Comparing Spatial Regression to Random Forests for Large Environmental Data Sets

• Activity Number: 671 - Environmental and Ecological Monitoring
• Type: Contributed
• Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Eric Fox* and Jay Ver Hoef and Anthony Olsen
• Companies: US EPA, Western Ecology Division and NOAA, National Marine Mammal Laboratory and US EPA, Western Ecology Division
• Keywords: spatial regression ; random forests ; National Rivers and Streams Assessment ; macroinvertebrate multimetric index

Abstract

Environmental data may be "large" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method.