Year: 2015
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Arnold, Taylor
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1897622370