Data Visualization: A Practical Introduction by Kierann Healy
I think the researches and graphic examples that were provided in the writing aims to identify factors that determine how people interact with visualized data—by investigating the working mechanism of human perception and its tendency, Kierann Healy discusses how graphic representations could be easily misleading and introduces a wide range of variables that are in play as data is mapped and interpreted on a visual platform.
Healy states the criteria in analyzing a bad figure are matters of poor aesthetic, substantive, and perceptual approaches that were conducted to map the data. A set of inconsistent design choices or over embellished graphic representations could be problematic aesthetic decisions that unnecessarily draw the viewer's attention and make them lose focus. Failing to present a good, unbiased set of data hinders the viewers to draw a substantive understanding from a figure. And visualizing without considering how people will perceive the outcome deeply undermines the importance of communicating the data.
However, I think it's important to note that Healy mentions that even if a figure is considered to have these bad practices, it may be successful in making people interested in looking at it or remember it over its simplified, plain alternative. Diverging from assessing the functionality and purpose of graphic representation as measures that judge the effectiveness of a graph to the value judgement of each individual, a question come to mind: What is really necessary to consider when mapping data, or in other words, what drives the process of data visualization? the data itself or the purpose of presenting that data and its audience?
In a way that Healy puts it, data visualization is encoding information that people needs to decode, understanding and then interpreting it. While I think that it is a safe choice to work within the frameworks of perceptive rules for graphics, I think it's also necessary to keep in mind to not treat them as absolute, divine methodologies. In the writing, Healy mentions the effects of optical illusions and how our "perception is not a simple matter of direct visual inputs producing straightforward mental representations of their content." And I think that this understanding of everything we experience visually are not absolute or objective, but respectfully more subjective and relative applies to the approach we should take when determining something to be a good or bad example of data visualization. How can we decide whether we are designing good or bad visual representations of data unless we know why and to whom that data matters? I'm curious to hear what you guys think that makes a bad graph.