  We present a modification of the standard geohash algorithm based on maximum
entropy encoding in which the data volume is approximately constant for a given
hash prefix length. Distributed spatiotemporal databases, which typically
require interleaving spatial and temporal elements into a single key, reap
large benefits from a balanced geohash by creating a consistent ratio between
spatial and temporal precision even across areas of varying data density. This
property is also useful for indexing purely spatial datasets, where the load
distribution of large range scans is an important aspect of query performance.
We apply our algorithm to data generated proportional to population as given by
census block population counts provided from the US Census Bureau.
