Avishi Jain
In Kieran Healy’s introductory chapter of ‘Data Visualization for Social Science’, he attempts to convey to the readers a detailed and critical analysis of data visualization, its importance in delivering information to people as well as the extent of its effectiveness in doing so. The writer repeatedly emphasizes the significance of visually depicting data over the mere examination of numerical summaries or tables and mentions that the former is often more memorable and easily understandable. Organizing numerical values into more easily perceivable graphs and charts such as scatter plots can often be more effective and less redundant than simply running one’s eyes over rows of numerical values arranged in columns of categories.
Healy adds that tools can only help so much in creating effective representations of data and much of the success of this process lies in tasteful yet clear and honest communication of real figures. He also emphasizes the importance of incorporating clarity, precision and efficiency into the process of trying to communicate data in an interesting way using design, statistics and matter of substance.Having a properly chosen format and design to represent data is crucial to creating good graphics. It is also important to use words, numbers and drawings in perfect harmony while keeping in mind the complexity of detail.
While explaining to the readers examples of what may constitute bad representation of data, Healy points out that content-free decoration is one of the greatest obstacles while trying to maximize data-to-ink ratio. While I agree with this to a great extent, I also agree with his later statement that “chartjunk is not entirely devoid of merit”. Ornamental charts are sometimes more aesthetically appealing and easier to remember but this may be true only if the data that is being represented goes hand-in-hand with the aesthetics. On the contrary, sometimes excessive decoration may make a chart more confusing and difficult to decipher. For instance, adding additional dimensions to a chart can disrupt the way the data is read and may lead to misinterpretation of values.
The writer also mentions that it is important to understand what kind of graphs are most appropriate for different kinds of data. What I found particularly intriguing about the author’s analysis of perception and data visualization was the fact that the perception of different retinal variables such as hue and saturation may differ from person to person. For instance, a range of colors differing in value arranged in numerical order may not be perceived as equidistant by everyone. It made me wonder that such differences in perception are likely to exist in several other situations. Thus, would it not be important to keep in mind the audience that the data is being represented to? Having knowledge of the audience and a general understanding of how the data will be perceived by a particular group of people could help design more effective representations that would cater to that population. Another interesting subject of discussion in the article was the honesty of data and this may be lost in the visual communication of data in the form of graphs and charts that mislead audiences. Are there laws against representing data in misleading ways and if yes, to what extent are these valid?