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A Case Study Competition among Methods for Analyzing Large Spatial Data

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  • Matthew J. Heaton
  • Abhirup Datta
  • Andrew O. Finley
  • Reinhard Furrer
  • Joseph Guinness
  • Rajarshi Guhaniyogi
  • Florian Gerber
  • Robert B. Gramacy
  • Dorit Hammerling
  • Matthias Katzfuss
  • Finn Lindgren
  • Douglas W. Nychka
  • Furong Sun
  • Andrew Zammit-Mangion

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Original languageEnglish
Number of pages28
JournalJournal of Agricultural, Biological and Environmental Statistics
Early online date14 Dec 2018
Publication statusE-pub ahead of print - 14 Dec 2018


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.

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