Chapter 4 Evaluation

Aside from the 4 attributes, we also evaluate visualization based on relevancy, interpretablity, simplicity.

Often a visualization is a summary of a story, put into an image that best present the idea behind the story. It may show the causes of an event, the changes over time or the effects of an event. In some instances, we use it to highlight a specific point that prompts the reader to take action.

4.1 Relevancy

Visualization complements or emphasizes a point in its accompanying story. It should include the right information that relates to the story. Data integrity and accuracy is also important. To have accuracy, data used for visualization should be comprehensive, without omission. For example, if the story is to describe change over time, then the duration of time used in the visualization should be suffifient to show the full picture of the change being highlighted in the story.

4.2 Interpretability

A roomful of conflicting interpretations is usually a sign that something is missing. To ensure interpretability, we need select the right type of visualization to convey the information. In some situations, a pie chart may present the results in a more clear and consise manner as to a bar chart. Hence, it is important to understand the purpose of the dataset so that we are able to choose the right visualization medium to present it. While there can be many different interpretation of a visualization by different individuals, ultimately they should all convey the same underlying idea or support the same overarching concept.

4.3 Simplicity

Visualization should be clear and easily understood. Complex visualization can be confusing to the reader and may mislead them to a different conclusion from intended.

Clarity on visualization can be achieved by including legends so that readers have a guide to what the parts of the visualization mean. Legends can be used to define all the symbols, figures, axes, colors, data ranges and other graphical components in a visualization.