The Science of Where in a Warming Planet: Spatial vs Non-Spatial Machine Learning

The intersection of machine learning and GIS is getting broader as we ask increasingly pragmatic questions related to complex spatial phenomena. Whether it is predicting traffic patterns in L.A. or the probability of being hit by the next big storm, we need answers to critical questions to make impactful decisions. In this blog, we'll explore an essential component needed towards answering such a question: what will the future climate be in U.S.? This question requires calibrating a global climate model with spatially-limited local temperature measurements. In a planet that is constantly warming, calibrating global climate models is vital to answer questions ranging from what will the average temperature be in Redlands in November 2050 to which Canadian cities will be wine country in the future.