Colin Ware, who is a visualization and vision scientist researcher,
who actually wrote one of the best data visualization books out there,
I would say the best visualization book in terms of
connecting vision science with visualization,
proposes in his book a color palette of 12 colors.
They are mostly based on the study that
I have just shown you and some other pieces of information.
So, this is a useful resource.
But again, as you can see,
you can't really go past beyond a few different colors.
These colors are also been selected because they
are the colors that are consistently named across different cultures.
So, it doesn't matter the culture you're
in or the culture in which these colors are tested.
People are able to assign unique names to all of these colors,
which of course makes this palette particularly strong and robust.
So, let's talk about color conventions and semantics.
When using color, you may be tempted to use
some specific shades because they have semantic association.
So, typically, one of the most common is red communicates danger,
or bad, or alert,
be careful, and green tends to be okay,
good, and so on.
But you have to be careful because these conventions are not necessarily universal.
So, when you design visualizations that rest upon some semantic associations,
you always have to be careful and think about who the readers are going to be,
whether this can be confused or not.
So, there are some positive and negative aspects of trying to
use color in a way that there is some sort of semantic hook associated to them.
So, I would say that the positive side of
using semantic associations is that they would be very intuitive.
So, a person reading the visualization that uses
this semantic encoding would probably not need to read a legend,
for instance, in order to understand what is going on.
On the other hand,
they can also very easily confusing, especially across cultures.
So, it's a balancing act,
and you have to be really, really careful there.
Sometimes, if used appropriately,
they can be extremely powerful.
But if not, they can damage the comprehension and intuition of the chart.
One special color that we have,
that has a very interesting semantic association is gray.
So, shades of gray tends to be perceived as being colors that have no shade,
no color in some sense.
Because of that, typically,
the semantic association that we give to any element in
a visualization that is gray is that it belongs to an unspecified class or category,
and sometimes this is as useful tool.
So, let me show you an example.
So, this is a chart created by visualization designer Moritz Stefaner,
and it shows the evolution of
child mortality over time for a number of different countries.
But as you can see, in this chart,
there are only two countries that are actually colored with some color,
and the rest is gray.
So, the idea here is that since most of the countries are colored as gray,
they are perceived in this particular chart as
not important and not classified with anything.
As I said, sometimes is useful to use gray with this purpose.
Say, if you have a given dataset with four or five classes,
but one of these classes actually does mean uncategorized,
you may want to associate the gray color to the class uncategorized.
So, it's a very powerful tool.
Enrico BertiniAssociate Professor
Cristian FelixPhD. Candidate
