Computational Redistricting of Iowa's Congressional Districts
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918118681477120 |
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| author | Wang, Stefanie G. Merrill, Nathaniel C. |
| author_facet | Wang, Stefanie G. Merrill, Nathaniel C. |
| contents | This article expands on the redistricting algorithm proposed by Chen and Rodden (2015) for states with fewer than eight congressional districts, populations highly concentrated in urban areas, or state laws that require preservation of county lines. We used the updated algorithm to redistrict Iowa's four congressional districts. Non-partisan, randomly drawn maps were used to evaluate the fairness of the enacted congressional map. Notably, the evidence suggests that the first proposed map drawn by the Iowa Legislative Bureau was rejected due to partisan bias. This article also analyzes Iowa's 2024 election results for partisan gerrymandering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computational Redistricting of Iowa's Congressional Districts Wang, Stefanie G. Merrill, Nathaniel C. Physics and Society This article expands on the redistricting algorithm proposed by Chen and Rodden (2015) for states with fewer than eight congressional districts, populations highly concentrated in urban areas, or state laws that require preservation of county lines. We used the updated algorithm to redistrict Iowa's four congressional districts. Non-partisan, randomly drawn maps were used to evaluate the fairness of the enacted congressional map. Notably, the evidence suggests that the first proposed map drawn by the Iowa Legislative Bureau was rejected due to partisan bias. This article also analyzes Iowa's 2024 election results for partisan gerrymandering. |
| title | Computational Redistricting of Iowa's Congressional Districts |
| topic | Physics and Society |
| url | https://arxiv.org/abs/2508.05848 |