Categorical Color Scales Part 2

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From the course by New York University Tandon School of Engineering

Information Visualization: Applied Perception

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New York University Tandon School of Engineering

Information Visualization: Applied Perception

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Course 2 of 4 in the Specialization Information Visualization

This module aims at introducing fundamental concepts of visual perception applied to information visualization. These concepts help the student ideate and evaluate visualization designs in terms of how well they leverage the capabilities of the human perceptual machinery.

From the lesson

Using Color in Visualization

Using Color in Visualization6:14

Quantitative Color Scales5:54

Multi-hue Sequential Scales6:37

Categorical Color Scales Part 17:37

Categorical Color Scales Part 27:26

Diverging Color Scales7:59

Using Color to Highlight & Emphasize6:21

Perceptual Issues With Color12:16

Effect of Size4:11

Contrast Effects4:18

Luminance for Contrast3:36

Background and Perception5:38

Color Tools11:46

Meet the Instructors

Enrico Bertini
Associate Professor
Computer Science and Engineering
Cristian Felix
PhD. Candidate
Computer Science and Engineering

Now, I want to introduce the concept of Graphical Decoding.

What is graphical decoding?

Okay, if you remember, so far we've been discussing the idea of graphical encoding.

What is encoding?

Encoding are the rules that you as a designer

implement typically in a computer program

to transform data into a graphical representation.

Okay, that's very important but

what I want to teach you right now is the opposite, graphical decoding.

Going from observing a visual representation and trying to figure out,

kind of like reverse engineer what the mapping rules and

the graphical components of this visualization are.

This is a very useful procedure.

So let me try to give you kind of a little algorithm

to follow in order to perform this graphical encoding step.

So you have two main steps here.

The first one is to identify explicitly what the graphical components are.

And the second one is to identify explicitly what the mapping rules are.

More precisely, the idea is to look at a visualization and

ask yourself, what are the visual marks here?

Similarly to what we have already done with some of the example.

So you look at visualization and you think what are the visual marks?

Do we have points, do we have bars,

do we have lines, do we have a mix of some of these marks?

Okay, once you identify the marks, ask yourself,

what kind of information, what data items do the marks represent?

So going back from visual representation to the visual world,

graphic world to the data world, okay?

Third step, identify the visual channels.

So, is this visualization using position, is it using color, is it using size,

like the length of something or the area of something and so on.

Once you identify the channels that are used in the visualization,

ask yourself what do these channels represent?

What kind of information do they represent?

More precisely, using the language that you've learned in the data obstruction

module, what attributes do these channels represent?

So you are basically retrofitting or

reverse engineer the mapping that exist in this visualization.

The same thing can be done for tabular and network data.

There is a little, little difference when your

visualization is a visualization of a network dataset.

The little difference is that when you identify, when you go from marks to

the actual data objects that are connected to these marks,

you can have two types of objects as we have learned before.

It's not only data items but you can have notes and links.

This is important because typically, in network visualizations,

you have one type of mark that represents the nodes and

another type of mark that represents the links.

It's not always true, it's not entirely true, but in many situations this is true.

So keep in mind, the procedure is slightly different

when you have a network dataset, a visualization of a network dataset.

You can have multiple marks, one set of marks to represent the nodes, and

one set of marks to represent the links.

So let me start with one example and then I want you to go through

a couple of other examples that I am going to assign to you as exercises.

So this is the first example of me performing graphical decoding and

then I am going to ask you to do exactly the same for

a couple of additional examples.

Let's start with an easy one.

So this a visualization coming from New York Times,

as you may have noticed, we use New York Times quite a lot in the course.

They are famous for producing very engaging and

very well crafted visualizations.

In this case this visualizations here, is showing medalists and

in the last 115 years of Olympic sprinters.

And in particular, this comparing is the performance of

Usain Bolt to all the other sprinters in previous years, okay?

So what do we have here?

We have, as you can see, a number of points and

these points are positioned in a sort of scatter plot, okay?

On the x-axis we have the distance from Usain Bolt, right?

And on the y-axis we have years, time.

It's a little unusual kind of design, but it works particularly well in this case.

So, how do we decode this scatter plot?

Okay, the first step is to identify the marks.

What are the marks here?

We have points, okay?

We have lots of points.

Okay, so what kind of data item or object does every point represent?

So normally when I ask these in a classroom,

the students say the point represents a runner.

Okay, not necessarily because these are medals and this means that in

principle you can have multiple runners if they won multiple medals, okay?

So this gives you a sense of the fact that it is not always obvious

what is the association between the mark and the data item, okay?

Keep this in mind.

Okay, next step, identify the visual channels.

So here we have three main visual channels.

The first one is x-position, the second one is y-position,

and the third one is color, okay?

So now, last step, identify what

data attributes are encoded with these channels, okay.

So the x-position encodes distance from Bolt,

the y-axis represents time,

the vertical position represents time.

And color, we have three different colors, represent the type of medal.

So that's how it looks like.

So that's a particularly easy example.

Not necessarily easy in terms of identifying what the association

between visual mark and data item is we have seen.

And now I want you to perform exactly the same procedure

with some additional visualizations taken,

actually, from real world projects.

So let me remind you what the steps are.

The first step is to identify the marks.

The second step is to identify what data items the marks represent.

The third step is to identify the visual channels used in the visualization.

And the last step is to identify what data attributes

are represented by the channels that you have identified.

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