A Case Study Competition Among Methods for Analyzing Large Spatial Data

By M. Heaton, Abhirup Datta, Andrew O. Finley, Reinhard Furrer, Joseph Guinness, Rajarshi Guhaniyogi, Florian Gerber, Robert B. Gramacy, Dorit M. Hammerling, Matthias Katzfuss and 4 more. Journal of Agricultural Biological and Environmental Statistics, 2017

458 citations recorded by the source corpus.

The Gaussian process is an indispensable tool for spatial data analysts. The onset of the “big data” era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This study provides, first, an introductory overview of several methods for analyzing large spatial data. Second, this study describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Specifically, each research group was provided with two training datasets (one simulated and one observed) along with a set of prediction locations. Each group then wrote their own implementation of their method to produce predictions at the given location and each was subsequently run on a common computing environment. The methods were then compared in terms of various predictive diagnostics. Supplementary materials regarding implementation details of the methods and code are available for this article online.

Subfields: Artificial Intelligence, Economics and Econometrics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/8d381a63294d39faff38bab716cea2a41dea6b17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Economics and Econometrics

In Environmental Engineering

Citations in the corpus

Basis-Function Models in Spatial Statistics (2021, methodology): “Bradley et al. (2016a) used a validation approach to make such a comparison from small, medium, and large data sets, and the basis-function models performed very well, as they did under the common-task-framework design in Heaton et al. (2019).”
A General Framework for Vecchia Approximations of Gaussian Processes (2017, methodology): “kle and Cressie, 1999; Quinonero-~ Candela and Rasmussen, 2005; Banerjee et al., 2008; Cressie and Johannesson, 2008; Katzfuss and Cressie, 2011). Several other approaches are described in Section 3. Heaton et al. (2019) review and compare many of these methods, plus several algorithmic approaches (”