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In this article, we review and compare a number of methods of spatialprediction. To demonstrate the breadth of available choices, we consider bothtraditional and more-recently-introduced spatial predictors. Specifically, inour exposition we review: traditional stationary kriging, smoothing splines,negative-exponential distance-weighting, Fixed Rank Kriging, modifiedpredictive processes, a stochastic partial differential equation approach, andlattice kriging. This comparison is meant to provide a service to practitionerswishing to decide between spatial predictors. Hence, we provide technicalmaterial for the unfamiliar, which includes the definition and motivation foreach (deterministic and stochastic) spatial predictor. We use a benchmarkdataset of $\mathrm{CO}_{2}$ data from NASA's AIRS instrument to addresscomputational efficiencies that include CPU time and memory usage. Furthermore,the predictive performance of each spatial predictor is assessed empiricallyusing a hold-out subset of the AIRS data.

A Comparison of Spatial Predictors when Datasets Could be Very Large