After Callais, the only thing standing between Black voters and a gerrymander is a data analyst

Writing in personal capacity. Personal opinions. Not speaking for Common Cause Georgia.

On April 29, 2026, the Supreme Court handed down Louisiana v. Callais. The decision gutted how Section 2 of the Voting Rights Act gets enforced (ACLU summary). For thirty years, the Gingles framework had been the way courts decided whether a district map illegally diluted Black or Latino voting strength. That framework just got narrower.

The Guardian's coverage put it plainly: the ruling mandates that plaintiffs demonstrate racial intent, which gives Republican majorities the ability to diminish Black political influence throughout the South, where voting patterns are sharply divided along racial lines (The Guardian, April 2026).

Here's what that means in plain language.

The legal bar to prove a map is racially discriminatory just got higher. The data bar to prove it got higher too.

It is no longer enough to show that Black voters are concentrated in one part of a county and the map splits them across three districts. You now have to show racially polarized voting, geospatial proximity, comparable ballot rejection rates, and a pattern of intent — and you have to do it with data that holds up in federal court.

That work doesn't happen by accident. It happens because somebody in a coalition learned how to read a voter file, calculate turnout at the precinct level, and turn it into a one-page policy brief that a litigator can hand to a judge.

We built a course for that person.

Data Analysis for Policy Making: Voting & Redistricting in Georgia. Six modules, twenty minutes each, all built around real Georgia voter files and the post-Callais landscape. No code required. You leave with a one-page Policy Data Brief you can adapt for your own county.

It's free.

We made it free because the organizations that need this analysis the most — small county coalitions, naturalization groups, civil rights orgs that just lost a federal grant — can't pay $2,000 for a data training. And without that analysis, the next map gets drawn in a back room and the people most affected never see the numbers until election day.

If you take the course and find it useful, the way to keep it free is to take one of our paid courses, or donate. Both subsidize the catalog for the next person.

Start the course