Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments

TL;DR

This paper develops practical, scalable Bayesian methods for analyzing massive spatial datasets efficiently on modest computing environments, making advanced spatial analysis accessible to practitioners.

Contribution

It introduces easily implementable hierarchical Bayesian models that deliver near-equivalent inference to more complex methods using standard software on modest hardware.

Findings

Achieves rapid Bayesian inference on large spatial data

Maintains inference accuracy comparable to more expensive methods

Provides guidelines for practical efficiency and implementation

Abstract

With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has generated substantial interest over the last decade, already too vast to be summarized here, in scalable methodologies for analyzing large spatial datasets. Scalable spatial process models have been found especially attractive due to their richness and flexibility and, particularly so in the Bayesian paradigm, due to their presence in hierarchical model settings. However, the vast majority of research articles present in this domain have been geared toward innovative theory or more complex model development. Very limited attention has been accorded to approaches for easily implementable scalable hierarchical models for the practicing scientist or spatial analyst. This article is submitted to the Practice section of the journal with the aim of developing massively scalable Bayesian approaches that can rapidly deliver Bayesian inference on spatial process that are practically indistinguishable from inference obtained using more expensive alternatives. A key emphasis is on implementation within very standard (modest) computing environments (e.g., a standard desktop or laptop) using easily available statistical software packages without requiring message-parsing interfaces or parallel programming paradigms. Key insights are offered regarding assumptions and approximations concerning practical efficiency.