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Art or science?Which of these is the right way to think of the field of visualization?
This is not an easy question to answer, even for those who have many years of
experience in making graphical depictions of data with a view to helping people
understand them and take action. When we look at beautiful hand-drawn pictures
of data, carefully composed by talented individuals, we are drawn to the artistic
side. In some ways those charts are discouraging; their artistic elegance implies that
the creation of good visualizations is not an option for most of us. There are books
that provide rules and advice on how to draw graphs. Some give general advice,
suggesting that such and such is good, but this other is bad. Others give specific
advice such as requiring all charts to have a title or all axes to go to zero, but these
are often tied to specific visualizations and so are not general enough to qualify as
scientific principles. They are valuable for describing existing visualizations, but not
general enough to provide guidance for future visualizations. If you are designing
something new, advice on a bar chart is not especially helpful.
In this book I want to bridge the gap and not simply give rules and advice but
base these on general principles and provide a clear path between them, so that the
rules and guidance fall into place naturally, due to knowledge of those principles. In
terms of the art/science split, I want to advance the scientific component. There are
excellent books describing artistically superb plots; however, my goal is not simply
to be descriptive, but to be prescriptive – to allow people to start with a goal in mind
and design a visualization that fulfills that goal clearly, truthfully, and actionably.
Because I have an essentially scientific direction in mind, I will concentrate on
reproducibility. A chart that is wonderful for exactly one data set is of little interest.
It can be appreciated and enjoyed, but the important question must always be:What
can I learn from this graphic that I can apply to other data? With this in mind, the
examples in this book have been chosen to be realistic rather than exemplary. I have
made a definite attempt not to choose data that make a picture look good, but rather
to choose data for which a chart should be applicable. If the result is not perfect, I
prefer to present imperfection and explore remedies rather than look for a different
data source. |
