Quantitative Methods Tutorials in R

A curated list of online tutorials for spatial analysis, including bayesian methods and machine learning

Spatial Analysis

Geocomputation with R (2019) - The online home of Geocomputation with R, a book on geographic data analysis, visualization and modeling published by CRC Press. Topics: Spatial Data Types in R, Mapping, Learning, examples in transportation and ecology.

Spatial Networks in R with sf and tidygraph (2019). Topics: cleaning networks; mapping; centrality; shortest paths

Drawing Beautiful Maps Programmatically with R, sf, and ggplot2 (2018)- Part 1 Basics, Part2 Layers, Part3 Layouts

Advanced Techniques with Raster Data (2018) - Part 1 Unsupervised Classification, Part2 Supervised Classification, Part 3 Exercises

Bayesian Methods

inlabru: an R package for Bayesian spatial modelling from ecological survey modelling (2019) https://doi.org/10.1111/2041-210X.13168

Pitfalls in the Implementation of Bayesian Hierarchical Modeling of Areal Count Data: An illustration using BYM and Leroux Models (2015) https://cran.r-project.org/web/packages/spam/vignettes/jss15.pdf

Bayesian Hierarchical Spatial Models : Implementing the Besag York Mollie model in stan (2019) - https://doi.org/10.1016/j.sste.2019.100301 https://mc-stan.org/users/documentation/case-studies/icar_stan.html

Using rstan and spdep for spatial modelling https://rpubs.com/chrisbrunsdon/carstan

Robust Gaussian Processes in stan (2017) - A really great discussion on using informative priors for the range of the covariance function. https://betanalpha.github.io/assets/case_studies/gp_part1/part1.html

Fast Hierarchical Gaussian Processes (2017): Kronecker Product space-time models in stan! http://sethrf.com/files/fast-hierarchical-GPs.pdf

Spatially Varying Coefficients in R (2019) - There’s no reason to still be using GWR. https://arxiv.org/pdf/1903.03028.pdf

Machine Learning

classification from Scratch (2018) 8 part series. Logistic, splines, knn, ridge/LASSO regression, Neural Nets, AVM, trees, random forests & boosting.