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.

投票区（voting precincts）是报告选举结果的最精细空间单元，而普查地理单元，如街区组（block groups）、普查片区（census tracts）与邮政编码统计区域（ZIP Code Tabulation Areas，ZCTAs），通常被用于发布人口、经济、健康与环境类数据。本数据集通过一套系统且可复现的框架，将投票区级别的选票重新分配至标准普查地理单元，从而实现两类统计单元的衔接。

本次重新分配的假设前提为：每个投票区内的选票分布与住户人口规模呈正比。我们采用拥有定期更新人口估算数据的最小普查单元——街区组的住户人口数，将选票分配至投票区与普查边界相交形成的分片区域。该分配过程通过三种策略实现：面积加权法、不透水面加权法，以及分区土地覆盖回归（Regionalized Land Cover Regression，RLCR），并提供了三种方法的全部计算结果。

其中，基于选民级真实验证数据的检验结果显示，分区土地覆盖回归法精度最高，推荐作为分析的首选版本；其余两种方法可用于开展稳健性检验或敏感性分析。

本数据集目前涵盖2016年与2020年美国大选数据，且通过GEOID字段可与美国社区调查（American Community Survey，ACS）、CDC PLACES、美国国税局收入统计（IRS Statistics of Income，SOI）等其他数据集实现无缝集成。

搜集汇总

背景与挑战

背景概述

该数据集将美国选举结果从选区级重新分配到标准人口普查地理单元（如区块组、普查区），通过三种分配方法（面积加权、不透水表面加权和区域化土地覆盖回归法）实现，其中区域化土地覆盖回归法验证准确性最高，被推荐为主要版本；它涵盖2016年和2020年大选，旨在促进与其他社会经济学数据集的整合分析。