  Optimized spatial partitioning algorithms are the corner stone of many
successful experimental designs and statistical methods. Of these algorithms,
the Centroidal Voronoi Tessellation (CVT) is the most widely utilized. CVT
based methods require global knowledge of spatial boundaries, do not readily
allow for weighted regions, have challenging implementations, and are
inefficiently extended to high dimensional spaces. We describe two simple
partitioning schemes based on nearest and next nearest neighbor locations which
easily incorporate these features at the slight expense of optimal placement.
Several novel qualitative techniques which assess these partitioning schemes
are also included. The feasibility of autonomous uninformed sensor networks
utilizing these algorithms are considered. Some improvements in particle swarm
optimizer results on multimodal test functions from partitioned initial
positions in two space are also illustrated. Pseudo code for all of the novel
algorithms depicted here-in is available in the supplementary information of
this manuscript.
