But for many, such elaborate encodings pose a barrier to understanding. The experimental encodings of data art play a very important role in extending the boundaries of the genre. In data visualization, if the intention is to get information clearly across, easily readable encodings should likewise be used. Keeping encodings simple: color hue, shape, and position along axis (DiceWar - Light of Dragons by SunCoreGames, designed by Adrian Bolla and Bujar Haskaj, illustrated by Malte J. Using them would quickly result in misreadings and confusion. Board games seldom include more difficult to discern encodings like shades of a color hue (light to dark) or orientation. Numerical data is usually encoded via location among common axis, number of elements, and size of elements. This goes, for example, for the different kinds of meeples controlled by each player. Categorical data is usually encoded via color hue and shape. Board games use easily readable data encodings, use overarching plots and metaphors, have graphic design that fits the topic, and represent the data in physical form.īoard games tend to use easily readable encodings of data. In the following, I will discuss a few such points. The good news is that many of the elements that make board games so engaging, fun, and accessible, are equally applicable to data visualizations. That is a degree of user engagement that would be great to also achieve for data visualizations. Players spend hours and hours poring over these visual representations of data. Obviously some kind of data visualization (Stress Botics by Token Synapse, designed by Fernando Barbanoj)īoard game players are willing to pay 30–50 € for standard games, and well over 100 € for elaborate expert games.
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