“No man is allowed to be a judge in his own cause, because his interest would certainly bias his judgment.”
— James Madison

Welcome to the Congressional Representation Project!

This site is an educational hub for understanding U.S. House representation from 1976 to 2024, covering how votes translate into seats, where elections are competitive, and how those patterns change over time and across states.

Explore the data through an interactive map, trend charts, and downloadable project files. Metrics such as fairness, competition, contestation, and the Electoral Health Index (EHI) summarize statewide patterns in plain numbers, with optional raw and adjusted views when uncontested races would otherwise distort the picture.

Start here:

  • Interactive Map — pick a year and metric, click a state for district-level detail, and double-click to return to the national view. Turn on metric information or the results table for deeper context.
  • Visualizing Trends — chart one or more metrics across years for the nation or a single state. Compare raw vs. adjusted results and spot long-run patterns.
  • Project Files — browse the electoral dataset, source CSV files, and the metric calculation script used to build the site.
  • Details & Sources — data provenance, downloads, licensing, and a full discussion of what these statistics can and cannot show.

Important disclaimer: These visuals and metrics are meant to support learning and comparison. They do not, by themselves, prove intent or legality, and they do not fully capture geographic realities, local communities, or every legal constraint that can shape a map.

Interactive Map

This interactive map provides a visual on nationwide electoral metrics and statewide vote totals.
Click a state to view the state metrics. Double-click to return to nationwide metrics.

Select Year
Elections occur in even-numbered years.
Select Metric
More Options

Usage

Quick example: Select 2024 and Electoral Health Index. Then click a state.

  • If that state’s EHI is high, it usually means fairness, competition, contestation, and stability are all relatively strong for that year.
  • Switch the metric to Fairness and Competition to see why the overall score looks the way it does.
  • Turn on Display Metric Information to view the explanation and formula for each metric and other important information.
  • Turn on Display Table to inspect district-level results and spot patterns (many close races vs. many lopsided races).
  • If lots of races are uncontested, enable Adjusted Metrics and compare: big shifts suggest raw results are being dominated by uncontested districts.
  • Click the Download table as CSV button to download the current table, whether it be state election results or national metrics.

Takeaway: This is best used to determine why a metric is high or low for a given state or year by comparing that metric to each district's congressional election results.

Taking a Closer Look

The metrics and election results shown in the map are derived from our Electoral Dataset, which is available in the Sources section. The map adds geographic context, allowing you to compare metrics nationally and then drill down to individual states and congressional districts.

Some districts have shapes that are widely cited as unusual or controversial, but shape alone is not proof of gerrymandering or political intent. One way to use this view is to explore well-known redistricting controversies as case studies. For example, Louisiana’s 4th district (1992) is often discussed for its "Zorro"-like shape, while North Carolina’s 12th district (1992) was central to the Supreme Court case Shaw v. Reno. The map can help visualize these districts while the metrics provide additional context for comparison.

Some Louisiana (LA) elections may appear unusual because of the state's use of nonpartisan blanket primaries, often called "jungle primaries." Instead of separate party primaries, all candidates compete on the same ballot, and the top two may advance to a runoff regardless of party. In some historical elections, comparable Democratic-versus-Republican vote totals are not available, so the dataset records only the winning party. On the map and in the data, these elections may appear as 1s and 0s rather than conventional vote totals.

District boundaries are also shaped by legal and political constraints. Common factors include the Voting Rights Act of 1965 (VRA), equal-population requirements, communities of interest, compactness criteria, and whether maps are drawn by legislatures, commissions, or courts. Because these factors often interact, the map and metrics should be viewed as tools for exploration and comparison rather than a definitive judgment of any single cause.

Apportionment can also have a major impact on representation. After each Census, congressional seats are redistributed among the states based on population changes. States may gain or lose seats, requiring district boundaries to be redrawn even when political goals remain unchanged. Population growth, decline, and demographic shifts can all influence how districts are configured and how competitive elections become.

Massachusetts (MA) in 2024 illustrates how limited major-party competition can affect headline metrics. When many races are effectively uncontested, measures of competition and contestation can decline, lowering the overall EHI. The example also highlights a dataset choice: independent and third-party votes are kept separate rather than reassigned to either major party, even when an independent is a significant challenger.

Visualizing Trends

Select one or more metrics to chart across years for the nation or a state.

State/National
National Weighting
Metric Type
Start Year
End Year

Usage

The chart can be used to analyze trends and correlations across years for the nation or a state.

  • Check each metric you want to chart by selecting it in the Metrics panel. You can select multiple metrics to find correlations and determine how they influence each other.
  • Select a State/National to compare state or national metrics. Using the Interactive Map, you can compare a state's metrics to it's rank and previous ranks in the table.
  • Select a Metric Type to compare raw, adjusted, or both metric types. Adjusted metrics are adjusted for uncontested or missing-opposition races.
  • Select a National Weighting to compare district-weighted or state-weighted metrics. District-weighted metrics give states with more districts more weight.
  • Select a Start Year and End Year to limit the range of years to chart. This can be used to find and compare short-term trends.

Takeaway: This is best used for comparison and pattern-spotting across years/states. If a metric looks extreme, check Confidence and compare raw vs adjusted before drawing conclusions.

Taking a Closer Look

The metrics shown in the chart are derived from our Electoral Dataset, which is available in the Sources section. The chart lets you compare national or state trends across years. The patterns below use district-weighted national averages of raw metrics as a starting point for exploration. They are not proof of any single cause.

Nationally, the Electoral Health Index (EHI) moves more in cycles than in a straight line. It peaks near 77 around 1992, when contestation and competitiveness were both relatively strong, then falls to a low near 67 in 2002. That year was the first House election after the 2000 Census redistricting cycle, and several states (including Nebraska, Massachusetts, and Louisiana) show unusually weak competition in the data. EHI rebounds again in high-turnout years such as 2020 (near 76) before settling near 72 in 2024. Over the full 1976 to 2024 span, the national EHI ends only slightly above where it began, which is a reminder that “healthier” and “less healthy” election years can alternate even when the long-run average looks stable.

Fairness tells a different story. The national fairness score drifts down from about 48 in 1976 to about 39 in 2024, with sharp dips that often follow redistricting. Fairness drops noticeably after the 2000 and 2010 to 2012 map cycles, when many states were still running new district lines from the prior Census. It rises in 2020 (to about 49) alongside broader two-party competition in a presidential year, then falls again by 2024 as maps from the 2020 Census cycle take full effect. States such as Illinois, Colorado, and Arizona show some of the largest fairness declines between 2018 and 2024 in the dataset. That timing lines up with real-world redistricting fights, court challenges, and partisan mapmaking debates. The metric still measures vote-to-seat balance in the data, not legal intent.

Confidence is usually high nationally (often above 85), but it is worth watching when it dips. The national average falls to about 82 in 2014, when many states are flagged as post-redistricting and several have low contestation, including Massachusetts, Georgia, and Louisiana. Confidence recovers in wave years like 2020 (near 96) when more districts have meaningful two-party vote totals, then eases again by 2024 (near 86). That pattern fits how the score is built: confidence falls when uncontested races, heavy reliance on adjusted values, or fresh map changes make statewide metrics harder to read reliably.

These national lines are averages, so they can hide dramatic state stories. Use the chart’s State/National control to zoom in on a single state. For example, Massachusetts posts a very low EHI in 2024 (about 23) with contestation near 22, illustrating how uncompetitive delegations can drag metrics even when the national picture looks moderate. Comparing raw and adjusted lines, and checking Confidence in the same year range, is the best way to separate a genuine structural shift from a data-quality or uncontested-race artifact before tying a trend to a specific election or redistricting event.

Project Files

Switch tabs to browse the electoral dataset, source CSV files, and metric scripts.

Select a tab to preview a file.

Details & Sources

This section is for readers who want the data sources, technical caveats, and a quick site index.

Quick index
Data sources and downloads

All project data lives under data/. You can preview most files in Project Files or download them directly below. Site code is licensed under MIT; dataset licensing is listed per source.

  • electoral-dataset.json

    Processed dataset powering the interactive map, trend charts, and tables. Includes state and district vote totals, raw and adjusted metrics, and presidential context by state/year.

    Download:
  • mit-1976-2024-house.csv

    Raw U.S. House candidate-level results, 1976–2024.

    Download:
    License: CC0 (public domain)
  • princeton_CE_results.csv

    Congressional election results by state, year, and district (Dem/GOP votes, vote share, incumbent). Used as input to metric calculations.

    Download:
    License: CC0 (public domain)
  • dailykos_GE_results.csv

    State-level presidential election results (Dem%, Rep%, swing) used when estimating vote shares in uncontested House races.

    Download:
    Source: Stephen Wolf and David Nir, Daily Kos Elections (2021) — spreadsheet
  • LICENSE

    CC0 license text for jsoncsv datasets.

    Download:
  • compute_metrics.py

    Reads the Princeton and Daily Kos CSV inputs, cleans and imputes uncontested races, calculates all statewide metrics (EHI, fairness, competition, and others), and writes the electoral dataset. Comments in the file label each metric calculation; preview it in Project Files.

    Download:
  • manifest.json

    Index mapping each state (by FIPS code) and election year to the correct boundary file for that redistricting cycle.

    Download:
  • District GeoJSON files

    One file per state and map era (325 files), named {FIPS}_{startYear}_{endYear}.geojson (for example, 37_2022_2022.geojson for North Carolina in 2022). These files are loaded on demand by the map; browse the data/geojson/ folder to download individual files.

  • LICENSE

    MIT License for boundary data.

    Download:
    Source: UCLA Congressional District GeoJSON (Jeffrey B. Lewis et al.)
  • LICENSE

    MIT License for this website, front-end code, and project scripts (including compute_metrics.py).

    Download:
    License: MIT

Citation: When using the processed electoral dataset in research, journalism, or derivative work, please cite The Congressional Representation Project (crproject.org).

Exceptions and limitations

This site uses statistical summaries of election results to help compare states and years. Those numbers are useful for exploration and pattern-spotting, but they are not a complete picture of representation, intent, or legality. Below are important limits of the approach and real-world factors the metrics do not fully capture.

What statistical analysis can and cannot show

  • Metrics are not proof of gerrymandering or bad intent. A low fairness score or unusual district shape can reflect many causes, including geography, the Voting Rights Act, population constraints, court orders, or partisan mapmaking. The site highlights patterns in vote-to-seat balance and competition; it does not determine why a map looks the way it does.
  • Scores compress complex elections into single numbers. Efficiency gap, mean–median, proportionality, and EHI summarize statewide patterns. They can hide local stories, such as one competitive urban district inside an otherwise safe delegation.
  • Adjusted metrics rely on estimates. When races are uncontested, the script imputes plausible two-party vote shares using statewide House results, presidential results, and neighboring districts. That improves comparability but introduces modeling choices raw vote totals alone do not contain.
  • Confidence scores flag uncertainty, not guilt. Lower confidence often means many uncontested races, fresh post-redistricting maps, or heavy use of adjusted values; it does not mean the underlying election was irregular.
  • Cross-year comparisons are approximate. District boundaries change after each Census. A state's metrics in 2010 and 2020 may refer to different geographies, so long-run trends should be read cautiously around redistricting cycles.

Real-world factors this analysis does not fully model

  • Geography and communities. Mountain ranges, coastlines, and urban/rural splits naturally cluster voters. A compact, geographically coherent map can still produce lopsided seat outcomes, and a bizarre-looking shape is not, by itself, evidence of manipulation. Metrics here do not measure compactness, communities of interest, or how well boundaries follow local geography.
  • Legal and political mapmaking constraints. Maps must satisfy equal population, comply with the Voting Rights Act, respond to court rulings, and follow state rules about commissions vs. legislatures. Those forces can simultaneously improve representation for some groups and reduce headline competitiveness, which a single fairness number cannot disentangle.
  • Apportionment and seat counts. When a state gains or loses seats after a Census, districts are redrawn even if partisan goals stay fixed. Shifts in competitiveness or fairness may reflect seat reallocation and population change, not a new gerrymander.
  • Unusual election systems. Louisiana's nonpartisan blanket ("jungle") primaries sometimes yield races without comparable Democratic–Republican vote totals. In those cases the dataset may record only a winning party, and district totals can appear as 1s and 0s rather than ordinary vote counts, which can distort raw competition and contestation metrics.
  • Third-party and independent candidates. Votes for independents and minor parties are not folded into either major party. A strong independent challenger can depress major-party competitiveness scores even when the race was genuinely competitive (Massachusetts in 2024 is one example discussed on this page).
  • Incumbency, fundraising, and local politics. Safe seats can reflect long-standing incumbents, weak recruitment, or regional one-party dominance, not only district lines. Incumbency insulation measures structural protection in the data but not campaign spending, media markets, or candidate quality.
  • Presidential vs. House behavior. Adjusted estimates lean on presidential vote shares when House races are missing opposition. States that split tickets or shift sharply between federal elections may not be well represented by that shortcut in every year.

Examples worth keeping in mind

  • North Carolina's 12th district (1992) — Central to Shaw v. Reno and often cited for its shape. The map helps you see the boundary; metrics add vote-to-seat context but do not settle legal or historical debates about the map's purpose.
  • Louisiana's 4th district (1992) — Frequently discussed for its unusual ("Zorro-like") geometry. Shape draws attention; statistical scores describe election outcomes within that geometry. They are complementary, not interchangeable.
  • Massachusetts (2024) — Many effectively uncontested House races can push competition and contestation down and drag EHI lower, even when the state's overall political landscape is stable. Comparing raw vs. adjusted metrics helps separate structural map effects from uncompetitive delegations.
  • Post-Census years (e.g., 2002, 2012, 2022) — National fairness and confidence often move after new maps take effect. A dip may track implementation of new boundaries and incomplete contestation data as much as a permanent structural change.

Bottom line: Use these tools to ask better questions: why a metric is high or low, how raw and adjusted results differ, and how a state compares before and after redistricting. They are not meant to support definitive conclusions about intent, fairness in a legal sense, or the full lived experience of representation in each district.

Credits & disclosure

AI disclosure: Artificial intelligence tools were used in part to help build this site, including code, copy, and layout. All data, metrics, and source material were reviewed and verified by a human before publication.

If you spot an error in the data, a metric calculation, or anything else on this site, please let us know at contact@crproject.org.

Created by Aydon Fauscett
Email: aydonsoffice@gmail.com
GitHub: github.com/aydon14