  Using two-dimensional numerical simulations of gravitational clustering, with
initial power spectra of a power-law form with index n, we compare the
properties of voids in real space to their properties in redshift space. Both
the void probability function (VPF) and the underdense probability function
(UPF) are enhanced in redshift space. The enhancement is greatest in the limit
n=-2. The VPF and UPF treat voids statistically and assume that two-dimensional
voids are circular; we present an algorithm which detects individual voids.
Voids found by this algorithm are ellipses whose enclosed density of galaxies
falls below a threshold density. When voids are identified using this
algorithm, the mean void size and the maximum void size both increase in going
from real space to redshift space. The increase is greatest in the limit n=-2.
In redshift space, the principal axes of the largest voids in the n=2 and n=0
simulations show a statistically significant tendency (at the 95% confidence
level) to be distributed anisotropically, relative to the line of sight from
the origin to the void center.
