2015/10/02 by Taylor Arnold, Arnold, Taylor
Computer Science · Environmental Science · #Bayesian Methods and Mixture Models #Computation (stat.CO) #Data Management and Algorithms #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Soil Geostatistics and Mapping
paper · pdf · doi:10.48550/arxiv.1510.00755
openalex publication_date 2015/10/02 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.