Redistricting Analyzer | US Map Comparison & Partisan Analytics

Synthetic Algorithmic Data

Connecting to Census API

Fetching Precinct Geometries...

Displaying Enacted Reality

Colorado

Projected U.S. House Control 435 Seats

D: 218 Split Control R: 217

DEMOCRATS 218 FOR MAJORITY REPUBLICANS

Actual 118th Congress: 212 D / 218 R (5 Vacancies)

The map geometry structurally favors Republicans 229 to 206 based on standard normal voting indexes. In reality, Democrats overperformed this geometric bias by +6 seats due to candidate quality and incumbency.

Simulate National Wave Swing 0.0% Wave

← 10% Dem Wave 10% Rep Wave →

Gerrymander Tax

0 Seats Shifted

Seat-to-Vote Bias

0.0% Bias

Efficiency Gap (Partisan Bias)

-

Partisan asymmetry in wasted votes. Values near 0% are fair. Positive favors Dem, negative favors Rep.

Competitive Seats

- / 8

Margins between 45% and 55%.

Avg Compactness

-

Polsby-Popper score (0-1).

Mean-Median Diff

-

Partisan vote-median gap.

County Splits

-

Lower keeps counties unified.

VRA / Minority Power

-

Influence (30%+)

-

Majority (50%+)

Partisan Distribution

States Comparative Leaderboard

Ranked by Enacted Partisan Bias. Select a state to inspect and run its model.

State	Bias (EG)	Compactness	Action

Methodology & Math

Understanding redistricting metrics and algorithms.

1. Partisan Bias (Efficiency Gap)

The **Efficiency Gap (EG)** measures partisan asymmetry in seat shares by tallying "wasted votes." A vote is wasted if it is cast for a losing candidate, or if it is cast for a winning candidate above the 50% threshold needed to win. $$EG = \frac{W_D - W_R}{\text{Total Votes}}$$ Values near **0%** represent partisan symmetry. A high negative gap denotes Republican bias; a high positive gap denotes Democratic bias.

2. Mean-Median Difference

The **Mean-Median Difference** compares a party's statewide average district vote share to its median district vote share. If the median is significantly lower than the mean, the party's voters are highly concentrated (packed) into a few districts, diluting their voting efficiency.

3. County Splits

Splits measure how many times counties are divided across multiple districts. Minimizing splits keeps local administrative borders unified, ensuring localized communities of interest share a single, accountable representative.

4. ReCom Spanning-Tree Algorithm

The **ReCom (Recombination)** algorithm is a Markov Chain Monte Carlo (MCMC) walk that explores redistricting plans.

Step 1: Merge two adjacent districts.

Step 2: Build a spanning tree of blocks.

Step 3: Cut the tree into two equal-population pieces.

Step 4: Check if new shapes satisfy compactness.

This process naturally generates compact shapes and equal-population districts, creating a neutral baseline (ensemble) to evaluate enacted maps.

Disclaimer: All demographic and partisan data used in this application is procedurally generated algorithmically over authentic geographical boundaries for simulation and demonstrative purposes only. Map boundaries & base data: PublicaMundi, OpenPrecincts & U.S. Census Bureau.
Partisan simulations driven by MGGG & GerryChain.

Hover over a district or state to view detailed demographics, partisan leans, and compactness scores.

Engineered by William Elias • Source Code

RedrawUS Analyzer