Statistics are cold hearted numbers that appear to be rational and objective to the naked eye. People tend to trust arguments based on statistical metrics and perceive them as persuasion free because they appear to be devoid of value and intentions. If you dig a little deeper beneath the surface you may begin to notice all the fine tuning that is required to transform raw numbers into trends. Numbers and statistics are no different than words and writing.
The world of information design and data visualization is often regarded as objective and neutral, but why do we trust numbers and graphical interpretations without questioning them? Are we, as readers and spectators, aware that content has been framed for us to believe there is a point of view imbued in its meaning? In the exploratory stages of my thesis, while learning how to read and analyze hard numbers, I discovered data can be shaped to narrate a particular point of view.
According to Edward Tufte, a pioneer in the field of data visualization, ”Good data visualization makes complex information clear and easier to understand”. Dozens of books dedicated to the art of infographics agree that the best infographics grab people’s attention through carefully synthesizing data and crafting compelling visual narratives.
Data visualization is not neutral because information that is synthesized to make an argument is framed and structured to communicate to a specific audience, purpose, and context. Data is always gathered at a certain time with a certain purpose; and to be useful it must be mined, parsed and presented. Each step of this process involves decisions about what to omit and what to prioritize. Yet, the end result, the visualization, carries an authority, timelessness and objectivity that belies its origins. Every decision a designer makes, from choosing a typeface to selecting data to prove a point, can have a profound impact on how viewer interprets the information.
What better way to explore the intersection data and storytelling, than by telling the narrative of disparity and inequality of Baltimore. I parsed out data from the US Census, BNIA and John’s Hopkins University than to manipulate “factual” meaning to evoke an emotional reaction.