I argue that the problem of redistricting is better solved by computational models than by humans, and that their adoption is not only an ethical and logistical possibility, but rather a vast improvement over current techniques.
Given n-dimensional data that we can classify into various categories, we can use KNN to predict what class an unknown n-dimensional data-point falls into based on its proximity to the nearest K points.