  Large spatial datasets often represent a number of spatial point processes
generated by distinct entities or classes of events. When crossed with
covariates, such as discrete time buckets, this can quickly result in a data
set with millions of individual density estimates. Applications that require
simultaneous access to a substantial subset of these estimates become resource
constrained when densities are stored in complex and incompatible formats. We
present a method for representing spatial densities along the nodes of sparsely
populated trees. Fast algorithms are provided for performing set operations and
queries on the resulting compact tree structures. The speed and simplicity of
the approach is demonstrated on both real and simulated spatial data.
