This is the fifth part of a blog post series on spatial machine learning with R. You can find the list of other blog posts in this series in part one.

This is the fourth part of a blog post series on spatial machine learning with R. You can find the list of other blog posts in this series in part one. Aims of this post This post aims to give a minimal example on how to use mlr3 for a spatial prediction task. We want to get from measurements of temperature at specific locations in Spain to a spatially continuous map of temperature for all of Spain.

This is the third part of a blog post series on spatial machine learning with R. You can find the list of other blog posts in this series in part one. Introduction In this blog post, we will show how to use the tidymodels framework for spatial machine learning. The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles.

This is the second part of a blog post series on spatial machine learning with R. You can find the list of other blog posts in this series in part one. Introduction This document shows the application of caret for spatial modelling at the example of predicting air temperature in Spain.

This is the first part of a blog post series on spatial machine learning with R. Part 2: Spatial machine learning with caret Part 3: Spatial machine learning with the tidymodels framework Part 4: Spatial machine learning with mlr3 Part 5: Specialized R packages for spatial machine learning: An introduction to RandomForestsGLS, spatialRF, and meteo Part 6: Specialized R packages for spatial cross-validation: sperrorest and blockCV The R

We are excited to announce that the first edition of Geocomputation with Python is now complete. You can find the online version of the book at py.geocompx.org. The book is also available for purchase from Routledge.com, Amazon.com, Amazon.co.uk, and other booksellers. Geocomputation with Python ( geocompy ) is an open-source, introductory resource for working with geographic data with Python.

As 2024 comes to an end, we have things to celebrate in the geocompx community, including the completion of two books: the second edition of Geocomputation with R and the first edition of Geocomputation with Python . Both books are open-source, can be accessed by anyone for free online, and will be on sale soon (watch this space). We are proud of the work we have done, grateful for the contributions we have

We are excited to announce that the second edition of Geocomputation with R is (almost) complete. It took us about three years to update and improve the book. This blog post summarizes the process and lists things we added and changed.

The ESRI Shapefile format is possibly the most popular data format for storing spatial vector data. It is well-established, widely supported, and can be used in most GIS software. At the same time, it has many shortcomings and limitations: it is not a single file, but a collection of files; it has a limit of 2GB per file;

Note This is a guest blog post written by Benjamin Nowak. He is the winner of the Geocomputation with R book cover competition for the second edition of the book. Congratulations!

Introduction rsi is a recent R package developed by Michael Mahoney and funded by Permian Global Research. It offers features that simplify the process of acquiring spatial data from STAC ( SpatioTemporal Asset Catalog ) and calculating spectral indices based on such data. A unique feature of this package is its source for the indices.