I want to conclude with a very important question. I get asked this question all the time by my students. How do you assess the quality of a visualization? How do you know that one visualization is better than another? That's a very important question. So, isn't that subjective? I show visualization to some people and some people like A, whereas some other people like B. Isn't that just about what people like? People have different tastes. Well, not necessarily. So, the thing is that some visual representations are actually much better than others at communicating some information. So, if your goal is to communicate information to somebody else effectively, some visual representations are clearly better because they are easier to interpret, and easier and faster, and more accurate to read. So, here is a small example that I want to show you. This is a visual representation that comes from a magazine, and it's titled "A World of Drugs". And here, it's very small dataset, shows how the proportion of different markets in a global spending in drugs changes over time. Going from 2006, and 2011, and 2016, and these are presented by three pie charts, but these pie charts also have different sizes that are meant to represent the growing spending over time, but also how the proportions change over time. So now, if your goal is to show how the temple of trains in terms of proportions change over time across these markets, that's not a particularly good representation. So, there are a number of issues here. Well, visually, we tend to perceive quantity with the area of the segments. But the quantity that the designer here wants to convey is actually represented with the angle of the pies. So that's one problem. So, we see something that is growing because the area is growing, but the intent is to communicate the information that is actually mapped to the angles of the pies. Another problem is that the angle and the size interfere. So it's very hard to disentangle these two pieces of information. Another problem is that if we want to see how proportions change over time, we have to mentally link these areas across the segments, which are also not aligned, which makes these comparison even harder. So, there are a number of issues with this chart. So, if your intent here, if the designers intent here is to represent the trend over time and how they change and intersect, that's not a particularly good visual representation. But if I represent exactly the same data with a chart like this one, a line chart, observing trend over time and how they relate to each other, how these markets relate to each other, is much, much easier. So, it's very important to keep in mind that when you are evaluating, assessing the quality of a visual representation, you have to think first of all, what is the intent, what problem am I try to solve, what information am I trying to convey. And then figure out whether one visual representation is bad at conveying this information than the other. So, in turn, designing effective visualizations requires two main steps. The first one is knowing the design space. That's a problem I see all the time. When you are asked to design a visual representation to communicate some data, you typically come up with a first idea, a first design. But if you don't know enough of the design space, it's very hard for you to create alternatives. But being able to create a certain number of alternatives is a crucial skills for visualization design, because if you don't do that, if you don't know how to do that, it won't be possible for you to actually see different solutions and start assessing them. So, still number one is being able to explore the design space effectively. The second skill linked to this one is being able to actually compare the solutions in an effective way. You have to be able to predict whether a given visual representation is actually going to be more effective than another. And how do you do that? There are many ways, but the most important skill is to understand how human perception works. And in particular, how human perception of graphical representation works. So, the rest of the course is going to provide you with exactly these two main tools: knowing the design space, knowing what visual representations are available for a certain type of data and problems, and learning enough about human perception that you can start reasoning about whether and why a given visual representation may be more effective than another.
