Welcome to the Spatial Data for Restoration Decision-Making in the GGW Workshop!
This workshop introduces participants to practical ways of working with restoration data in a notebook environment. It builds from foundational concepts about spatial evidence and data access into guided analysis workflows using R in Google Colab.
The workshop is designed to be guided, progressive, and applied. Participants begin by understanding where restoration data comes from, how it can be accessed, and how it can be used for decision-making. They then work through two practical notebooks that combine basic programming with R, data handling, visualisation, and introductory spatial analysis workflows.
What you will do
Across the practical sessions, you will:
- Work with real datasets from LDSF-derived predictive maps
- Learn a simple, repeatable workflow for analysing data
- Explore patterns in climate, land health, and soil indicators
- Produce and interpret basic outputs
Each practical follows the same core workflow:
1. Load data ➨ 2. Inspect ➨ 3. Analyse ➨ 4. Visualise ➨ 5. Interpret
Practical notebooks
Day 2 (Tuesday, 24th March 2026)
Notebook 1: introduces the basics of working in R and Google Colab, then applies those skills to climate datasets.
Day 3 (Wednesday, 25th March 2026)
Notebook 2: builds on those foundations using land health and soil health datasets, before moving into simple interactive mapping and introductory spatial analysis.
Data and tools
You will work with small, workshop-ready datasets derived from LDSF data.These are provided as tabular datasets (CSV files), where each row represents a location and columns represent different indicators.
There are many ways to work with this type of data. In this workshop, we use:
- Google Colab (web-based notebook environment)
- R for data analysis
Getting started
To begin:
- Open Notebook 1: R basics and climate data
- Follow the instructions on the notebook page
- Open the notebook in Google Colab
Practical notebooks
This first notebook introduces the practical environment used throughout the workshop: R programming in Google Colab.
Participants begin by learning the basics of working in a notebook, including how to run code, create simple objects, understand packages and libraries, and load datasets into R.
The notebook then applies these skills to climate data, guiding participants through a workflow of loading, inspecting, checking, summarising, and visualising variables such as rainfall and temperature.
By the end of this notebook, participants should be able to produce and save a simple climate-related output.
This second notebook builds directly on the first. It begins with tabular land health and soil health datasets, using the same workflow of loading, checking, transforming, summarising, and visualising data.
Participants then move into introductory spatial workflows, including simple interactive maps with leaflet, working with spatial data frames using sf, and optional extensions that introduce raster data and summaries using terra.
The notebook is designed to show how restoration datasets can move from tables to maps and simple spatial summaries within one connected workflow.
