In the world of visual communication an illustrative design might be classed as one designed specifically to convey a message. The message drives the design and is not tethered to any data.
The ‘flatten the curve’ graphic that has become iconic over the last two months is an example. The difference in shape of the two distributions is marked, the narratives clear: ‘Act, or else’. The design was 'free' from the noise that comes with real data or the uncertainty that comes with statistical models. Symmetrical distributions, smooth lines and no need for scales. This graphic was designed near the beginning of the pandemic, and was influential in developing public understanding of decision-making in government. An unburdened message that spoke to us all.
Other graphics also used illustrative techniques to contribute to awareness and influence social behaviour. The Washington Post interactive on the effect of social distancing, simulated the spread of a (fake) disease called simulitis under 4 levels of social distancing; 'free for all', 'quarantine', 'moderate distancing' and 'extreme distancing' . As we watch people (circles) become infected and infect those around them, the key message becomes clear: it will be a hell of a lot worse without social distancing.
The previous two graphics are illustrative designs or 'explainers'. Sometimes however, data visualisations which aim to communicate insights from real data (especially in complex subjects such as a pandemic) cannot avoid being complex themselves. As always, it depends on the purpose and the audience. ‘Simplicity’ should not be seen a goal when visualising data. It is helpful to see visualising data in terms of clarity of design rather than simplicity.
The visual decoding of complex, multi-faceted (and often inconsistent and unreliable) data such as COVID-19 data, can require a degree of data literacy, graphical literacy and patience. There are occasions where it is appropriate to encourage and even demand this patience. The pay-offs can be worth it. For example the log scales used in many representations of trends in COVID-19 cases or deaths, especially those by the excellent graphics team at the Financial Times, will by now have become familiar to a broad, heterogeneous audience, some of whom may initially have been unsettled by it's non-linearity. This is a step forward in terms of graphical literacy.
Financial Times Coronavirus live tracking
Depending on the intended message, using a log scale makes sense when visualising exponential disease spread but the question for the designer must always be, in terms of visual communication, what is the trade-off between visual complexity and understanding? While it is true that the simplest designs can be the most powerful, complexity should not necessarily always be avoided in favour of simplicity. Many visualisations are ‘complex’ and all the better for it. Over-simplification is misleading. Overlooking detail and ignoring error or uncertainty in favour of the ‘quick and easy’ message can frequently give the wrong impression. The most satisfactory answer to the question: 'How do we look at this?' is usually 'From multiple perspectives'. Rather than relying on a single model and searching for an 'answer', use many models (from a variety of sources) and search for understanding. Visualisation can be thought of similarly as a contextual framework: gain understanding through multiple viewpoints and perspectives. Visual communication has contributed much towards understanding and navigating our new reality. These three examples are very different to each other, but they all communicate their message with clarity. Whether illustrative explainer or data-driven, design is king.
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