This workshop will explore examples of spatial modeling tasks (e.g., spatial interpolation from point data to gridded data) with machine learning methods. The content of the session will primarily focus on the spatial component (e.g., how to include spatial proximity as a predictor) although machine learning concepts will be discussed as relevant.
The goals of this session are to 1) introduce key concepts about incorporating spatial data in machine learning and 2) provide examples in Python on how to manipulate spatial datasets to use in machine learning functions, compare the performance of machine learning approaches for spatial prediction, and visualize observed spatial data and the prediction results.