Balanced data visualization

Smart diagramsDiagrams are needed to transfer new information to the audience in visual form. All graphics consist of both real data and decoration.

How to balance data and decarations on diagrams?

--> Heuristic tools

According to (Tufte, 2001) all decorative graphical elements just draw attention away from the main idea, hide it or even can give opposite impression. A density of the information on a diagram can be described by Data-ink ratio. It is the ratio of a relative amount of ink or pixels those represent data, to the total amount of ink on the graph.

Formula of Data-ink ratio
Formula of Data-ink ratio

Clear graph requires less effort to understand the idea. Such visualization has high Data-ink ratio close to 1. To improve the ratio we need to reduce non-data and enhance the data pixels by following these rules:

  • Delete unnecessary axis, gridlines, and ticks
  • Avoid unnecessary borders
  • Do not use bright colors without real reason
  • Use minimal weight lines for remaining axis, gridlines, and other non-data pixels
  • Do not use 3D when it doesn’t represent the actual data
  • Delete other decoration
Examples of high and low Data-ink factors
Examples of high and low Data-ink factors

The recommendation to increase the Data-ink ratio is very useful for idea representation. However, at the same time, People don’t require only pure knowledge but also need emotions. So emotionally rich visualization also makes diagrams more friendly and their perception easier. Of course, such decoration has to be reasonable and very balanced with the main idea of a graph. Be careful with it.

To focus audience attention on the most important data, we have to highlight corresponding graphical elements. For this purpose (Few, 2006) advices to use these methods:

  • color intensity – darker or saturated colors usually attract more attention
  • size – bigger objects are usually treated as more important
  • width – thicker lines and borders look more significant
  • markers – any marker like a dot or asterisk attracts attention to the object
  • borders – everything surrounded by borders or fill color is usually considered as important
  • orientation – object oriented differently than other attracts additional attention
  • hue – a hue that is different from the general, emphasizes the object

Data visualization has
to be Evidencing

Sometimes data on a diagram is formally correct, but the way of representation distorts the information. That usually happens when the wrong scale or visual 3d perspective are used. To avoid misrepresentation of the main idea, sizes of objects on the graph have to be proportional to their values.
As a measure of idea’s distortion, lie factor has to be used. It is the ratio of visual elements’ size and data those they present.

Formula of Lie factor
Formula of Lie factor

In truthful diagram lie factor has to be equal to 1. Any disproportion leads to a growth of lie factor and results in miss-presentation of the idea.

Example of high and low Lie factors
Example of high and low Lie factors

The Visual Display of Quantitative Information by TuftePrinciples of data visualization were briefly expressed by (Tufte, 2001): “Graphic Excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest place.”

Prototyping -->

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