Recommended literature

Bischl, Bernd, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, and Zachary M. Jones. 2016. Mlr: Machine Learning in R. Journal of Machine Learning Research 17 (170): 1–5. http://jmlr.org/papers/v17/15-066.htmlExterner Link.

Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. 2013. Applied Spatial Data Analysis with R. 2nd ed. New York: Springer.

Brenning, A., M. Schwinn, A. P. Ruiz-Páez, and J. Muenchow. 2015. Landslide Susceptibility Near Highways Is Increased by 1 Order of Magnitude in the Andes of Southern Ecuador, Loja Province. Natural Hazards and Earth System Sciences 15 (1): 4557. https://doi.org/10.5194/nhess-15-45-2015Externer Link.

Gareth, James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics 103. New York: Springer.

Longley, Paul, Michael Goodchild, David Maguire, and David Rhind. 2015. Geographic Information Science & Systems. Fourth edition. Hoboken, NJ: Wiley.

Lovelace, Robin, Jakub Nowosad, and Jannes Muenchow. 2019. Geocomputation with R. The R Series. CRC Press. https://geocompr.robinlovelace.net/Externer Link.

Muenchow, Jannes, Patrick Schratz, and Alexander Brenning. 2017. RQGIS: Integrating R with QGIS for Statistical Geocomputing. The R Journal 9 (2): 40928. https://journal.r-project.org/archive/2017/RJ-2017-067/index.htmlExterner Link.

Pebesma, Edzer. 2018. Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.htmlExterner Link.

Pebesma, Edzer, and Roger Bivand. 2020. Spatial Data Science. CRC Press. https://keen-swartz-3146c4.netlify.comExterner Link.