Position, size, color, contrast, and shape

- [Instructor] There's a great article in Scientific American called The Data-Visualization Revolution and it had a really great quote that I want to read to you. "The new graphic designer no longer creates visualizations "by choosing a rigid collection "of shapes, positions, and colors "but rather by choosing the rules needed "for data to breathe form into geometric abstractions." I love that quote 'cause I really think that that's so accurate. There are really only a few ways to show differentiation between objects on a page or a screen.

There are a million ways to execute it, but there's a very simple list of the number of ways that you can really show this differentiation. And it comes down to these five things: Position, size, color, contrast, and shape. But I love what this quote is saying, that it's not about designing something, which, you know, may be a little bit disappointing to hear, (laughs) for the designers out amongst our members. But it's really more about creating these rules, as this says, about creating the rules to allow those five things to take shape and, as it says here, to breathe form into geometric abstractions from data.

I just love that quote. So, you're trying to share information, right? You're trying to share knowledge with your audience based on data. And usually, what's interesting in that data is where it varies. So, this chart is a valid chart, right? This is what the data looks like. We have a scatter plot here and we have things along the x-axis and the y-axis. But if all charts looked like this, then this would not be a very interesting field to be in. Usually, charts look something more like this or this or this.

What's interesting, of course, are the outliers and the trends that you can see in these charts. So, as I said, there are really five ways that you can show differentiation. Position, size, color, contrast, and shape. These are, in fact, I think, really the only five ways. I think probably I could find someone who could argue with me on that, but this is really what it comes down to. So, we're going to walk through the list and we're going to start off with position. Position, in this case, as the quote said at the beginning, the rules are going to dictate the position of the objects on the screen. So, here we have an x and y on a chart.

We have a scatter plot. So, our dots are going to be arranged based on their data, right? Their position can't be changed. It is driven by the data. That's what puts the dots in their place. In an infographic, which is different from a chart, of course, you can decide where you place things, right? So, here we have five objects. I'm just drawing attention to the top two. And by putting things at the top of the page, I am drawing emphasis and I'm pointing your eye towards them. So, I'm creating the emphasis by the position manually. If I want to draw the eye to the center of the page, of course, I emphasize the center of the page and put something there.

I can also make you start on the left and force a linear progression by the way I place things and draw your eye to things on an infographic. Of course, this would be helpful in a left-to-right reading culture like English speakers, but maybe not as helpful for Arabic or Hebrew or some of the other right-to-left languages. Size, of course, can also be data driven. The third variable in a scatter plot, so you have an x and you have a y and now you have a z, which makes the size of the dots change based on some variable. So, data, of course, will change the size of your objects in a chart like this if you want it to.

Or you might use size to manually highlight things even in a chart. So, in this case, we have a scatter plot where it's clear that size is not a data-driven element. But if I wanted to, I could manually change a dot label it, put a callout on it, and draw attention to it in that way. If you're going to do this, of course, you want to be careful and make sure you add a legend at the bottom that says size means nothing. I'm manually drawing attention to this data and so that's not confusing to your users, so that they think size is a data-driven element. And of course, in an infographic, size can be used strategically in the same way, right? You can really create a focal point and draw the eye to something just by manually changing the size of the objects.

Color is a very powerful tool in visualization as well, but it can be challenging. So, like size and position, it can be used to draw the eye for emphasis, whether it's in an infographic or in a chart. So, if you're in this chart, of course, color you could say is a fourth variable, right? So, we have an x and y. We have z, the size, which is a third variable. And here, we have color, maybe representing a category. So, that's a fourth variable that we've introduced to our data here using color. But one of the main challenges with color, the use of color in visualization, is color blindness.

So, this graphic actually shows you on the left-hand side, you see a rainbow pattern. And on the right-hand side, you actually see simulations of how that exact rainbow pattern on the left is perceived by people with different types of color blindness. Up to 8% of Northern European males have red-green color blindness. This is not an insignificant percentage of your audience. It's far less prevalent among women and varies between different ethnicities. But the going assumption is that around 5% of the population is color blind to some degree.

Now, the most common type of color blindness is what's called red-green color blindness, and so these are people who have difficulty distinguishing between red and green colors. And if you think about every stock chart you've ever seen that emphasized red and green for up and down, you get a sense of how often it's ignored, the issue of color blindness. So, keep this in mind. Now, keeping it in mind is one thing, but solving for color blindness is another and luckily, there are some really great resources to do so. So, one of them is something called ColorBrewer, which lets you go in and find different color palettes which are color blind sensitive.

So, I can click on this little check box here that says Only Show Color Blind Safe Color Palettes and there are other options here as well, as you can see. I can click here and get multi-hue palettes, right, or I can get single hue palettes, just sort of the shades of colors. I can get diverging color palettes. There's a lot of different things you can do on this site and define color palettes that are color blind friendly. A really cool tool. At the same time, let's say you have a brand that you need to adhere to. There is this website called colorblindness.com and here, you can actually upload an image of a visualization that you've created and once you upload it, it'll actually convert it and show you what it would look like for people with different types of color blindness.

Again, a really great tool for testing to make sure that your image is going to be color blind friendly. Another great way to emphasize things is contrast. Here we have a choropleth map. We'll talk more about these in the video called The Right Paradigm Maps. You've seen these a million times when viewing political coverage. You have red and blue to indicate Republican and Democrat for voting patterns and we have contrast, the opacity of the color, to indicate how strongly, in this case, one county voted one way versus another county.

So, contrast is a great way to show differentiations in data. You can also use it manually. In this map, the contrast is data-driven. The contrast is directly communicating the underlying values. We could also do it manually by graying out all the counties except one and then using color and contrast to make that one pop and draw your eye. Another one of the advantages to contrast and thinking carefully about contrast, especially in interactive visualizations, is you can use it strategically when you have lots of data.

So, this is a visualization that I created looking at every season of every NHL team to get a sense of who was the best and worst hockey team based on a certain criteria, which I'll explain in another movie. But it can be hard to see what you're looking at here because we have 1200 dots on this chart and they're all overlapping. So, of course, by making them translucent, like this one where you can see in isolation, I can then see that there are patterns, right? Where I see dark white color, I can immediately understand that that means that there's a lot of dots over here. And so, it makes it a lot easier to see some of the outliers sitting around on the outside of the chart.

Makes it much easier to see trends in your data. We're on to talking about shape, which is my least favorite method of visualizing differentiation. And it's not just my least favorite. Research has shown that shape is at the bottom of the list compared to the other options for showing categorical differentiation. So, use shape cautiously. Here's a good example where the shapes are noticeably different. If I really sort of scan this graphic, I can see the squares, I can see the circles. But if I'm sort of taking a quick glance at this, I don't necessarily just immediate see the squares and immediately see the circles and immediately see the patterns of the data and the differences between the two.

But if you combine color and contrast adjustments and/or these shapes, then it can make a huge difference. Right, now of course I can see the green squares as opposed to the gray circles. In this case, I might want to upload this to that colorblindness.com website to make sure that the green pops against the gray for even different types of color blindness. But now, I've really helped, probably, various different audiences be able to see these two different types of data points more easily.

So, there really are five key techniques to show variance in data. I definitely recommend that you experiment. Find what works for your audience. Mix and match. Find the rules that you can use over and over and over again. And like I quoted at the beginning of the article, really think of them as rules. These are rules. You're establishing the rules for how to use these elements to bring your data to life visually for your audience. And what you do, what rules you create, actually could end up being your calling card, your sort of fingerprint, your visualization brand, look, and feel, as well as a great differentiator for your data.