mlsanityData to 7 Oct 2026

A Case Study Competition Among Methods for Analyzing Large Spatial Data

arXiv 1710.05013 ↗First posted 13 Oct 2017stat.MEcited by 44

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Abstract (arXiv)

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 which 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.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2017.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 6; 2024: 9; 2025: 13; 2026: 2.

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  1. 2022Statistical Deep Learning for Spatial and Spatio-Temporal Datacited by 15
  2. 2023Neural networks for geospatial datacited by 11
  3. 2023Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoodscited by 10
  4. 2022Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernelscited by 9
  5. 2021MuyGPs: Scalable Gaussian Process Hyperparameter Estimation Using Local Cross-Validationcited by 8
  6. 2022Scalable Gaussian-process regression and variable selection using Vecchia approximationscited by 8
  7. 2023Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Modelscited by 6
  8. 2024Spatial Conformal Inference through Localized Quantile Regressioncited by 6
  9. 2021Faster Kernel Interpolation for Gaussian Processescited by 5
  10. 2022Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Treescited by 5
  11. 2023Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimizationcited by 4
  12. 2024Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Datacited by 4
32 more
  1. 2021Scalable Spatiotemporally Varying Coefficient Modelling with Bayesian Kernelized Tensor Regressioncited by 3
  2. 2025All Emulators are Wrong, Many are Useful, and Some are More Useful Than Others: A Reproducible Comparison of Computer Model Surrogatescited by 3
  3. 2021Modeling Massive Spatial Datasets Using a Conjugate Bayesian Linear Regression Frameworkcited by 2
  4. 2022Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Datacited by 2
  5. 2024Compactly-supported nonstationary kernels for computing exact Gaussian processes on big datacited by 2
  6. 2025New Bounds for Sparse Variational Gaussian Processescited by 2
  7. 2025Bayesian Data Sketching for Varying Coefficient Regression Modelscited by 2
  8. 2025Fast Gaussian Process Approximations for Autocorrelated Datacited by 2
  9. 2021On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approachcited by 1
  10. 2022Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputationcited by 1
  11. 2022Dynamic Bayesian Learning for Spatiotemporal Mechanistic Modelscited by 1
  12. 2022Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)cited by 1
  13. 2023Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysiscited by 1
  14. 2024Implementation and Analysis of GPU Algorithms for Vecchia Approximationcited by 1
  15. 2024Superior Scoring Rules for Probabilistic Evaluation of Single-Label Multi-Class Classification Taskscited by 1
  16. 2024Probabilistic Spatiotemporal Modeling of Day-Ahead Wind Power Generation with Input-Warped Gaussian Processescited by 1
  17. 2024Correspondence of NNGP Kernel and the Matern Kernelcited by 1
  18. 2025Diffusion Models for Inverse Problems in the Exponential Familycited by 1
  19. 2025Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Associationcited by 1
  20. 2025LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Datacited by 1
  21. 2026Spatio-Temporal Gaussian Process for Building Terrain-Incorporating Wind Power Curvescited by 1
  22. 2022Scalable Gaussian Process Hyperparameter Optimization via Coverage Regularizationcited by 0
  23. 2023Spatial Process Approximations: Assessing Their Necessitycited by 0
  24. 2024Adaptive Basis Function Selection for Computationally Efficient Predictionscited by 0
  25. 2024When the whole is greater than the sum of its parts: Scaling black-box inference to large data settings through divide-and-conquercited by 0
  26. 2025DeepRV: Accelerating Spatiotemporal Inference with Pre-trained Neural Priorscited by 0
  27. 2025Adaptive Resolution for Finite-Rank Gaussian Processescited by 0
  28. 2025Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processescited by 0
  29. 2025Fast Gaussian process inference by exact Mat'ern kernel decompositioncited by 0
  30. 2025A comparison between geostatistical and machine learning models for spatio-temporal prediction of PM2.5 datacited by 0
  31. 2025A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applicationscited by 0
  32. 2026Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matricescited by 0