A retrospective of 15 years of data visualization projects
October 24th, 2024
4 min
Datawrapper lets you show your data as beautiful charts, maps or tables with a few clicks. Find out more about all the available visualization types.
Our mission is to help everyone communicate with data - from newsrooms to global enterprises, non-profits or public service.
We want to enable everyone to create beautiful charts, maps, and tables. New to data visualization? Or do you have specific questions about us? You'll find all the answers here.
Data vis best practices, news, and examples
250+ articles that explain how to use Datawrapper
Answers to common questions
An exchange place for Datawrapper visualizations
Attend and watch how to use Datawrapper best
Learn about available positions on our team
Our latest small and big improvements
Build your integration with Datawrapper's API
Get in touch with us – we're happy to help
This article is brought to you by Datawrapper, a data visualization tool for creating charts, maps, and tables. Learn more.
Ignore the in-betweens to find yourself
It’s been another cold Winter week here in Berlin (and Chicago, I’ve heard). Time for a heated topic: climate change. It’s perfect for data visualization; crazy data-heavy & crazy important at the same time. Seriously, there can’t be enough data vis about climate change. This week, I want to demonstrate its local consequences:
We can see that the temperature increased over the last sixty years in every single city mapped. In some areas (like Greece) just by 1.5 to 2 degrees, in some areas (Finland) up to 4 degrees. Let’s remind ourselves that this map doesn’t show the temperatures: It’s still colder in Finland than in Greece, most of the time. But the map shows us that, since 1960, it became a bit less cold.
It’s a weird idea to only show the temperature change in cities. As if it doesn’t get warmer in-between them. As if cities somehow face a different, harsher fate. Quite the opposite is the case: The temperature has risen the most in areas with few big cities. Like, you know, at the North Pole.
But there’s one big advantage to cities: People live there. People who read data visualizations. People who like to find themselves on a map. People who, if I did something right, look at this map, find their city and think “Climate change has an effect on me, personally, on my family and friends. Well. That sucks.”
If we can, we should always find a connection between our data and our reader’s lives. How is our data an explanation for what they’ve encountered in the past? And how can our information change their behavior in the future? Offering answers to these questions won’t just make our readers care more – it’s also proof for us that we created something worthwhile, something they should care about.
Visualizing climate change comes with a tricky problem: Small temperature increases of a few degrees Celsius are a huge deal for us in the long-term, but not in the short-term. If you want to learn more about it, have a look at a Weekly Chart I wrote a few months ago: “The challenges of visualizing climate change”. I’ll see you next week!
Comments