Bad Visualization:
Description:
The visualization above is a bar chart representing the number of people enrolled in United States government-sponsored healthcare named “Obamacare” in March 27th and March 31st(expected). The data that the scientist wanted to present are quantitative data(number of people), and the reason why we visualize them is to help viewers digest raw numbers and understand their relationships in an obvious and intuitive way.
Analyzation:
I think the scientist chose the right type of chart to represent the data: bar chart. A bar chart can directly reflect quantitative data in different categories and the specific categories being compared. Although the scientist has chosen the right chart, the diagram itself is very misleading. To begin with, the bar on the right(7,066,000) is almost three times taller than the left bar(6,000, 000), but when you take the numbers into account, you will find that the right bar is only 1.2 times the left bar. The bars are visually far off making the entire diagram disproportionate. The scientist failed to use the “length” visual attribute to convey the story. Moreover, the y-axis was not labelled and was not started at zero. When representing numerical data with a bar graph, it is always important to label the y-axis. When the y-axis didn’t start at zero and there is no labelling, viewers will find it very confusing and misleading. In this case, I had no idea where the Y-axis really starts when I first saw it, so the chart distorts the way that viewers interpret the data and there is no way to understand the growth and relationship of the two bars.
Good Visualization:
Description:
The good visualization I found is a visualization of nutritional supplements from the website “information is beautiful.” According to the author, this type of diagram is called “balloon race”, where each bubble represents a type of nutritional supplements and one of its effects. In this visualization, the higher up the bubble, the more evidence there is for that supplement, and the size of the bubble correspond to its popularity.
Analyzation:
One thing I really like about this visualization is that, by looking at this visualization, you can immediately understand the relationship between efficacy and popularity of almost all the dietary supplements on the market. This visualization is created by analyzing more than 1,000 studies from a biomedical database. The scientist used a variety of visual attributes to presents different dietary supplements(nominal variables) and their effects(nominal variables), including position, shape, area and color. From the very top of the diagram to the bottom, the scientist labelled “strong”, “good”, “promising” to “harmful”; as a result, we can easily understand the efficacy from the position of each bubble, and the author has effectively used the “position” to convey information. In terms of shape and area, each bubble(circle) represents one nutritional supplement and one of its effects; larger bubbles means higher popularity according to Google hits. I think the circle-shaped representation gives us a sense of “pill” and “supplements”, and a larger area means higher popularity follows a good conviction. The author also made good use of hue and luminance to convey efficacy, dark green means most effective, green/pale green means inconclusive efficacy and yellow presents no efficacy. The overall color scheme gives us a feeling of health, nature and a sense of relaxation, and the luminance(light and dark) follows the convention that dark means more evidence and proofs.
Last but not least, this data visualization teaches me that information visualization can be a form of knowledge compression. You can easily interpret some of the essential information and their relationships in only a few minutes rather than reading thousands of research papers, and I think that’s the beauty of information visualization.
