Learning to See: Visual Inspirations and Data Visualization
How Abstract Art Can Help Understanding the Global Brain Drain
Only apparently unrelated, abstract art and data visualization actually have a lot more in common than what one would expect, and can be considered by some means two very close disciplines.
A study on “Early Abstract Art and Experimental Gestalt Psychology” by Crétien van Campen of MIT draws the conclusion that the same theories that are universally recognized as a basis for perception studies to support effective data visualization, have actually also deeply influenced the work of abstract artists such as Kandinsky or Mondrian.
This common root that we can trace back to German psychologists of the early 20th century reveals how, while clearly pursuing different goals, abstract artists and data visualization designers both draw on common perception principles and apply them to simple shapes and a definite range of colors to create basic visual compositions that please the eye and, hopefully, deliver a message.
To be a data visualization designer you have to find new ways to attract people’s attention through new languages and new solutions that besides being functional, accurate and appropriate must be magnetic and surprising.
To this regard, I believe that learning how to see is essential to learn how do design.
I will present here a specific example, a data visualization project that deeply relied on a clear visual inspiration from abstract art to derive guiding principles that informed both the data analysis and the visualization.
This visualization we designed a while ago for the Sunday cultural supplement of Corriere della Sera explores the phenomenon of global “brain drain” in science, with an eye towards understanding the reasons why researchers might choose to leave their countries of origin and pursue careers elsewhere.
And we explored the topic by borrowing several rules of visual composition by artists such as Malevich or Mondrian.
The migration of educated and skilled personnel in search of better work standards has been going on for decades. Typically, workers move from their countries of origin to look for higher salaries, compelling career perspectives and an improved quality of life.
But which are the conditions that influence the emigration of highly skilled people? And which are the countries that are mostly affected?
Combining three sets of data — a World Bank survey, results from a research paper titled Foreign Born Scientists: Mobility Patterns for Sixteen Countries, and The Times’ ranking of the world’s best universities — we contrasted the number of researchers per million people (y-axis) with the percentage of the country’s GDP dedicated to research and development (x-axis).
To provide a further context to interpret the primary set of data, we also displayed the unemployment rate, the female employment rate, the percentages of foreigners and emigrants in population, emigrant researchers, and emigrant researchers returning to their country of origin. Moreover, we included the principal relationships among the countries as regards migration flows.
As Brainpickings points out, some interesting patterns emerge from this dense piece: “Japan, held as a paragon of technological innovation, actually attracts very few foreign researchers. Denmark, despite a GDP budget significantly larger, doesn’t do too much better than countries like Belgium, France and Germany. Canada, Australia, the United States, and Switzerland attract — and export — the greatest number of scientists.”
While we were analyzing the data, I payed a visit to the fascinating MoMA’s Inventing Abstraction exhibition;
I was looking for a way to visually correlate those many parameters about researchers per each country and, while walking past Mondrian’s, Malevich’s, and Kandinsky’s art pieces, I started to envision each country as a compound element, the parameters of which should have been visually related by the positioning, rotation and spatial correlation of those geometrical shapes I was sketching down during my visit.
Consequently, we normalized the numbers: we represented each value as a function of the country’s population, thus we are displaying relative percentages to let readers visually compare the relevant information.
We are not certainly visualizing big data here but, until the visualization wasn’t complete, we ourselves wouldn’t notice many unexpected potential correlations among the data.
For example, in general, we notice that researchers move away much more than regular people, in fact the red and blue solid histograms are almost always longer than the hollow ones.
However, we see an exception in Latin countries like Spain and Italy, which import proportionally more regular workers.
But what are the conditions that influence the migrations?
It seems to be not so much a question of GDP per capita, and the presence of top universities is important but not essential. Apparently a higher female employment is correlated with attracting more foreign researchers, and generally the nations where English is the primary language perform at their best.
Personally, I spend a great amount of time looking at different types of images, absorbing their qualities.
And I do this not only for amusement, but mainly as a necessary practice to force myself to ask the questions: “What is that I like of what I see? What elements, aspects and features am I appreciating and why?”
I realized I am mostly inspired by visual languages that are somehow already conventional, the aesthetics of which are familiar to our minds: if a set of aesthetic rules for shapes, for colors, and for spatial composition works in a context I observe, I believe there should be a way to apply them to the designs I am working on.
The visual contexts I am referring to are abstract art, but also the repetitive aesthetics of music notations, especially contemporary music notations, or the layering systems of architectural drawings, or even the shapes and features of objects and natural elements: visual environments our minds can refer to even without really getting it.
Learning to see and to understand what are the aesthetic qualities that attract our eyes about our surroundings is essential for creators of any kind.
What I always do when I start every kind of project is allowing myself to make the time to get truly inspired by the world that is around me.
Looking for clues in unusual contexts is a valuable way for a creator to discover and dissect the aesthetic qualities of all the things that we naturally like, as a constant resource for inspiration, and in order to be able to abstract them and introduce them as core principles and guidelines in our work.
Just by paying attention to what happens in our mind while looking at the world around us we can force ourselves to learn how to see, and how to recognize the qualitative features of all the different images we see.
With time we can learn to parse these features and recall them while creating something new.
This is an open invitation to the art of observation.
If you liked this post, complement it with Beautiful Reasons, Engaging Aesthetics for Data Narratives, and with The Architecture of a Data Visualization, Multilayered Storytelling through Info Spatial Compositions or explore the project on Behance.
Accurat is a data-driven research, design and innovation firm.
We help our clients understand, communicate and leverage their data assets
through static and interactive data visualizations, interfaces and analytical
tools that provide comprehension, insight and engagement.
We have offices in Milan and New York.