A comprehensive guide to how we calculate voting power and what each component means.
The VoteValue Index (VVI) is a composite score from 0-100 that quantifies how much individual voting power varies across different political districts. The index combines five key factors that influence the effectiveness and impact of an individual vote.
Our goal is to provide a transparent, data-driven measure that helps citizens understand their political influence and encourages informed civic engagement.
Each subscore is normalized to [0, 1].
The 0.4 exponent is a monotonic legibility transform. Most districts are uncompetitive, so raw weighted sums cluster near zero. The exponent lifts scores into a more intuitive range while strictly preserving rank order: if district A scores higher than district B on the weighted sum, it will also score higher after scaling. It makes no claim about "true" absolute voting power.
The most direct determinant of whether an individual vote changes the outcome. Empirically, marginal-seat effects dominate all other factors in vote-power models (Banzhaf 1965, Gelman et al. 2004).
Turnout gaps are the second-largest opportunity for vote impact. A 10pp turnout swing in a district with 30% turnout changes more votes than the same swing at 70%.
Structural amplification of individual votes based on absolute vote margins and district size (Banzhaf power index intuition).
Gerrymandering structurally overrides competitiveness; an unfairly drawn map makes individual votes less meaningful regardless of margin (Stephanopoulos & McGhee 2015).
Whether a chamber is close to flipping party control. Important but applies uniformly across a chamber so less district-specific.
VoteValue uses three established geometric compactness metrics to measure how gerrymandered a district boundary is. Each score ranges from 0 to 1, where lower scores indicate more irregular, potentially gerrymandered shapes and higher scores indicate compact, regular boundaries.
The most widely cited compactness metric. Compares a district's area to the area of a circle with the same perimeter. A perfect circle scores 1.0; highly irregular shapes approach 0.
Measures how much of the district's minimum bounding circle is filled by the district itself. Long, thin, or tentacled districts score poorly.
Compares the district's area to the area of its convex hull (the smallest convex polygon that contains it). Districts with concave notches or narrow corridors score low.
The District Integrity metric used in the VVI combines geometric compactness (weighted average of all three metrics, giving the most weight to Polsby-Popper due to its empirical robustness) with partisan efficiency gap analysis.
The Gerrymander Guesser is an interactive tool where users rank congressional districts by how gerrymandered they appear visually. Every ranking contributes to a community-derived perception score for each district, complementing the purely mathematical compactness metrics.
Rounds are classified by the Polsby-Popper spread between the districts shown. A larger spread means the difference is more visually obvious.
Users can choose to compare 2, 3, or 4 districts per round. Larger rounds generate more pairwise comparisons per submission, which accelerates convergence of each district's ELO rating.
Each district accumulates an ELO rating based on all pairwise outcomes across every round it has appeared in. The ELO system is the same algorithm used in competitive chess ratings.
A district that users consistently identify as more gerrymandered than its opponents will accumulate a higher ELO, even if its Polsby-Popper score is only moderately low. This captures visual and contextual cues that geometric metrics can miss.
To ensure systematic coverage and prevent popular districts from dominating the dataset, approximately 30% of rounds are "balanced" rounds where the selection pool is restricted to districts with fewer than 20 total match appearances. This guarantees that every district eventually reaches a statistically meaningful sample size.
A round counts as correct for streak purposes if the user correctly identifies the single most gerrymandered district (rank #1). Partial credit for correctly ordered pairs is tracked separately and shown in the post-round reveal.
The Map Marketplace allows users to upload, share, and vote on proposed redistricting maps in the VVR (VoteValue Redistricting) format. It is designed to surface community-drawn alternatives to existing district boundaries.
VVR files are JSON-based and contain a complete redistricting proposal including district geometries, proposed boundaries, and optional metadata such as partisan shift estimates and compactness analysis.
format: "vvr" identifierWhen a VVR file is uploaded, VoteValue automatically computes Polsby-Popper scores for each district using the shoelace formula for polygon area and perimeter. This gives immediate feedback on how geometrically compact the proposed map is compared to the existing one.
Logged-in users can upvote or downvote proposed maps. Vote tallies are public and help surface the most community-supported redistricting proposals. Each user may cast one vote per map, and votes can be changed.
The primary precinct-level data layer. VEST shapefiles contain both true precinct boundaries and certified 2020 Presidential election returns in the same dataset — no external join required. 163,904 precincts across all 51 states (50 + DC), 156,144,718 total votes. Used for precinct-to-district aggregation and all district-level margin calculations.
DOI: 10.7910/DVN/K7760H
Historical federal and state election returns used for competitiveness and mobilization calculations.
District boundaries (TIGER/Line 2024: CD119 congressional, SLDU/SLDL state legislative) and demographic data (ACS 2023 5-year estimates: population, race/ethnicity, education, median income).
Voting-Eligible Population (VEP) by state, 2016–2024. Used as the denominator for Mobilization Potential calculations.
Geocoding services for address validation and coordinate lookup.
State chamber composition and representative contact data. Used for Electoral Leverage and Race Significance calculations.
The VVI calculation is implemented in Python using the following key libraries and approaches:
The source code is designed to be transparent and configurable, allowing researchers and analysts to adjust weights, parameters, and data sources as needed.
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