A dataset of US precinct votes allocated to Census geographies with precision
Abstract Voting precincts are the finest spatial units for recording U.S. election results, while census geographies, including block groups, census tracts, and ZIP Code Tabulation Areas (ZCTAs), provide administrative data on demographic, economic, health, and environmental factors. This paper pres...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05140-3 |
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| author | Amir Fekrazad |
| author_facet | Amir Fekrazad |
| author_sort | Amir Fekrazad |
| collection | DOAJ |
| description | Abstract Voting precincts are the finest spatial units for recording U.S. election results, while census geographies, including block groups, census tracts, and ZIP Code Tabulation Areas (ZCTAs), provide administrative data on demographic, economic, health, and environmental factors. This paper presents datasets that link precinct-level voting records to census geographies with precision. The allocation assumes votes within a precinct are proportional to household population, with population distributed from block groups to overlapping precinct fractions using the Regional Land Cover Regression (RLCR) method. Datasets based on surface area and imperviousness methods are also provided, but RLCR outperforms them across multiple error metrics in validation tests using census blocks and voter-level data. Covering the 2016 and 2020 U.S. general elections, these datasets facilitate merging voting records with sources like the American Community Survey, CDC Places, and IRS Statistics of Income to explore voter behavior across various contexts. |
| format | Article |
| id | doaj-art-c643a12613254b58a47cf5eb927920a3 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-c643a12613254b58a47cf5eb927920a32025-08-20T03:48:05ZengNature PortfolioScientific Data2052-44632025-05-0112111010.1038/s41597-025-05140-3A dataset of US precinct votes allocated to Census geographies with precisionAmir Fekrazad0College of Business, Texas A&M University-San AntonioAbstract Voting precincts are the finest spatial units for recording U.S. election results, while census geographies, including block groups, census tracts, and ZIP Code Tabulation Areas (ZCTAs), provide administrative data on demographic, economic, health, and environmental factors. This paper presents datasets that link precinct-level voting records to census geographies with precision. The allocation assumes votes within a precinct are proportional to household population, with population distributed from block groups to overlapping precinct fractions using the Regional Land Cover Regression (RLCR) method. Datasets based on surface area and imperviousness methods are also provided, but RLCR outperforms them across multiple error metrics in validation tests using census blocks and voter-level data. Covering the 2016 and 2020 U.S. general elections, these datasets facilitate merging voting records with sources like the American Community Survey, CDC Places, and IRS Statistics of Income to explore voter behavior across various contexts.https://doi.org/10.1038/s41597-025-05140-3 |
| spellingShingle | Amir Fekrazad A dataset of US precinct votes allocated to Census geographies with precision Scientific Data |
| title | A dataset of US precinct votes allocated to Census geographies with precision |
| title_full | A dataset of US precinct votes allocated to Census geographies with precision |
| title_fullStr | A dataset of US precinct votes allocated to Census geographies with precision |
| title_full_unstemmed | A dataset of US precinct votes allocated to Census geographies with precision |
| title_short | A dataset of US precinct votes allocated to Census geographies with precision |
| title_sort | dataset of us precinct votes allocated to census geographies with precision |
| url | https://doi.org/10.1038/s41597-025-05140-3 |
| work_keys_str_mv | AT amirfekrazad adatasetofusprecinctvotesallocatedtocensusgeographieswithprecision AT amirfekrazad datasetofusprecinctvotesallocatedtocensusgeographieswithprecision |