Introduction

In Colin Ware’s book, Information Visualization, he wonders: Is visualization a science or a language?

It’s perhaps a science because it must represent data accurately, methodically and without flourish so that we can see the underlying trends and patterns. Because of this, selecting the right visualization for your content could be prescriptive based on what you want to show.

However, many argue that it is a language because it uses diagrams to convey meaning. Data is encoded into symbology and semiology. The syntax and conventions of these diagrams must be learned and are not inherent.

Throughout this lesson, think about which you think it is.

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The goal of visualization is to aid our understanding of data by leveraging the human visual system's highly-tuned ability to see patterns, spot trends, and identify outliers. Well-designed visual representations can replace cognitive calculations with simple perceptual inferences and improve comprehension, memory, and decision making. By making data more accessible and appealing, visual representations may also help engage more diverse audiences in exploration and analysis. The challenge is to create effective and engaging visualizations that are appropriate to the data.

Creating a visualization requires a number of nuanced judgments. One must determine which questions to ask, identify the appropriate data, and select effective visual encodings to map data values to graphical features such as position, size, shape, and color.

The challenge is that for any given data set the number of visual encodings — and thus the space of possible visualization designs — is extremely large.

To guide this process, computer scientists, psychologists, and statisticians have studied how well different encodings facilitate the comprehension of data types such as numbers, categories, and networks.

For example, graphical perception experiments find that spatial position (as in a scatter plot or bar chart) leads to the most accurate decoding of numerical data, and is generally preferable to visual variables such as angle, 1D length, 2D area, 3D volume, and color saturation.

Thus it should be no surprise that the most common data graphics, including bar charts, line charts, and scatter plots, use position encodings.

However, our understanding of graphical perception remains incomplete, and must appropriately be balanced with interaction design and aesthetics.

Graphical Perception