Clutter is your enemy - Data Visualization

In the last two articles The Importance of Context in Data Visualization and Selecting an effective visual: Data Visualization, we have discussed the context and type of visuals we have to choose for effective data visualization.

In this article, we will be discussing the Clutter or anything which is not useful in data visualization and how can we identify and overcome that.

Cognitive Load

Whenever we see anything new even if it is a blank screen, it takes brainpower to process the information, which is termed as cognitive load. Humans' brains have a finite amount of this mental processing power. Therefore, anything which is irrelevant or not adding any value in visuals it is better to get rid of that. Identifying and eliminating such irrelevant things or clutters is the focus of this article.

Clutter

These are visual elements that take up extra space but don't increase understanding and contribute to excessive or extraneous cognitive load. There is a simple reason we should aim to reduce clutter: because it makes our visuals appear more complicated than necessary.

Gestalt Principles of Visual Perception

When it comes to identifying which elements in our visuals are signal(the information we want to communicate) and which might be noise(clutter), we need to consider the Gestalt Principles of Visual Perception. These principles are widely accepted that define how people interact with and create order out of visual stimuli.

We will discuss six principles here: proximity, similarity, enclosure, closure, continuity, and connection.

Proximity

We tend to think of objects that are physically close together as belonging to part of the group. The proximity principle is demonstrated in the below figure.

We can leverage this way that people see in table design. In the below figure, simply by virtue of differentiating the spacing between the dots, your eyes are drawn either down the columns in the first case or across the rows in the second case.

Similarity

Objects that are of similar color, shape, size, or orientation are perceived as related or belonging to part of a group. In the below figure, you naturally associate the blue circles together on the left or the grey squares together on the right.

This can be utilized in tables to help draw our audience's eyes in the direction we want to focus on. In the below figure, the similarity of color is a cue for our eyes to read across the rows.

Enclosure

We think of objects that are physically enclosed together as belonging to part of a group. It doesn’t take a very strong enclosure to do this: light background shading is often enough, as demonstrated in the below figure.

One way we can leverage the enclosure principle is to draw a visual distinction within our data, as done in the graph below.

Closure

The closure concept says that people like things to be simple and to fit in the constructs that are already in our heads. Because of this, people tend to perceive a set of individual elements as a single, recognizable shape when they can-when parts of a whole are missing, our eyes fill in the gap. For example, the elements in the below figure, will tend to be perceived as a circle first and only after that as individual elements.

It is very common for applications like Excel to have default settings like chart borders and background shading. This principle tells us that these are unnecessary and we can remove that such that relevant information should not get left behind. For example, in the below chart, it is looking better once we have removed the border and shading of the chart.

Continuity

The Continuity principle is quite similar to the closure principle. When looking at the objects, our eyes seek the smoothest path and naturally create continuity in what we see even where it may not explicitly exist. By way of example in the figure below, if we take the objects (1) and pull them apart, most people will expect to see what is shown next (2), whereas it could as easily be what is shown after that (3).

In the application of this principle, we’ve removed the vertical y‐axis line from the graph in the figure below altogether. Your eyes actually still see that the bars are lined up at the same point because of the consistent white space (the smoothest path) between the labels on the left and the data on the right. As we saw with the closure principle in the application, stripping away unnecessary elements allows our data to stand out more.

Connection

This is the final principle we will focus on. We tend to think of objects that are physically connected as part of the group. The connective property typically has a stronger associative value than similar color, size or shape. Note when looking at the figure, your eyes probably pair the shapes connected by lines (rather than similar color, size, or shape): that’s the connection principle in action.

One way that we frequently leverage the connection principle is in line graphs, to help our eyes see the order in the data, as shown in the figure below.

I hope the above points will help you remove the clutters and make your visuals more efficient and aesthetic.

I am happy to receive your feedback and suggestion.

Source - Storytelling with Data by Cole Nussbaumer Knaflic