Underestimating Users
Back when I was still doing research in visualization, a field concerned with making complex data sets understandable, our field was haunted by the spectre of a ostensibly misunderstood tool: the Rainbow colour map.
Assuming that most of my readers are unfamiliar with this, here is a brief and incomplete introduction to colour maps: The goal of a colour map is to assign colours to data, so that pattern can emerge. For instance, suppose you measure the temperature at some places in your country. To show these temperatures on a map, you might pick colours that range from ‘rather cold’ to ‘rather hot.’ In the parlance of our research field, this would call for a continuous colour map, since temperature is (hopefully) a continuous quantity.
Next to aesthetic choices and questions of colour-blindness, our field used to be convinced—and probably is—that colour maps should be chosen such that different gradations in consecutive colours are perceptually uniform. In other words, your perception of differences in colours should roughly match the differences of their underlying values. So far, so good! It turns out that algorithmically-generated colour maps, i.e. colour maps that can be easily generated, are often not perceptually uniform. Even worse (or so I believed): The default choice of colour map in some common software packages was our enemy, the notorious Rainbow colour map! Myriads of papers use(d) this colour map, even though our research showed that the rainbow colour map introduced patterns where none are. This led to all kinds of nice papers following the ‘X considered harmful style,’ such as work by Borland and Taylor.
