What if you could make data immersive in a way that people could feel or empathize with the information presented?
Hyoyong Kim and Jin Wan Park, explain the answer to this question. By using aesthetics with data visualization, rather than the conventional method of displaying information, data can become immersive. In their piece, Topics on Aesthetic Data Visualization, they look at the artwork that utilizes data and classify it into three topics. Each topic explains how artists use viewpoints, interpretations, and senses to enhance data and the impact it has on the viewer.
To begin, Hyoyong Kim and Jin Wan Park first make it clear that there is a distinction between Conventional Data Visualization, as we know it today, and Aesthetic Data Visualization. Conventional Data Visualization has a single goal, to deliver clear and concise information to the viewer. The data visualization is only practical when the viewer can recognize patterns from the in the once overcrowded Data. Aesthetic Data visualization, on the other hand, cares less about the practicality of the data. The goal of adding aesthetics to data is to make room for artistic expression. By removing the practical parts, the artist can add a voice to the data that would be missing otherwise.
By using aesthetics the information delivered takes a back seat to its appearance. By combining art, philosophy, and iconography with conventional data, the field of Aesthetic Data Visualization emerges. Hyoyong Kim and Jin Wan Park explain that there are ways to describe this practice of information visualization. Some words they use are: Ambient - abstract depictions of data, Social - the data that uses social experiences, and Artistic - displaying data as a visual representation or interactive method, describing how the viewers explore parts of data that would not fit into the goal of data visualization. Kin and Wan Park also suggest that the use of these methods could allow viewers to see problems that conventional data may miss.
Aesthetic Data Visualization appeals to the viewer in ways that conventional data visualization can not. Hyoyong Kim and Jin Wan Park explore and classify their argument into three categories. These categories are titled: Viewpoint, Interpretation, and Enhancing Sense. Each topic, when added to conventional Data Visualization creates works that enhance the viewers’ experience and understanding of numbers and quantities.
The viewpoint of the creator determines the perspective of the data. Aesthetic Data Visualizers can choose a viewpoint for their data but, when conventional visualizers do this, there is often a bias. With conventional data, the motive of the project is often determined by the usefulness to the viewer. However, for aesthetic data visualizers, the use of data is determined by only the goals and motives of the artist. The data selected acts as inspiration rather than the focus of the work. An example of this is in graphs of neighborhood crime. Often with conventional data, the viewer is limited to the neighborhood and the type of crime. The goal of these charts is to show a buyer the crime in an area the data visualized is limited to information that helps the viewer make an informed decision when buying. Aesthetic Data Visualizers have the flexibility to choose their goal. Yet, in the piece Out of Statistics: Beyond Legal, by Rebecca Ruige Xu and Sean Hongsheng Zhai, their work uses the same data as conventional visualizers, neighborhood crime statistics, but the perspective is different. Instead of the focus being on the residential areas it looks at the data across several states. Their visualization dramatizes the data and leaves an impact on the viewer about crime in each state, in a way that conventional data is not able to do.
Interpretation explains how a data source can become Art. Conventional data can capture information at a single moment in time, but its ability to capture information is limited by technology. Additionally, data cannot capture emotions or abstractions with visualizations. A way around this is to use art with data. By making this combination, aesthetics can take on deeper meanings and can draw on individuals’ experiences and create expressions that simple data visuals can not capture. This can be seen in the project, “Mapping time”, by Lev Manovich. This piece collects decades of Time Magazine covers and places them in chronological order. By using images rather than qualities or charts, the work documents public perceptions on race, and wars by the lack, or inclusion, of diversity on the covers. Aesthetics can also show multiple opinions in data and give a new appreciation of the art, and the information presented. The journal uses the example, A digitally generated paternal family tree of Mr. Park, by Jin Wan Park. The data was pulled from a traditional Korean family “Jokbo” (family book of ancestors). When Jin Wan Park visualized this information artistically, he found gaps that documented the lost lives of young family members and clusters of spaces that represent times of war when several family members pass away. This piece gives emotion to otherwise static data.
Seeing data as facts and figures is not the only way to understand it. Hyoyong Kim and Jin Wan Park propose that aesthetic visualization can enhance how the viewer understands the data through their senses. Typically, visualizations are limiting in their ability to pull in our intrigue and deliver information. Adding aesthetics to the visualization, allows the artist to draw out sensations and emotions by placing information in a way that makes it impactful. An example of this is in the piece, The News Knitter, by Ebru Kurbak. The work collects information from public news and places it onto knit shirts. Ebru Kurbak amplifies the data by making it interactive rather than a simple chart. This delivery method of choice is an object from popular culture, allowing the data to send a message that the public can understand and relate too. The sight of a T-shirt touches the sight and kinesthetic sense in a way that a chart or graph can not.
To conclude, Hyoyong Kim and Jin Wan Park suggest that Aesthetic visualization is more impactful than typical visualization methods. They claim that by using the viewpoint, interpretation, and sense of the artist, the visualization takes on dimensions that go farther than graphs and charts can explain. By adding aesthetics to data visualization, the viewers of data can understand the works in a way that goes beyond numbers. As the field of Data Science expands, data will have a much larger role to play in the shaping of society and the decisions made in it. Hopefully, the importance of aesthetics will be taken into considerations, as visualization is not just for one group but rather the public eye. It would be best for data to appeal to the publics’ senses, perspectives, and understandings of the world around them, as it is not just one person whom the data impacts but rather several voices.
Kim, Hyoyoung, and Jin Wan Park. “Topics on Aesthetic Data Visualization.” SIGGRAPH Asia 2013 Art Gallery on - SA ’13, Association for Computing Machinery: Digital Library, Accessed 12 Oct. 2020, < dl.acm.org/doi/epdf/10.1145/2542256.2542259>.
Tagged #writing_Data Vizualization.