8. Complex Visual Tradeoffs


When deciding which visual traits to use for which data, there are certain tradeoffs you need to consider. Learn what factors need to be considered to make these decisions in this lesson.

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  1. Lesson Goal (00:13)

    The goal of this lesson is to learn how to make decisions about how to incorporate different visual traits in a visualization.

  2. Tradeoffs for Categorical Data (00:20)

    In our visualization, we have two pieces of data - region and product category. We want to know which should be encoded using color and which should be encoded using shape. Both traits are designed to be used with categorical information, but color ranks higher on the hierarchy.

    As a result, we should use color for the data that has more elements. In our case, there are 9 product categories and 4 regions, so we use color for product categories and shape for regions.

  3. Tradeoffs for Continuous Data (01:09)

    A similar tradeoff occurs between size and gradient when we are considering data with continuous numeric values. The twist here is that the number of elements can change for data with continuous numeric values, so we can’t use the same rules as for categorical data.

    In this case, it’s better to use the easier trait, which is size, for the data that has higher importance, and use gradient for the less important data.


In the previous lesson, we began the process of converting a large data table into a more effective visualization.

In this lesson, we'll learn how to make decisions about how to incorporate different visual traits in a visualization.

In our visualization, we have two pieces of data in the labels, region and category. We'll encode one of these using shape and the other using color.

But how do we match the data to the best traits? It's actually quite simple to figure this out and it doesn't require guessing. Let's take a look.

Shape and color are both meant to be used with categorical information. Region and category are both categories of information so we could use shape or color for either of them.

To find the best solution, we need to consider the hierarchy.

Since shape is harder to process than color, the best case is assigning the data with fewer elements to shape.

In our case, there are nine categories and four regions, so it's better to assign categories to color and regions to shapes.

Another way to think about this is that we want to process the least amount of hard things.

You can see that sometimes there are a lot of choices to make when moving through the hierarchy. We can think about the trade-off between size and gradient in a similar way.

But here there's a twist because size and gradient are both used for data that has values, not data that has categories. When dealing with values, the data can change so the number of elements won't be constant.

As a result, we can't use the same trade-off matrix as we used before. Since we don't always know how many items there are, whichever measure has higher importance should get the easier trait. It might also come down to a design choice. We can use a hierarchy as a guide, but you have the final say.

Now that we've made a decision on how to complete our visualization, let's stop the lesson here.

In the next lesson, we'll finish this case study by incorporating color and shape into the visualization.