The Shifting Landscape Of American Politics A Look At The 2020 Us Map Red And Blue States

Working With the 2020 Election Map Isn't As Simple As It Looks

I spent years building custom political maps for client briefings and internal research, and the 2020 cycle was where the usual assumptions started falling apart. You pull up a red-and-blue map and think you understand the landscape. You don't. The truth is more granular and a lot messier than any coloring book. The core problem most people hit when they try to work with the 2020 US map is that the presidential results paint a misleading picture of where actual political power lives. Biden won the popular vote by about 4.5 points and the Electoral College by a comfortable margin, but the county-level data tells a completely different story about suburban erosion and rural consolidation. I had a client once who wanted to use the state-level map to justify expanding field operations into rural Pennsylvania. The statewide numbers looked promising for Democrats. The county data showed Trump had gained roughly 8 points in those same rural counties compared to 2016, and we convinced him to drop that plan instead.

The Shifting Landscape Of American Politics A Look At The 2020 Us Map Red And Blue States

What the 2020 map actually shows is a continued acceleration of the urban-rural political divide that has been building since the 1980s. Metropolitan counties, which account for roughly 60 percent of the population, went heavily Democratic. Non-metropolitan counties, despite being far fewer in number, still deliver the bulk of electoral votes. This isn't new, but 2020 made the gap sharper than it was in 2016. One thing beginners consistently miss is how swing counties dominate the narrative while swing states get all the attention. In 2020, states like Arizona, Georgia, and Wisconsin weren't actually that close at the statewide level when you look at the margins. What made them look competitive was a handful of suburban swing counties flipping from Trump in 2016 to Biden in 2020. Maricopa County in Arizona alone accounted for a massive chunk of Biden's margin. Flip that one county and the state changes color entirely. The same pattern showed up in Fulton County, Georgia and Wayne County, Michigan. If you're building or analyzing these maps yourself, start with county-level data from the MIT Election Lab or the Carnegie Endowment for International Peace. Both provide downloadable shapefiles and CSVs with vote totals broken down by candidate and party for every county. The FEC website has federal election data too but their interface is terrible and the file formats are not friendly. State election secretary websites are another source but they vary wildly in quality and availability. Some states publish clean datasets. Others make you dig through PDFs.

I used to build these maps using QGIS because it handles geographic data without a license fee, but for anything production-level I switched to ArcGIS Pro. The difference isn't just about capabilities. It's about how much time you save when you're dealing with thousands of county boundaries, proper projections, and label placement. A well-structured map project with layered county data and proper symbology usually takes me about 45 minutes to set up from scratch. A rushed job with wrong projections and misaligned boundaries can look fine to most people but will fall apart under any real scrutiny. There are several pitfalls you should be aware of before you start. The first is that presidential maps and congressional maps tell different stories. A county that looks solidly red in a presidential race might send a Democrat to Congress. Local elections operate on completely different dynamics and the colors on a national map are essentially meaningless at that level. The second pitfall is ignoring population density. A sparsely populated county covering hundreds of square miles can look huge on a map but contribute almost nothing to the electoral outcome. Cartographic distortion makes this worse when you're not careful about using equal-area projections. The third major issue is the way some states handle mail-in ballots. Arizona and Wisconsin in particular had significant vote count delays in 2020 that made it look like the map was shifting back and forth during election night coverage. If you're presenting any kind of live or near-live analysis, you need to factor in that late-arriving ballots could change the trajectory. I learned this the hard way during the 2020 cycle when a client sent out an early read on Wisconsin that turned out to be wrong once the mail ballot count settled. We ate that one.

For anyone who wants to download data and build their own version, here's what I typically use as a starting point. MIT Election Lab provides the Elections Data Series which includes county-level presidential results from 1976 through 2020. That gives you the ability to track shifts over time rather than just looking at a single snapshot. The Carnegie Endowment's American Electoral Map dataset offers a clean interactive version if you want something quicker. Both are free and both require at minimum basic GIS knowledge to work with properly. The 2020 map also shows something that will likely define the next decade of American politics: the further polarization of American voters along geographic lines. This isn't just about red states and blue states anymore. It's about red counties and blue counties existing side by side within the same state. North Carolina is a good example. The state as a whole is competitive, but the Charlotte metro area and the Research Triangle go Democratic while the rest of the state goes Republican by large margins. Same thing in Georgia outside of Atlanta. Same thing in Arizona outside of Phoenix and Tucson. Understanding this pattern matters more than memorizing which states are red and which are blue. The electoral college system rewards geographic concentration of votes, and the current distribution of the American population makes that increasingly advantageous for one party over the other depending on where people live. Gentrification and urbanization continue to shift this dynamic every cycle.

One advanced technique worth knowing is creating a choropleth map that shows the margin of victory rather than just a binary red-or-blue classification. This reveals swing areas that a simple two-color map hides. A county that went 52-48 for one candidate looks the same color as a county that went 70-30, but the political reality in those two places is completely different. The 52-48 county is a battleground. The 70-30 county is not. If you're serious about this stuff, I'd recommend pairing your map work with turnout data. Vote count alone doesn't tell you much. You need to know what percentage of the eligible population actually showed up. A county that went heavily for one candidate might have had 30 percent turnout while a neighboring county with a similar result had 65 percent turnout. Those are two very different political environments and treating them the same will lead to bad conclusions. The tools are accessible now in a way they weren't ten years ago. The data exists. The challenge is knowing what to look for and what to ignore. Most amateur analysts fixate on the wrong variables and draw the wrong conclusions from correct data. That's usually more dangerous than working with incomplete information because it creates a false sense of confidence.

I stopped trying to predict specific county-level outcomes a while ago. The noise at that granularity is too high and the variables too numerous. But I still build the maps because they provide more clarity than any polling aggregate or news analysis piece I've seen. They force you to confront the actual distribution of votes rather than the simplified narratives that dominate coverage.