Reallocating U.S. Election Results from Precincts to Census Geographies

Voting precincts are the most granular spatial units for reporting election outcomes, whereas census geographies, such as block groups, census tracts, and ZIP Code Tabulation Areas (ZCTAs), are commonly used for publishing demographic, economic, health, and environmental data. This dataset bridges the two by reallocating precinct-level votes to standard census geographies through a systematic and replicable framework.

The reallocation assumes that votes within each precinct are distributed proportionally to the household population. Household population counts from census block groups—the smallest census unit with regularly updated population estimates—are used to allocate votes to fractions created by the intersection of precinct and census boundaries. This process is implemented using three allocation strategies: areal weighting, impervious surface weighting, and Regionalized Land Cover Regression (RLCR). Results from all three methods are provided.

Among these, the RLCR method demonstrates the highest accuracy based on validation against voter-level ground truth data and is recommended as the primary version for analysis. The alternative methods may serve as robustness checks or sensitivity tests.

The dataset currently includes the 2016 and 2020 U.S. general elections and is designed for seamless integration with other datasets, such as the American Community Survey (ACS), CDC PLACES, or IRS Statistics of Income (SOI), via the GEOID field.