Post-Callais data priorities

After Callais v. Landry, the data priorities for redistricting analysts shifted. Traditional effects-based metrics remain valuable—they are not useless—but they must now be framed as circumstantial evidence supporting an intent claim, not as standalone findings. Here are the data priorities that matter most in the post-Callais landscape.

1. Tracking dissolution of minority-majority districts

The most immediate data task is documentation: which majority-minority districts existed in Georgia before Callais, and which have been eliminated, reduced, or altered since the ruling? This requires comparing enacted district maps across cycles using CVAP data. Analysts should build a comparison dataset with each district's minority CVAP percentage in 2011, 2021, and any post-2026 revision.

2. Historical baseline comparisons

To support an intent argument, you need evidence that a pattern of discrimination is not new. Historical baseline data—voter registration denials, polling place closures correlated with race, changes in district configurations that consistently fragment Black or Latino communities—can establish the kind of historical pattern that courts look for when evaluating intent claims.

Key data sources for historical baselines in Georgia: the Georgia Secretary of State's precinct-level results going back to 2002; Brennan Center reports on polling place changes; and archived redistricting files from the 2001 and 2011 cycles (available through the Redistricting Data Hub).

3. Ensemble simulations

One increasingly important analytical tool is the ensemble simulation: generating thousands of random district maps that satisfy neutral redistricting criteria (population equality, compactness, contiguity) and then asking: how often do maps produced by chance create majority-minority districts? If the enacted map produces far fewer majority-minority districts than the ensemble of neutral maps, that is circumstantial evidence that something other than neutral criteria drove the map drawing—which, in turn, is evidence relevant to intent.

Ensemble simulation software (such as the GerryChain Python library, developed by researchers at the Metric Geometry and Gerrymandering Group) has been used in litigation and expert reports. After Callais, ensemble analysis may be one of the most powerful tools available because it directly addresses intent: if neutral criteria consistently produce more minority districts than the enacted map, the deviation requires an explanation.

4. Legislative record documentation

Intent is proved with documents, not just data. Analysts can support advocacy efforts by indexing public records: committee hearing transcripts, draft map proposals, email records obtained through FOIA, and public statements by legislators. Data-driven analysis of the enacted map's statistical properties is strongest when paired with documentary evidence of the decision-making process.