While scaffolds guide and help a user to do a task, emphasis draws attention to salient points in our visualization's messaging by design. In art, we juxtapose a lone red apple against an arrangement of light green apples. The contrast draws the eye, regardless of where the red apple is located in the arrangement. In conversation, we may intone a particular word: we want that apple. The emphasis makes clear what the literal sum of our words do not: we're not only making a choice, but no other apple is comparable. If we shift the intonation or italics—we want that apple—we can expect that the apple may be among pears or other fruits. Emphasis also imparts additional meaning through semantics and intent.

Languages use a variety of techniques to shift the focus. The English language, for example, cares a lot about word order. Shifting sentences around can add some emphasis, but intonation and use of italics and underlining in print are common ways of drawing focus to a word or idea. ASL may alter the movement of the sign either by making it larger, or providing a different speed from the rest of the utterance, or by holding a particular aspect of the sign for longer than expected.

As we shift focus to emphasis in charts, we can call attention to particular data attributes and marks. Do we want to take note of the trend or distinct values? Are we highlighting a very precise number, or do we need to showcase the uncertainty of the metric? These questions will help direct not only the charts we choose but how we design and arrange them.

Individual Choices

We use emphasis in charts to highlight particular characteristics reflected in the data and to best match our analytical intent. We can use our chart choices themselves to create emphasis or the visual supports in and around the chart to draw focus. Our intent informs our choices. Figure 14.1 shows two chart types for the same data and general purpose. Both bars over time (a) and line chart (b) display monthly profit over several years. As you look at these two chart choices, think about what they emphasize.

At an initial glance, the bar chart makes clear what the line chart does not: that profits are quite low frequently. The values in a bar chart clearly have a direction-positive or negative. Even without additional emphasis, the few values above zero are clearly the exception. Is there a chart where it's easier to summarize the general pattern? The line chart, while heavily jagged, makes it easier to see where the average profit might fall, somewhere around –500. The selection of either option will balance the chart's purpose, both individually and within the greater visualization as we also saw in Chapter 5.

The emphasis in each of these charts can be further refined. In addition to what they already display through position, we can make both the bar and line marks thicker or thinner. We can highlight the negative values through color. We can draw greater attention to the attributes that support the data marks, such as the zero line. We can modify the line chart from Figure 14.1 and adapt it slightly to draw emphasis to both the negative values by adding a line as shown in chart (a) of Figure 14.2 and calling out each negative value as shown in chart (b) of Figure 14.2.

Chart (b) in Figure 14.2 balances the task of the overall trend by drawing emphasis to all the values below zero. To achieve this, we made the line itself thicker, matched the dots to the zero line, and worked carefully with the formatting options to balance where the attention went on the chart.

Collective Choices

As we move from individual charts to compositions, we have to think about the whole piece. Map generalization techniques (discussed in Chapters 1 and 8) are a good example for how important parts of a map are more emphasized than the less important ones. Focusing on points of interest and turns along the way provides a useful and delightful navigational experience. It's important to be cohesive in the way we go about guiding and communicating our message to the consumer. Amy Alberts (2017) found through her studies with eye-tracking data that people look on dashboards when the task is not clear and context is not set. Revisiting concepts from Chapter 9, we can scaffold people's visual attention by making visualizations cohesive with the following parameters:

• Color
• Sequence
• Visual supports and style
• Use of space (shape)
• Alignment

In Chapter 13, we used these visual attributes as parameters to create scaffolds. Now we'll revisit them to balance emphasis in our visualization's message. Let's look at a dashboard with minimal emphasis in Figure 14.3. Within the title, “Profit Ratio by Key Divisions,” the user is cued to a control to toggle the metric used within the dashboard. It's currently set to Profit Ratio to explore food item sales for a fictional coffee and tea shop. The dashboard uses a warm brown palette to set the tone, and the charts are sequenced to draw the consumer first to the high-level categories split out by regions, then to products, and last to monthly trends. A primary analytical goal is to identify where profit and loss occur.

Designing in this manner—where emphasis is austere—helps pinpoint what should be emphasized to support the task. Do we want those using the dashboard to focus on negative values, regional performance, or something else? We can draw attention by designing to salient points.

Most dashboards designed by analysts for consumer consumption strive to support strategic action; we want them to investigate the negative values or clearly understand regional impacts. Figure 14.4 directs the consumer's attention to all the negative values by highlighting them in yellow. Just like the red apple juxtaposed against light green apples, strategic emphasis draws attention to those values.

Strategy supports intent. When we start with minimal emphasis, it's easier to add focus points to direct attention to the salient points. Designing with minimal emphasis requires thinking differently about the design process: rather than starting at the chart level and working to combine them at the end, we turn our attention first to getting the base elements right and seasoning the sauce as a cohesive unit. It's iterative, exploring the impacts of both small and large tweaks, and taking a step back to understand what it does to the whole piece.

Correcting Common Problems

Many data visualization tools start with the chart, which forms the basic building blocks of a visualization. From there, charts can be combined to create cohesive compositions in the form of dashboards, analytical applications, and journalistic pieces. Tension in design exists between individual parts created by charts and the greater whole. One key reason for purchasing this book could be this very problem: you've followed all of the conventional advice and something still feels off. The medium and the mode affect how the composition comes together, a topic we'll explore in greater detail in Chapter 15.

Overemphasis is a common problem. In the practitioner world, emphasizing all the points often stems from a lack of clarity, leading to a lack of results. Decisions made at the chart level become overwhelming at the composition level. Figure 14.5 shows an iteration of the Profit Ratio dashboard where decisions at the chart level become overemphasized at the composition level. In Chapter 1, we showed the Stroop effect. Colors that align with our expectations are easier to process. Coffee is colored a dark brown, tea a lighter brown, and snacks are a mustard color that align with our mental images. While the colors make sense for the first chart, they become distracting in later charts. The design choices overemphasize the importance of the category and don't help with understanding successive charts.

Overemphasis creates distraction and loses focus. In English, we can put text in italics to highlight a key point. In excess, the effect is lost. Color, in particular, can create unintended emphasis depending on the weighting and balance between colors. To correct the overemphasis on color, while preserving the original goal of using color to segment category, we'll refactor the design using the other parameters like shape, sequence, visual supports, and alignment to effectively communicate the intent of comparing categories.

Figure 14.6 separates the line chart into three panes. It preserves the original color choices but uses the other parameters to emphasize the notable differences. At a glance, consumers can clearly see that the profit ratio of snacks is lower over time continuously. The dimensions on the bar chart are swapped. Region now displays along the row, and the category splits the data across the columns. The effect of this makes the lower values of snacks more readily apparent, and the eye can travel down from the line chart to the remaining charts due to the shared color but also the strategic alignment. The final change concerns products: turning the bar chart into a scatterplot allows adding sales while reducing the size needed.

As a whole composition, the pieces work in isolation but leverage functional aesthetics to create clarity. A legend is not needed because the color cueing is clear and supports the eye. The analytical tasks are widened—consumers can clearly compare the categories in line with the original intent but can also spot lower profit products. The scatterplot also makes it easier to judge the scale of the problem with protein packs under snacks. Not only is the profit low, but sales are also fairly high. Where the profit losses between eggs and cranberry tea are similar, the sales are not.

Another problem in the practitioner space is the use of ambiguous emphasis. We want consumers to see the profit ranges and dual encode by using both the length of the bar and color. In Figure 14.7, we can see that the implementation has the opposite effect of what we desire: the positives are clearly visible and the negatives, as smaller absolute values, fade to the background. It becomes very easy to miss the problem areas of the negative values.

We can correct this issue by removing the full-color ramp and instead using only one shade of the blue and coral. Figure 14.8 shows two iterations where the ramp has been simplified. The first is to weigh the colors equally while the second lightens the blue to de-emphasize it. Notice what draws your attention.

Further iterations of Figure 14.8 could also get closer to the example shown in Figure 14.4. The more the positive values are de-emphasized, the more the negative values will come forward. Emphasis is a powerful way to direct attention intentionally. Done well, it supports the consumer in understanding the data.

When crafting visualizations, we need to be aware of what our emphasis communicates and how the message snaps together. Visual communication uses a variety of means to relay a message with graphical alignment playing a major role in understanding.

View Snapping

Graphical alignment and snapping dates back at least as far as Ivan Sutherland's Sketchpad system (1963). Using the system, Ivan Sutherland showed that computer graphics could be used for both artistic and technical purposes using novel human-computer interactions. A user could begin with a vague notion of what they want to draw, then collaborate with Sketchpad to layer in design constraints and iteratively refine the desired graphic. Design constraints from Sketchpad inspired several object alignment methods that are widely used in drawing programs such as PowerPoint, Photoshop, and OmniGraffle.

The premise of snapping objects together also applies to dashboards with multiple charts. However, the idea goes beyond simple graphical alignment to more of a semantic alignment to help the consumer see and understand the data. View snapping is a technique that semantically aligns charts along one or more common axes in a dashboard. When a view is placed on a canvas, it's “aligned” with the other views based on shared data properties such as common attributes, visual encodings, and axes. It's balancing the emphasis of information among views that share common semantic characteristics.

Let's go over an example. Figure 14.9 shows two sets of bar charts: one for Category and the other for Products, with Profit Ratio being the common numerical attribute. The bar chart sets are placed one above the other with the intent of being useful for comparison. However, the task is challenging with different Profit Ratio ranges on each of the x-axes. You find your eyes darting back and forth between the Category bar charts and the Products bar charts, mentally trying to compare the bar lengths, even though it's like comparing apples and oranges.

Truthfulness in visualization is key. Depicting charts in a way that can be misinterpreted or is analytically difficult to understand goes against the principles of functional aesthetics and can even be detrimental. Figure 14.10 shows view snapping in action where the Category bar charts are snapped to Products, sharing a common readjusted x-axis. The alignment and shared colors make the dashboard cleaner and balanced, but most importantly, emphasize the focus on comparing. These charts can be read and viewed as one unit despite being two separate charts.

The challenges of mistrust and misinformation have prompted the research community to explore tools for suggesting and guiding authors to create responsible charts. Authors may not be aware of existing guidelines and lack expert design knowledge when composing multi-view visualizations. “Semantic snapping” (Kristiansen et al., 2021) is one approach for guiding the design of effective multi-view visualizations. Algorithms detect and suggest resolutions for conflicting, misleading, or ambiguous designs as well as provide suggestions for alternative presentations.

You've probably realized that there are common ties between concepts discussed in this chapter and the previous ones—we talk about the bento box in Chapter 1 and the mantelpiece design in Chapter 2. These concepts share an intrinsic appreciation for space, harmony, and proportion. There is a deliberate intent in the purpose and assemblage of the artifact, far greater than the sum of its parts.

We end this chapter with Figure 14.11, Figure 14.12, and Figure 14.13 to hopefully inspire you to think of ways to bring view snapping into skillful practice. Kelly Martin shows how text, charts, and legends can work harmoniously together as first-class citizens. The attention to detail is apparent where the legend of Cable TV consumers visually aligns with the peak in the area graph. It's an analytical work of art that takes balancing emphasis to a deeper level in the craft. We will sign off here by letting you think about ways where you can bring visual poetry into your analytical practice.

Summary

Emphasis is an important tool for communicating intentionality and capturing the viewer's attention. In this chapter, we discussed how we can balance the visual weights of chart elements, marks, and colors in space. Creating functionally aesthetic visualizations goes beyond the visual aspects of harmony and alignment; the semantics of the data inform the process. In the next chapter, we'll take inspiration from Marshall McLuhan and look at mode and why he says, “The medium is the message.”