Few weeks ago I attended a week-long workshop on Data Viz @CIID using the UN Sustainable Development Goals, in particular Climate Action, as a context and a starting point for our data-story.
We worked with D3, a JavaScript library, and searched the world bank database to find some interesting datasets.
As an environmental engineer working on circular economy I had always been interested in loops, flows and transformation of material into energy and back into material, always aware that nothing is created and nothing is destroyed but entropy is always growing. So I was looking for this macro story to tell.
I found an interesting data collection about the rate of agricultural land from 1961 till 2016 in all the world countries.
Recently in Italy, and maybe in Europe, everyone talk about how young people are going back to the country, working in the agricultural sector with a greater awareness and concern about climate change and food sovereignty; and a deeper knowledge about technologies and techniques like aquaculture or permaculture.
I wanted to check if data were maybe talking about a different story and immediately many other stories and angles emerged.
First of all Italy, as well as Europe and almost all the “old”, ”industrial” economies, dramatically reduced their rate of cultivated land. On the other side all the “emerging” economies increased this rate.
So I looked at the rate of urban population and overlapped this visualization on the previous one. But, as expected, these rates were always growing for all the countries analysed providing no answer or correlation with the agricultural land rate.
At this point the story emerged from my data was not the one I imagined at the beginning and this kind of visualization was not the right one anymore. Now I wanted to show how different countries had different starting points and were on different paths.
From a coding perspective this was a big step, for me, into the creation of an array combining the 2 different datasets. Besides that, since the steps left on this plot by each country visualized were not associated with a visible time direction, I decided to include the “year” in the array and to add an “History” Button visualizing at each click the next step year after year.
When I presented the Viz on the final day it was quite impressive to see how the whole audience was not able to predict the path these countries were on, even if I have just shown them the previous visualization.
I had the proof on my intuition.
Clearly this is one specific story, definitely true, but far from being exhaustive and far from any real answer to my initial question.
What I would like to do next would be to explore the way to
“provide users with a structured way to explore a complex phenomenon on their own terms… rather than a pre-digested narrative with a surprise at the end.” Moritz Stefaner
