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

Pareto Charts Tutorial

Just wanted to share these links I found which run through how to create the Pareto Charts we did in class:
http://kb.tableau.com/articles/knowledgebase/pareto-analysis
http://www.vizwiz.com/2015/02/pareto-how-to.html (video in link)
-- Heng Yi Teng Mabel

Geocoding Assignment 1

Since the Assignment 1 data has postal code in it, it could be useful to transform that to Lat,Long so tableau can read it. Having attended Geospatial Analytics last semester under Prof Kam, I've learned how to geocode it into x,y coordinates (SVY21) using the onemap API.

A previous student has contributed the code which can be found here. I've also uploaded a pdf which explains how to use it at page 2-19 of the pdf from Prof's notes.
A text editor like atom and sublime text will help greatly compared to notepad as recommended in the notes.

There is an extra step to convert it to Lat, Long though. I've found this which might help https://dominoc925-pages.appspot.com/webapp/calc_svy21/

- Aaron Mak

Tapping the Power of Visual Perception

This article talks about the ability of our sight, to be able to receive tons of information compared to the other senses we have, be it hearing, smell, taste and touch. The author provides a basic explanation of how our eyes receive information, how our brains process information and some suggestions on how to present our data.

The author mentioned that our eyes are able to process these two methods of encoding quantitative information: 2D location (eg. location of data points in a scatter graph) and line length (eg. length of a bar in a bar graph). Using these two methods will allow the audience to quickly process the data, and need not to use the metadata frequently to analyse the graph.

To conclude, this article reiterate the points Prof mentioned in class, with regards to visual perception, pre-attentive processing and how representing the data visually either makes it simple or difficult for the audience to understand. Also, the author also talks about how pie charts are not suitable because we are not able to accurately compare the different sizes of the variable represented within a pie chart.

- Jonathan Eduard Lim