Healy’s chapter on data visualization provided a thorough overview of the different factors when it comes to designing for data. Factors that range from audience perception, aesthetics, structure, accessibility and more, Healy emphasizes the importance of balance and planning when creating visual datasets. The chapter also discusses the power of data visualization and the influence it holds amongst various audiences. There is the idea that “even tasteful, well-constructed graphics can mislead us” (Healy, 1). I agree and appreciate how he mentions that a design’s visual appeal does not correlate to its accuracy. This section piqued my interest and led me to wonder about ‘nice’ designs that represent false or skewed data. My assumption of misleading visualizations are associated with bold simple bar graphs from extreme left or right wing political news outlets, which I don’t believe to be ‘nice’ or creative solutions.
The chapter also frequently mentions the concept of human cognition and perception when it comes to comprehending data and design. It accentuates the idea that in order to successfully design, one must understand their audience. Data visualization can be quite an anthropological topic, one may take into account their viewers’ environment, culture, background, emotions, etc. In addition to knowing one’s audience, and in order to achieve ‘graphical excellence’ (I enjoyed a lot of his word choice), designers must wrestle with the challenge of providing the viewer with “the greatest number of ideas in the shortest time with the least ink in the smallest space” (Tufte, 1983, p. 51). Quite the task to accomplish! I am curious to hear what my peers believe is an efficient use of data to ink ratio and how their creative process changes when creating while using a given dataset. Healy also provides the example of Poisson and Matern’s work, and I am interested to hear what my classmates interpreted as the more structured design. The author mentions “if you ask people which of these panels has more structure in it, they will tend to say the Poisson field. We associate randomness with a relatively even distribution across a space” (Healy, fig. 1.20). My initial reaction was to view the Matern piece with more structure. Then again, it goes to show that data visualization heavily relies on the viewer’s perception.