  As granular data about elections and voters become available, redistricting
simulation methods are playing an increasingly important role when legislatures
adopt redistricting plans and courts determine their legality. These simulation
methods are designed to yield a representative sample of all redistricting
plans that satisfy statutory guidelines and requirements such as contiguity,
population parity, and compactness. A proposed redistricting plan can be
considered gerrymandered if it constitutes an outlier relative to this sample
according to partisan fairness metrics. Despite their growing use, an
insufficient effort has been made to empirically validate the accuracy of the
simulation methods. We apply a recently developed computational method that can
efficiently enumerate all possible redistricting plans and yield an independent
uniform sample from this population. We show that this algorithm scales to a
state with a couple of hundred geographical units. Finally, we empirically
examine how existing simulation methods perform on realistic validation data
sets.
