A detailed map overlaying US precinct boundaries and census tracts, representing the core concept of linking election data to demographic information. Wide-angle lens, 24mm, sharp focus.

Mapping Votes: Unlocking US Election Data with Precision

Hey there! Ever look at election maps and wonder, “Okay, but *who* exactly voted where?” It’s a question that gets tricky fast in the US. We’ve got these super-small areas where votes are counted, called precincts. They’re the ground zero for election results. But here’s the rub: government agencies don’t give us the juicy details – like demographics, income, or health factors – at that tiny precinct level. Nope, that kind of info usually lives in different geographic buckets, like census tracts or ZIP Code areas.

So, you’re stuck. You have election results here and population data there, and trying to mash them together is like trying to fit a square peg in a round hole. Most studies end up using bigger areas, like counties. And while county data is useful, it can totally hide the real story. Imagine a county with fancy neighborhoods and working-class areas. County data averages them out, making it look like everyone’s the same. But zoom in, and you see those groups vote really differently. That’s where the magic happens – at a finer geographic level.

The Core Idea: Linking Votes to the People

That’s why we got to work. We wanted to build something that bridges this gap – a dataset that links those granular precinct vote counts directly to the places where we *do* have demographic and other administrative data, like census block groups, tracts, and ZCTAs. Think of it as creating a high-resolution lens for looking at voting behavior.

Our goal was to make it precise, reproducible, and easy for anyone to use. We figured if we could accurately map *who* lives where within a precinct, we could then figure out how the votes cast in that precinct likely distributed among those different population groups in the census areas it overlaps with. It sounds simple, but getting it right is key.

How We Did It: The Methodology

Okay, let’s get a little into the nuts and bolts, but I promise to keep it straightforward. The basic idea we ran with is this: within a precinct, votes are probably distributed based on where the household population lives. If a certain area within a precinct has more people living in houses, it likely accounts for a proportional share of that precinct’s total votes.

Breaking Down the Map: Intersections and Fractions

First, we took the precinct maps and the census block group maps and overlaid them. Imagine cutting both maps into tiny pieces wherever the lines crossed. This creates even smaller areas – we call them “fractions.” Each fraction belongs to one specific precinct *and* one specific block group. This is our starting point for getting granular.

Distributing Population: The Weighting Game

Now, we know the total household population for each block group (thanks, Census Bureau!). The challenge is figuring out how to distribute that population accurately among all the little “fractions” within that block group. This is where the precision comes in. We explored a few ways to do this, essentially creating “weights” for each fraction based on how likely people are to live there:

  • The Simple Way (Areal): Just assume population is spread evenly. A fraction covering 10% of a block group’s area gets 10% of its population. Easy, but often wrong, especially in mixed areas.
  • A Smarter Way (Imperviousness): This method looks at land cover data, specifically how much of an area is covered by non-road impervious surfaces (like buildings, parking lots). The idea is, more buildings usually mean more people.
  • The Star of the Show (RLCR): This is our main method, and it’s pretty neat. It uses detailed land cover data (forests, farms, different types of developed areas) and a statistical model. But instead of trying to build one giant model for the whole country, we first group nearby block groups together into small clusters. Then, within each cluster, we figure out how much each *type* of land cover contributes to the population. This makes the model much more accurate because population patterns vary a lot from place to place. We use the results of this model to predict how many people likely live in each fraction based on its land cover, and *those* predictions become our weights for distributing the block group’s population.

Why Household Population Matters

A quick but important detail: we used *household* population, not total population. Why? Because some areas have lots of people living in group quarters, like prisons or nursing homes, and many of those folks aren’t eligible to vote in local elections. Using household population (which excludes group quarters) gives us a much more realistic picture of the voting-eligible population distribution.

A high-detail digital illustration showing overlapping geometric shapes representing geographic boundaries (precincts and census blocks), creating smaller, precisely defined 'fraction' areas. Macro lens, 60mm, precise focusing, controlled lighting.

Putting It All Together: The Allocation Process

Once we had those population weights for every little fraction, allocating the votes was the final step. If a fraction contained, say, 5% of the household population within its precinct, we assigned it 5% of that precinct’s total votes and 5% of the votes for each candidate. Then, we simply added up the votes for all the fractions that fall within a specific census block group, tract, or ZCTA to get the estimated vote totals for that census geography. Simple addition, but built on that precise population distribution.

The Data We Used

To make this happen, we pulled together data from several public sources:

  • Precinct Data: We got the actual precinct boundaries and election results for the 2016 and 2020 US general elections from reliable sources that compile this data.
  • Census Geographies: The Census Bureau provides the official boundaries for block groups, tracts, and ZCTAs (which are approximations of ZIP codes) through their TIGER/Line Shapefiles. We used the 2016 and 2020 versions.
  • Population Data: Annual household population estimates for block groups come from the American Community Survey (ACS).
  • Land Cover Data: The National Land Cover Database (NLCD) gives us detailed maps showing what’s on the ground – developed areas, forests, farms, etc. We used the NLCD data that matched the election years (2016 and 2020) because land cover changes over time.

It’s worth noting that the NLCD data doesn’t cover Alaska or Hawaii, so our current datasets are for the conterminous United States.

A photorealistic satellite view of a diverse US landscape (urban, rural, forest), overlaid with a grid representing land cover data pixels and abstract data points illustrating population distribution modeling. Wide-angle lens, 10mm, sharp focus.

Checking Our Work: Validation, Validation, Validation

Building this dataset was one thing, but we had to be sure it was accurate. We put our methods to the test in a few ways:

Testing Population Distribution: Against Census Blocks

Remember how we distribute block group population to fractions? Since census blocks are even smaller sub-areas of block groups (and we know their actual populations from the Decennial Census), we could test how well our three weighting methods (Areal, Imperviousness, RLCR) did at predicting block populations. The results were clear: RLCR consistently beat the other two methods across different error metrics. It’s just better at figuring out where people actually live within a block group.

Real-World Votes: North Carolina Ground Truth

This was a cool test. North Carolina is unique because they make detailed voter registration and history data publicly available. While we can’t see *who* voted for *whom*, we *can* see which registered voters participated in an election and where they live. We used this to build a “near ground truth” for vote totals aggregated at the census tract level in North Carolina for 2020. We then compared our estimated tract vote totals (using all three methods) to this ground truth. Again, RLCR came out on top as the most accurate method, even with the unavoidable messiness of real-world voter data.

Seeing is Believing: Visual Check

Sometimes, you just need to look at a map. We compared precinct-level election results maps (the original data) with the maps created after allocating those votes to census tracts using RLCR. Looking at Pennsylvania, for example, the patterns of partisan preference looked remarkably similar. This visual check suggests our allocation process preserves the underlying political geography without introducing weird distortions.

A close-up, high-detail shot of data visualizations (charts and graphs) comparing different data allocation methods, potentially with a subtle map of North Carolina in the background. 100mm Macro lens, high detail, precise focusing.

Does It Make a Difference? Empirical Example

Okay, so RLCR is more accurate in validation tests, but does that precision actually *matter* for research? We ran a simple test: we built a basic model predicting voter turnout using demographic and economic variables, first using vote counts allocated by the simple Areal method, and then using counts allocated by RLCR. The results? Yep, it totally matters! Especially in rural areas, where census tracts are larger and populations are more spread out unevenly, the coefficients in the model changed significantly depending on which allocation method we used. This shows that using a more precise method like RLCR can actually change the conclusions you draw from your analysis.

A diptych image contrasting a dense metropolitan area street scene (portrait, 35mm, depth of field) with a wide-angle view of a rural landscape (landscape wide angle, 10mm), symbolizing the regional differences highlighted in the data analysis.

Grab the Data!

The best part? We’re making these datasets publicly available! You can find them on Harvard Dataverse. They’re in CSV format and organized by geographic level (block group, tract, ZCTA), election year (2016, 2020), and allocation method (Areal, Imperviousness, RLCR). We also provide state-specific files because, let’s be honest, nobody needs empty columns for candidates who weren’t on their state’s ballot.

Each file includes standard geographic identifiers (GEOID, FIPS codes), population info, land/water area, and counts of contributing precincts. Then comes the good stuff: the vote counts! Variables are clearly named (e.g., G20PREDBI for 2020 General Election, Presidential race, Democrat party, Biden). If a candidate wasn’t on the ballot in that area, you’ll see N/A.

These datasets are designed to be easily merged with other public data sources using the GEOID. Want to study how income affects voting? Merge with IRS data. How about health factors? Merge with CDC Places data. Climate vulnerability? FEMA data. The possibilities are pretty exciting!

A detailed map overlaying US precinct boundaries and census tracts, with a subtle background image of diverse people casting votes in a polling station. Wide-angle lens, 24mm, sharp focus.

What’s Next?

We’re not stopping here. We plan to update the dataset with the 2024 election results and future elections as the precinct data becomes available. The goal is to keep this resource current and valuable for researchers and anyone interested in understanding the fascinating intersection of geography, demographics, and voting in the US.

Conclusion

So, there you have it. We’ve built a powerful tool for analyzing US election results with unprecedented geographic precision. By linking precinct votes to census geographies using our validated RLCR method, we’re opening up new avenues for exploring voter behavior and its relationship with a whole host of demographic, economic, health, and environmental factors. We’re excited to see what insights researchers will uncover using this dataset!

Source: Springer

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