Talk:Lesson02

Eight Principles of Data Visualization

While Bell’s article does provide a good recommendation on the principles that we should follow when creating our own visualizations, the problem lies with a few principles that might actually do more harm than good.

One of the principles is “Add small multiples”. The author states that small repeated variations of a graphic side-by-side allow for quick visual comparison, but he fails to recognize that too many graphics might actually cause confusion to the readers. He notes that scales and axis should be kept the same for comparison. However, he did not identify the target audience that such principle will be useful for. Some readers might be forced to shift attention back and forth between the exact graph and scrutinize on the thumbnail graphs in order to make comparisons.

Additionally, the author also mentioned that traditional business charts are no longer enough to analyze complex data, but he fails to distinguish that it depends on who your audience are. We need to factor in the reader’s numeracy level, literacy level or even how much time do they have to create that visualization? We cannot simply create a chart for a group of economists or statisticians and use it for a group of high school principals, and vice versa. We need to create a chart that will add value for the readers (Emery, 2014).

In conclusion, this article effectively accomplished its purpose in delivering the eight principles to create a good visualization. The article also gave examples and scenarios to support and explain how these principles can be applied into various situations to answer multi-faceted questions. However, a few additional research and sources will be essential to further support these principles.

References
Bell, R. (2012, August 17). Eight Principles of Data Visualization. Retrieved from http://www.information-management.com/news/news/Eight-Principles-of-Data-Visualization-10023032-1.html?zkPrintable=1
Emery, A.K. (2014, May 1). The Dataviz Design Process: 7 Steps for Beginners. Retrieved from http://annkemery.com/dataviz-design-process/
--Lim Kim Yong

Quantitative Literacy Across the Curriculum

In this paper “Quantitative Literacy Across the Curriculum”, the writer writes about common problems that people make when creating graphs. Using examples of graphs such as pie, bar and line charts, he highlights how certain details do not add value in clarity and in fact cause confusion to readers.

In one example, the writer criticized the unnecessary slanted design of the bars in the bar chart. The bars were not rectangle in shape but trapezium. I share the writer’s judgement that this is not ideal in a graph. The slanted end contributes to ambiguity as it is difficult to know exactly where the bar ends.

The writer also brought up inconsistencies in the graph’s range and spacing. Comparing 2 graphs, one with regular range and spacing and the other without, the overall visual impression was vastly different. The shape of the irregular graph was not even close to the correct graph. This stresses the importance of making sure that graph axes are of proper range and spacing.

In all, I agree with the writer’s argument that we should refrain having unnecessary aesthetics which do not value add in making the data clearer. As mentioned in class, simple is best. A simple design graph helps readers to focus on what is important, which is the data. Having too many fancy aesthetics (3D or slanted design) distracts readers from what is important. Instead, it lowers the overall standard of the paper, making it seem mediocre. This makes it more difficult for readers to take the writing seriously. --Arnold Lee Wai Tong