About This Project
How we built it, where the data comes from, and why we believe redistricting transparency serves everyone — regardless of party.
Mission
Show-Me Districts exists to make Missouri's redistricting process transparent and accessible. We are not affiliated with any political party, campaign, or advocacy organization. Gerrymandering is a bipartisan problem — both parties have drawn self-serving maps when given the chance — and the antidote is sunlight.
Our tools let you explore district history, draw your own plan, and compare it against alternatives scored on objective metrics. We present facts and let citizens draw their own conclusions.
Data Sources
| Data | Source |
|---|---|
| Historical district boundaries | UCLA Congressional District Shapefiles |
| Current district boundaries | Census TIGER/Line Shapefiles |
| Election results | Missouri Secretary of State |
| Census population data | Redistricting Data Hub |
| Community fair maps | Dave's Redistricting App |
Methodology
Historical boundaries are sourced from the UCLA Congressional District Shapefiles project, which digitized boundaries for every Congress since 1789. We extract Missouri's features, simplify geometries for web performance, and overlay election results compiled from the Secretary of State's archives.
The district editor uses 2020 census tract boundaries (~1,400 tracts in Missouri) as the smallest paintable unit. Census tracts are large enough for performant browser rendering while small enough for meaningful district design. Population and voting-age population are sourced from the 2020 PL 94-171 redistricting data.
Fairness metrics include Polsby-Popper compactness, Reock compactness, population deviation from ideal, the efficiency gap, and the mean-median vote share difference. Each metric is computed client-side using Turf.js for geometric calculations. We explain what each metric measures and its limitations — no single metric captures "fairness" completely.
Fairness Statement
We present data and tools — not conclusions about which party "should" win. Reasonable people disagree about what constitutes a fair map: some prioritize compact shapes, others prioritize keeping communities of interest together, and still others prioritize competitive elections.
Our metrics are descriptive, not prescriptive. A map with a high efficiency gap is not automatically "gerrymandered" — Missouri's political geography naturally concentrates Democratic voters in Kansas City and St. Louis. We encourage users to weigh multiple metrics and form their own judgments.
Open Source
This project is open-source under the MIT License. All data processing scripts, source code, and documentation are publicly available. If you find an error or want to contribute, we welcome pull requests and issue reports.