When visualizing data, we should always consider cognitive load. What’s that? It’s the mental effort required to process information. There are two types of cognitive load: extraneous and intrinsic. Let's consider each in relation to data visualizations.
Extraneous cognitive load concerns how information is presented. There's a lot we can do to reduce the extraneous cognitive load of a chart.
Intrinsic cognitive load concerns the complexity of the information being shared. You can reduce intrinsic load only by altering what is being learned or by changing the knowledge levels of learners.
We can reduce the extraneous cognitive load of any chart through careful choices about titles, annotations, colors, use of white space, etc. But we are much more limited in what we can do to reduce the intrinsic cognitive load of a chart. I’d argue that the primary thing we can do is to choose a chart type that does not increase the intrinsic load by requiring the viewer to learn something new. So familiar or intuitive chart types usually work best.
Let's consider this data dashboard in terms of cognitive load. It has several different chart types.
The bar charts impose a fairly low intrinsic and extraneous cognitive load. We already know how to read a bar chart; the compositions and colors aid interpretation; and they each show one measure which we probably can understand, such as C02 global share or CO2 per GDP across several years. The cumulative carbon clock, however, is a different story. It may grab our attention with its novelty. It's a radial column chart (aka circular column graph or star graph.) But most of us will have to figure out how to read this chart using the color and shape legends to understand what the chart is showing and how it is showing it. I also find the circular shape, which usually suggests some type of cycle, confusing because there is no cycle inherent to this data.
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