TEACHING & CODE

EDS 222: Statistics for Environmental Data Science

This course is a core class in the Masters of Environmental Data Science program at the Bren School. We cover fundamental statistical concepts and tools, and then apply and expand upon those tools to learn some temporal and spatial statistical methods that are particularly helpful in environmental data science. Our course website is here, and all slides and labs are available on the course GitHub. 

Multi-task Observation using SAtellite Imagery and Kitchen Sinks (MOSAIKS)

MOSAIKS is a task-agnostic approach to linking satellite imagery and machine learning for prediction of ground conditions at scale. More information can be found here, and our API hosting downloadable imagery features is here. Check out the Resources page for examples of how to use MOSAIKS features for prediction in R and Python.

stagg:: A data pre-processing R package for climate impacts analysis

stagg is an R package that transforms raw gridded climate data into tabular administrative-level variables intended for use in climate econometrics analyses. Flexible options let users control specifications like nonlinear transformations, weighted spatial aggregation, and temporal aggregation with a few lines of code. Our GitHub package is here. Tyler Liddell made some additional helpful resources: here is a cheatsheet and and here is a scientific poster. It's a work in progress -- please send us feedback!