The geocomp x project provides open-source, open-access resources for learning and teaching about geocomputation in multiple programming languages. The year 2025 was special for the project, marked by various milestones and progress. This post summarizes the key updates from 2025, outlines the current work in progress, and speaks on how you can support the project.

We want to introduce a new, work-in-progress book on spatial data visualization in R using the tmap package. The current version of the book, titled Spatial Data Visualization with tmap: A Practical Guide to Thematic Mapping in R , is available online at https://tmap.geocompx.org/. This blog post provides an overview of the book, its current status, and how you can get involved in its development.

This is the sixth and the final blog post in a 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 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.