A Case Study Competition Among Methods for Analyzing Large Spatial Data

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 and Andrew Zammit-Mangion
Additional contact information
Matthew J. Heaton: Brigham Young University
Abhirup Datta: Brigham Young University
Andrew O. Finley: Brigham Young University
Reinhard Furrer: Brigham Young University
Joseph Guinness: Brigham Young University
Rajarshi Guhaniyogi: Brigham Young University
Florian Gerber: Brigham Young University
Robert B. Gramacy: Brigham Young University
Dorit Hammerling: Brigham Young University
Matthias Katzfuss: Brigham Young University
Finn Lindgren: Brigham Young University
Douglas W. Nychka: Brigham Young University
Furong Sun: Brigham Young University
Andrew Zammit-Mangion: Brigham Young University

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 3, No 2, 398-425

Abstract: Abstract 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.

Keywords: Big data; Gaussian process; Parallel computing; Low-rank approximation (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

Downloads: (external link)
http://link.springer.com/10.1007/s13253-018-00348-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jagbes:v:24:y:2019:i:3:d:10.1007_s13253-018-00348-w

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/13253

DOI: 10.1007/s13253-018-00348-w

Access Statistics for this article

Journal of Agricultural, Biological and Environmental Statistics is currently edited by Stephen Buckland

More articles in Journal of Agricultural, Biological and Environmental Statistics from Springer, The International Biometric Society, American Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().