OpticonIQ · Charleston Library · Preliminary Finding

Charleston Tri-County Partisan Composition × Demographics

2024 General Election, integrated with ACS 5-year demographic estimates

This analysis integrates precinct-level results from the November 5, 2024 General Election with U.S. Census American Community Survey 5-year demographic estimates, apportioned from block groups to precincts via population-weighted areal interpolation. The integrated dataset covers 301 matched precincts across Charleston (134), Berkeley (89), and Dorchester (78) counties, representing a population of approximately 676,038 and 332,383 votes cast.

Key Findings

  1. Median household income in precincts where Trump won by more than 20 points ($92,045) was 52% higher than in precincts where Harris won by more than 20 points ($60,521).
  2. Black population share in Strong Democratic precincts averaged 40.3%, compared with 15.6% in Strong Republican precincts — a ratio of 2.6 to one.
  3. Owner-occupied housing rates climbed monotonically with Republican lean, from 49% in Strong Democratic precincts to 81% in Strong Republican precincts. Across the four lean categories, owner-occupancy was the single strongest demographic correlate of partisan vote share.
  4. Educational attainment did not vary linearly with partisan lean. Precincts at both partisan extremes (Strong D: 36.7%, Strong R: 34.6% with bachelor's or higher) had lower college-completion rates than precincts in the Lean categories (Lean D: 38.8%, Lean R: 39.9%). Suburban precincts with college-educated populations were partisan-competitive on both sides.

Headline Table: Demographics by Partisan Lean

Partisan lean Precincts Population Votes Median income % Bachelor's+ % White % Black % Owner-occupied
Strong D (>60% D) 67 136,935 49,942 $60,521 36.7% 47.8% 40.3% 48.7%
Lean D (50-60% D) 55 134,034 63,875 $80,507 38.8% 61.6% 26.8% 69.4%
Lean R (50-60% R) 98 227,673 119,590 $87,834 39.9% 69.7% 20.7% 74.1%
Strong R (>60% R) 81 177,395 98,976 $92,045 34.6% 74.1% 15.6% 81.0%
Partisan lean derived from two-party Democratic vote share in the 2024 presidential contest. Demographic estimates apportioned from Census ACS 5-year (2019–2023) block-group data using area-weighted overlap between 2020 Census Voting Districts and 2024 block groups.

Demographic Differences Across Lean Categories

Demographics by partisan lean

Partisan Lean × Median Household Income

Scatter plot: partisan lean vs income
Each point is a precinct. Point size scales with total votes cast. Dashed vertical line at 50% marks the two-party tipping point.

Partisan Lean × Racial Composition

Scatter plot: % Black vs Democratic share
Each point is a precinct. Point size scales with total votes cast. Dashed horizontal line at 50% marks the two-party tipping point.

County-Level Summary

County Precincts Population Total votes Harris % Trump % Median income % Bachelor's+
Charleston 134 290,304 149,635 53.2% 45.0% $77,223 48.2%
Berkeley 89 225,753 107,030 41.0% 57.6% $80,507 27.7%
Dorchester 78 159,980 75,718 41.9% 56.2% $80,455 30.5%

Methodology and Sources

Sources. Precinct-level vote totals: South Carolina State Election Commission, accessed via the SC Elections Database (electionhistory.scvotes.gov). Demographic estimates: U.S. Census Bureau American Community Survey 2019–2023 5-year estimates, retrieved via the Census Data API. Geographic boundaries: U.S. Census Bureau TIGER/Line shapefiles — 2020 Voting Districts and 2024 Block Groups.

Integration approach. Block-group demographics were apportioned to precincts (Voting Districts) using population-weighted areal interpolation. For each precinct–block-group overlap, count variables (population, race, education counts) were weighted by the area share of the block group falling within the precinct, then summed. Per-capita measures (median household income) were weighted by apportioned population to produce population-weighted precinct estimates.

Matching. Of the 358 Voting Districts in the 2020 Census tri-county coverage and 307 precincts reporting in the 2024 General Election, 301 were matched by precinct name (98.0% of vote precincts; 84.1% of VTDs). Unmatched precincts result from boundary changes, consolidations, or renamings between 2020 and 2024. Unmatched records are documented separately for review.

Limitations. Findings describe observed patterns in the integrated dataset and are reported as descriptive associations, not causal inference. Areal interpolation introduces error proportional to within-block-group heterogeneity; precincts that cross block-group boundaries unevenly may have apportioned demographics that diverge from their actual resident composition. ACS 5-year estimates have margins of error that compound under apportionment; precinct-level estimates should be interpreted as directional.

Use. This report describes precinct-level demographic patterns in Charleston tri-county and is intended as research context for litigation strategy, voir dire preparation, and market analysis. Findings are descriptive research signals, not predictions of outcomes in any specific case or proceeding. Fast Focus does not provide guidance on the use of demographic information in jury selection. All applicable law — including Batson v. Kentucky and its progeny — governs the permissible use of any demographic data in jury selection.

OpticonIQ is a market intelligence firm. This document is not legal advice and does not create an attorney–client relationship.