Part 1: Creating a Data Visualization

1. Choose a Greenhouse Gas Removal Research Area

There is a wiki page on 9 areas to explore and help you choose one that clicks with you.

Once you've chosen your topic, look to see if there are any associated projects in your local area that you could reach out to for additional information and support. Connecting the big ideas to real places and people could make your visualisation more powerful and give you the opportunity to connect to interesting work happening close to you.

Don't rush this step, exploring the topics will be a year long project supported by our community in Discord and an opportunity for all of us to learn together 🙂

• To continue, we will choose Topic 4, Create and manage woodlands.

2. Collect and form relevant datasets

• Gather relevant data for your chosen area. For woodlands, you might look for data on forest cover, carbon sequestration rates, deforestation rates, and reforestation efforts. We will try and supplement the wiki pages with guides to open-source datasets to help you.

3. Choose a tool or library for creating your visualization

The more popular options include:

• Python libraries like Matplotlib, Seaborn, Plotly
• R libraries like ggplot2
• Online tools like Tableau or Google Data Studio

4. Design the Visualization

Decide on the type of visualization that best represents your data. For example, you might use:

• Line charts to show changes in forest cover over time
• Bar chart to compare carbon sequestration rates of different tree species
• A map to visualize deforestation and reforestation areas

5. Implementing the Visualization

• Write the code or use the tool to create your visualization.

Here is a simple example using Python and Plotly to create a bar chart of carbon sequestration rates of different tree species (sample code is included separately in the samp.py format)

Part 2: Reflecting on Storytelling Techniques

6. Identify the Key Message

• Determine the main point you want to communicate. For example, "Managing woodlands effectively can significantly increase carbon sequestration and help mitigate climate change."

7. Use Visual Elements to Support the Story

• Ensure that your visualization highlights the key message. Use colors, annotations, and labels to draw attention to important data points.

8. Provide Context

• Include background information and context to help the audience understand the significance of the data. For example, explain why certain tree species are more effective at sequestering carbon.

9. Engage the Audience

• Make your visualization interactive if possible. Allow users to explore the data themselves by hovering over data points or filtering the data.

10. Simplify Complex Information

• Break down complex information into simpler, digestible parts. Use clear and concise language in your annotations and descriptions.

11. Tell a Story

• Structure your visualisation and accompanying text to tell a coherent story. Start with an introduction, present the data, and conclude with the implications and potential actions.

12. Extend your visualisation into a storymap

• You may wish to take your visualisation further by presenting it in a story map and working with others.