip three states by less than a combined 80,000 votes.
Think about that. A few packed stadiums' worth of people in three counties changed the course of global history.
Data Nuggets You Probably Missed
The 2016 data is full of weird anomalies that pundits still argue about in bars.
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- The Pivot Counties: There are 206 counties that voted for Obama twice and then flipped to Trump. These "Pivot Counties" are the holy grail for political scientists. They aren't in the Deep South; they are clustered in the Upper Midwest and Northeast.
- The "Double Hater" Effect: In many of these counties, voters disliked both candidates. But on the map, they broke for the "change" agent.
- The Third Party Factor: In counties like Utah County, UT, the map looks weird because Evan McMullin took a massive chunk of the vote. It wasn't just a two-way street.
Why the 2016 Map Still Haunts Us
We’re still living in the shadow of this specific map. It redefined what "swing territory" looks like. It moved the battlefield from the suburbs of Ohio to the post-industrial towns of the Lehigh Valley.
When you study the 2016 presidential county map, you aren't just looking at an old election. You’re looking at a blueprint for every election that has followed. It taught the GOP that they could win without the popular vote if they maximized their rural margins. It taught the Democrats that they could no longer take the "Working Class" for granted just because of union history.
It’s a map of a divorce.
The geographic sorting of Americans is nearly complete. We choose where to live based on our politics, and our politics are shaped by where we live. If you live in a red county on that 2016 map, you likely don't know many people who voted for Clinton. If you live in a blue county, Trump voters might feel like a myth.
The map didn't just record the divide; it deepened it.
Actionable Insights for Analyzing the Map
To truly understand the implications of the 2016 data, don't just look at the colors. Look at the margins. Use tools like the MIT Election Data and Science Lab to download the raw county-level CSVs.
- Filter by "Margin of Victory": Look for counties where the win was less than 2%. Those are your future battlegrounds.
- Compare Population Density: Cross-reference the 2016 county map with US Census density data. You’ll find an almost perfect correlation between "people per square mile" and "probability of voting Democrat."
- Track the "Pivot Counties": Watch how the 206 Obama-Trump counties voted in 2020 and 2024. They are the most volatile parts of the American electorate.
- Analyze Voter Turnout: A red county isn't always a "pro-Trump" county; sometimes it’s just a "low-turnout Democratic" county where the base stayed home.
The 2016 presidential county map is the most important political document of the 21st century so far. It’s the moment the "Big Sort" became undeniable. Study it not as a final result, but as a warning of how far apart the two halves of the country have drifted.