  We investigate the distribution of partisanship in a cross-section of ten
diverse States to elucidate how votes translate into seats won and other
metrics. Markov chain simulations taking into account partisanship distribution
agree surprisingly well with a simple model covering only equal voting
population-weighted distributions of precinct results containing no spatial
information. We find asymmetries where Democrats win fewer precincts than
Republicans but do so with large marjorities. This skew accounts for persistent
Republican control of State Legislatures and Congressional seats even in some
states with statewide vote majorities for Democrats.
  Despite overall results showing Republican advantages in many states based on
mean results from simulations covering many random scenarios, the simulations
yield a wide range in metrics, suggesting bias can be minimized better by
selecting districting plans with low values for efficiency gap than by
selecting plans with values near the means for the ensemble of random
simulations.
  We examine constraints on county splits to achieve higher compactness and
investigate policies requiring cohesiveness for communities of interest as to
screen out the most obvious gerrymanders. Minimizing county splits does not
necessarily reduce partisan bias, except for Pennsylvania, where limiting
county splits appears to reduce bias.
