Sparse Density Representations for Simultaneous Inference on Large Spatial Datasets

TL;DR

This paper introduces a novel sparse density representation method using tree structures that enables efficient simultaneous inference on large spatial datasets, addressing resource constraints and improving query performance.

Contribution

The paper proposes a new sparse density representation technique with fast algorithms for set operations, enhancing efficiency in handling large spatial datasets.

Findings

Demonstrates improved speed on real spatial data

Efficient set operations on sparse tree structures

Applicable to large simulated datasets

Abstract

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.