R1 Sejin

Kierann Healy’s chapter discusses of some factors in data visualization that may mislead the viewers. He introduces a claim that the strong aesthetic judgement isn't the most effective visualization method, but maximizing "data to ink" ratio is another element to consider important. From this viewpoint, simplifying is almost all of what we need to make sure in order to let the charts remain junk-free, and thus effective. At this point however, an opposite opinion arises; some viewers find it easier and more enjoyable to look at the embellished charts. All graphs are meant to be reproducible and effective, but personally I am more convinced to the latter. People of the Internet age are exposed to the overabundant data. In terms of the role of data visualization: to curate meaningful information, readability would be the priority. However, even these well-aligned and executed data may be none of the viewers' interest because we look at lots of them.

Visualized data are sometimes info-biased on purpose. Dramatic value is achieved by manipulating x-axis or omitting zero baseline. It might seem tricky or even worse misleading, but it is remarkable that how data be perceived is variable depending on the adjustment of the graph elements. Here the reading suggests graphic designers, or someone else who make charts, to be honest when dealing with charts and graphs. As mentioned on the reading, there are numerous systems—including tips such as popping up, gestalt rules, or colors—to represent data. These makes graphs more thoughtful for viewers when decoding them. But since "often the main audience for your visualizations is yourself", we should remember that data is so easily be distorted in a way the author want to argue.