---
title: "Modelling Space and Time with GAMS: spatially and temporally varying coefficient models"
author: "Lex Comber"
date: "2024-06-03T09:27:14Z"
tags: [Models, Data Science, Machine Learning]
link: "https://bookdown.org/lexcomber/AGILE2024/"
length_weight: "2.9%"
repo: "rstudio/bookdown-demo"
pinned: false
---

This is a workshop introducing GGP-GAMs as a method for undertaking spatially and temporally varying coefficient models [...] GAM (General Additive Models) are emerging as the goto approach for all kinds of data science activities. GAMS perform as well or better than most machine learning models and they are relatively fast. They are powerful and quick but critically they offer a middle ground between overly simple but interpretable standard statistical approaches, and efficient but opaque machine leaning algorithms, where it is difficult to understand how one variable relates to an outcome. ...
