geocompx hosts free resources on reproducible geographic data analysis, modelling and visualization with open source software

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.

Spatial machine learning with mlr3

Adam Mickiewicz University in Poznań

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

Spatial machine learning with R: caret, tidymodels, and mlr3

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.

Specialized R packages for spatial machine learning: An introduction to RandomForestsGLS, spatialRF, and meteo

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.

Spatial machine learning with the tidymodels framework

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.

Geocomputation with Python has arrived

Inset maps enable multiple places to be shown in the same geographic data visualisation, as described in the Inset maps section (8.2.7) of our open source book Geocomputation with R. The topic of inset maps has gained attention and recently Enrico Spinielli asked inset maps could be created for data in unusual coordinate. R’s flexibility allows inset maps to be created in various ways, using different approaches and packages.

Inset maps with ggplot2

This post explains how to quickly get key R packages for geographic research installed on Ubuntu, a popular Linux distribution.  A recent thread on the r-spatial GitHub organization alludes to many considerations when choosing a Linux set-up for work with geographic data, ranging from the choice of Linux distribution (distro) to the use of binary vs or compiled versions (binaries are faster to install). This post touches on some of these things.

Installing spatial R packages on Ubuntu

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.

Spatial machine learning with caret

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!

Geocomputation with R competition: book cover for the 2nd edition

Introduction

In this blog post, we talk about our experience teaching R and Python for geocomputation. The focus of the blog post is on geographic vector data, meaning points, lines, polygons (and their ‘multi’ variants) and the attributes associated with them. Geographic data analysis is a broad topic and in a later post we will cover raster data, meaning gridded data such as satellite images.

Geographic data analysis in R and Python: comparing code and outputs for vector data

Currently, hundreds of R packages are related to spatial data analysis. They range from ecology and earth observation, hydrology and soil science, to transportation and demography. These packages support various stages of analysis, including data preparation, visualization, modeling, or communicating the results.

Recent changes in R spatial and how to be ready for them

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;

Shapefile must… be replaced

TL;DR: Please help us by filling out the survey at https://forms.gle/nq9RmbxJyZXQgc948. It’s been almost 3 years since the first edition of Geocomputation with R was published back in 2019. It’s been an amazing journey for this open source book on geographic data analysis, visualization and modeling since then. We have reached almost 500k people via the website at https://geocompr.robinlovelace.net/ and the physical book.

Geocomputation with R: Second Edition feedback

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

Support geocompx and the development of open-source geocomputation resources!

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.

Second edition of Geocomputation with R is complete

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.

An overview of the rsi R package for retrieving satellite imagery and calculating spectral indices

Introduction

The 2nd edition of Geocomputation with R is due to be published in 2024. Now, we’re looking for a new cover image and we’d like your help.

Geocomputation with R comptetition: book cover for the 2nd edition

Geocomputation with Python

, also known as

 geocompy

, is an open-source book on geographic data analysis with Python.

 1

It is written by Michael Dorman, Anita Graser, Robin Lovelace, and me with contributions from others. You can find it online at py.geocompx.org. We started working on this project at the beginning of 2022, and since then, we have been making steady progress.

Say hi to ‘Geocomputation with Python’

The issue Three popular R packages for spatial data handling won’t be available on CRAN after October 2023.

 1

These packages are:

 rgdal

: a package that provides bindings to the GDAL and PROJ libraries. In other words, it gives a capability of reading and writing spatial data formats, creating coordinate reference systems and projecting geometries.

Upcoming changes to popular R packages for spatial data: what you need to do

Geocomputation with R is a book on geographic data analysis, visualization, and modeling. Each chapter

 1

ends with a set of exercises allowing the readers to test how well they understand each presented concepts and approaches. However, exercises are often not enough without their solutions, which provides a way to compare readers’ answers with an expected one.

Automatic website for the Geocomputation with R solutions

Introduction

This workbook outlines key concepts and functions related to map projections — also referred to as coordinate reference systems (CRSs) — and transformation of geographic data from one projection to another. It is based on the open source book

 Geocomputation with R

, and Chapter 6 in particular.

Geographic projections and transformations

Introduction

The coronavirus pandemic is a global phenomenon that will affect the lives of the majority of the world’s population for years to come.

Demo of reproducible geographic data analysis: mapping Covid-19 data with R

This vignette builds on the making maps chapter of the Geocomputation with R book. Its goal is to demonstrate all possible map styles available in the

 tmap

package.

Map coloring: the color scale styles available in the tmap package

This vignette builds on the making maps chapter of the Geocomputation with R book. Its goal is to demonstrate how to set and modify grids and graticules in the

 tmap

package.

Grids and graticules in the tmap package

Geocomputation with R is a book on geographic data analysis, visualization, and modeling. The First Edition was first published as a physical book in 2019 and we have reached 500k people through via the website at https://geocompr.robinlovelace.net/ and the physical book since then.

Progress update: Geocomputation with R Second Edition Part 1

sf

has transformed the R ecosystem and helped pave the way for wider adoption of geospatial workflows by R users.

R-spatial beyond sf

The R programming language has, over the past two decades, evolved substantial spatial data analysis capabilities, and is now one of the most powerful environments for undertaking geographic research using a reproducible command line interface. Currently, dedicated R packages allow to read spatial data and apply a plethora of different kinds of spatial methods in a reproducible fashion.

Conversions between different spatial classes in R

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.

Specialized R packages for spatial cross-validation: sperrorest and blockCV

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.

New, work-in-progress book on spatial data visualization in R

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.