![]() What is striking about this chart is the complete absence of players who wear the number 42. In the below snippet from a graphic titled ‘The Uniform Distribution’, created by Dark Horse Analytics, we see the number of MLB players who wear each different numbered jersey. Representing zero: When it comes to the challenge of representing zeros we are not talking about the absence of data but rather the absence of amount. There’s something compelling about those who manage to preserve such anonymity, and the inclusion of their absence adds intrigue to the visualization. ![]() Those blanked out faces represent the reclusive and elusive, the ones for whom only a college yearbook photograph exists in the public domain. ![]() In the ‘Billionaires’ visualization, by Bloomberg Visual Data, we see a number of generic blank faces included among the illustrations of the top 200 billionaires. The absence of data does not hinder the project, it adds a whole new dimension to it. As one of the project’s authors, Kim Rees, explains, these areas without any data were not excluded, just greyed out: “It was a political statement to Russia to release the data they have about polar bears”. Here are two projects that make the “nothing” of missing data into something.įirstly, in the ‘State of the Polar Bear’, by Periscopic, we see an attempt to convey the change in the population and habitat of the Polar bear around the Arctic.Īside from the sadly apparent areas of population decline, a key observation from this project is the ‘data deficiency’ status for a large section of the displayed region, mostly part of Russia. Showing the absence of data: Though analysts and designers naturally seek to work with data that is complete, missing data can be just as revealing as the data itself. How do we make these slippery attributes of nothingness visible? Welcome to the design of nothing, a delicate and often neglected aspect of data visualization concerned with showing the absence of data, representing zero, and utilizing the property of emptiness. Yet, what if there is no size? What if there are no amounts for a category? What if no relationships exist? It enables people to move beyond just looking at data towards actually seeing the shapes and magnitudes of its physical properties to inform and enlighten.ĭiscernibility is a prominent guiding decision: making the size of values as readable as possible, the distinction between categories as identifiable as possible, and the nature of relationships between entities as evident as possible. In its most revealing form, data visualization makes the “invisible” visible. ![]()
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