  Density estimates based on point processes are often restrained to regions
with irregular boundaries or holes. We propose a density estimator, the
lattice-based density estimator, which produces reasonable density estimates
under these circumstances. The estimation process starts with overlaying the
region with nodes, linking these together in a lattice and then computing the
density of random walks of length k on the lattice. We use an approximation to
the unbiased crossvalidation criterion to find the optimal walk length k. The
technique is illustrated using walleye (Sander vitreus) radiotelemetry
relocations in Lake Monroe, Indiana. We also use simulation to compare the
technique to the traditional kernel density estimate in the situation where
there are no significant boundary effects.
