🔧 What Was Built

A new computational algorithm was developed to generate legislative district maps that are contiguous, population-balanced, and relatively compact. The design explicitly aims to avoid the forms of bias identified in prior automated redistricting methods while improving computational efficiency so it can handle medium and large jurisdictions.

📊 How Maps Were Created and Tested

• 10,000 simulated congressional maps were generated for each of three states: Mississippi, Virginia, and Texas.
• Simulations were used as a neutral benchmark to compare the distributional properties of the algorithm’s output with both observed maps and outcomes produced by earlier algorithms.

🔎 Key Findings

• The new algorithm produces contiguous, balanced, and relatively compact districts without showing the kinds of bias found in earlier automated approaches.
• The algorithm is computationally more efficient than previously published redistricting algorithms, making it practical for larger jurisdictions.
• Across 10,000 simulated maps for each state, it is unlikely that the observed number of majority-minority congressional districts in Mississippi, Virginia, and Texas would arise from a neutral redistricting process.

📌 Why This Matters

These results demonstrate both the promise and the diagnostic power of neutral, efficient simulation tools for redistricting. By providing a tractable, less-biased benchmark for large-scale map generation, the algorithm offers a clearer basis for assessing whether real-world maps could plausibly have been produced without intentional design choices affecting racial representation.