The next principle is the effectiveness principle which states that the relevance of information that you are displaying, should match the effectiveness of the channel. So what do we mean by effectiveness of the channel? In order to explain this, I'm going to start with an example. Okay. For each of the upcoming examples, try to estimate how much bigger is the value of the object that is marked with B, compared to the value of A? So, look at the objects that I'm showing, and try to think how much bigger is the value of B, compared to the value of A. Okay, let's start with the first one. If I show you two squares, with two different colors, of different intensity, and I ask you, how much bigger is B compared to A? It's really hard. It almost feels like it doesn't make sense. The question that I'm asking doesn't make much sense. What do I need for B to be higher than A? How much higher? It's really hard. Let's try with another example. So now we have two circles. If I ask you, how much bigger is the area of B, compared to the area of A? Try to think about it for a moment. You can kind of like estimate it, but it's not precise. It's hard to have a precise estimation of B compared to A. Let's move on. If I have two segments like these ones, and I ask you how much bigger, or larger is the length of B compared to the length of A? It's easier. It's somewhat easier. And if I align A and B, like this one, now, you are no longer using with your eyes. You are no longer trying to extract information using only the length of the bars, but you're also using their position. And in this case, it's a little easier compared to the previous one. So, why do I show you these examples? I show you these examples to give you a sense of the fact that different channels convey quantitative information, information about magnitude, more or less effectively. So when we talk about the effectiveness principle and stated that, relevant information should be prioritized, then mapped, encoded with channels that are more effective, more accurate at presenting information, that's what I mean. You should use channels that are more effective. So, the effectiveness of channels is a somewhat complex topic. And here, I'm going to provide a summary of how good are certain channels at communicating different types of information. So the ranking that I provide in this table actually comes from a lot of different sources. A lot of research that visualization and vision scientists have been doing throughout the years. So, I'm not going to describe this research in details. I'm only going to give you the actual final results. It's not even the final results. It's just the synthesis of a lot of pieces of research. So, how do you read this diagram or table that you have in front of you? So there are two important aspects to this table. The first one is the difference between the channels that are presented on the right and the channels that are presented on the left. On the left, we have channels that are appropriate as a way to encode quantitative and ordered attributes. The ones that you have on the right are appropriate to encode categorical attributes. Information about categories. Okay. That's the distinction between left and right. Now, you have the distinction between top and bottom. So, within each category, left and right, the channels are ordered in order of their effectiveness. Okay. Let me walk you through this order. On the left side, when we want to use channels to encode quantitative or ordered information, the best channel is position. And you have two types of position. We have position on a common scale. So, when the positions of the objects is relative to a scale that is common to these objects. The next one, which has a similar performance, is position on underlying scales. So, if you have, for instance, multiple charts, and both use positions on the same axes, but they are in different positions, that will be position on underlying scales. The next one is length. Like, for instance, in the situation where we are using the length of a bar to represent something. The next one is slope and angle. Like, as we have seen before in the situation when we used the slope of a line to represent amount of change, or when we use the angle of a pie chart to represent the percentage of a quantity. This one is followed by area. And as we have seen, area is used in many different situations. One example is a scatter plot with the bubbles of different sizes. So, it's not as effective as representing information with any of the other channels that you have above area. And the last one, at the bottom of this list, which actually means these are the least effective, we have color luminance and color saturation. So, in one of the previous videos, I mentioned the idea that color, is not just one single channel, is made of multiple channels. In particular, color can be described the three channels. So, one that we have already mentioned is color hue, which is the name of the color. The second one, I actually, in the previous video, I talked about color intensity. Now, I'm making this a little bit more precise. We have color luminance, which is the amount of brightness or light that is emitted by the object. And we have color saturation, which is actually the vividness of the color. Okay. So, the amount of black and white that you can find in a color, basically. I can't really provide too many details on that, but I think what is important for you to know is that color is made of three channels. And that the two channels that are appropriate to visualize quantitative information are called luminance and color saturation. And that they are available, but they are not particularly effective. This means that, if I ask you to estimate or compare quantities that are encoded with color intensity, color luminance, or color saturation, you won't be able to provide very accurate estimates. Okay. That was on the left-hand side, for quantitative and order information. For categorical attributes, categorical information on the right-hand side, we have three main channels. The first one is spatial region, which is a different way to basically say position. Position is always at the top. Then, we have color hue. I just described to you what color hue is, is the name of the color. So, this particular channel for color is appropriate and effective to represent categorical information. And the last one is shape or also texture, if it's possible to apply it to the mark that you are using for the visualization that you are designing. And these, again, are order in terms of effectiveness. So the most effective is spatial region, followed by color hue, followed by shape, or texture. One last thing to notice is that position is the best everywhere. So, as a corollary to this thing is that, when you are designing or even evaluating a visualization, you have to give very special attention to how position or the spatial arrangement of the objects is used. Why? Because it's by far the most important characteristic of a visualization.
