Course web for CS838 Spring 2010, Visualization

The Cost of Care

http://blogs.ngm.com/blog_central/2009/12/the-cost-of-care.html

This visualization compares cost of healthcare per person to average life expectancy for various developed countries. This text is included on article linking to the actual image:

“The United States spends more on medical care per person than any country, yet life expectancy is shorter than in most other developed nations and many developing ones. Lack of health insurance is a factor in life span and contributes to an estimated 45,000 deaths a year. Why the high cost? The U.S. has a fee-for-service system—paying medical providers piecemeal for appointments, surgery, and the like. That can lead to unneeded treatment that doesn’t reliably improve a patient’s health. Says Gerard Anderson, a professor at Johns Hopkins Bloomberg School of Public Health who studies health insurance worldwide, “More care does not necessarily mean better care.”  —Michelle Andrews “

This visualization encodes four dimensions of data in the following ways:

–       Cost of healthcare per person- y position

–       Average life expectancy- y position

–       Average number of doctor’s visits per person- line thickness

–       Type of coverage (universal or otherwise)- hue

What Munzner might say:

Cost and life expectancy clearly fall into the quantitative data category, and are encoded using position, the strongest visual channel for their data type. Type of coverage is categorical, and is encoded using hue, the second strongest visual channel for its data type (after position, which has already been used).  All the visual channels are seperable, and code these four dimensions without confusion. Also, cost and life expectancy are connected by lines, so their relationship is encoded using line slope. Clearly and explicitly relating this data makes the US pop out as the country with the steepest downward slope.

What Tufte might say:

First, this graphic is well documented. The creator, his position, the data source, the year the data was collected, the fact that some countries aren’t shown, and the scales for all the numeric data are all clearly written on the image. The lines connecting cost and life expectancy facilitate clear comparisons of all the data.

Edit: Here’s an interesting article where the creator justifies his design choice over a scatterplot: http://blogs.ngm.com/blog_central/2010/01/the-other-health-care-debate-lines-vs-scatterplot.html

Trends and Technology Timeline 2010+: A roadmap for the exploration of current and future trends

http://nowandnext.com/PDF/trends_and_technology_timeline_2010.pdf

This visualization denotes current trends as well as predictions for future trends, and displays them in a way that is analogous to a subway map. This text is included on the map:

“This map is a broad representation of some of the trends and technologies currently visible. Improvement works are carried out at weekends and travellers should check to see whether lines are still operable before commencing any journeys. Helpful suggestions concerning new routes and excursions are always welcome.”

This visualization encodes five dimensions of data in the following ways:

–       “time zones”– radial distance from the center of the map, hue

–       phenomena – text labels, position on category lines, connection?

–       category of phenomena – hue

–       type of phenomena – shape, glyphs

–       global risks – bulleted list, containment?

What Munzner might say:

First of all, this “roadmap” doesn’t even use the strongest visual channel(s): absolute x and y position. Then, it uses hue to distinguish different time zones (even though this is ordered data, and saturation would be more appropriate), AND to distinguish different categories. And there are 16 different colors corresponding to different categories, even though the max amount of colors used should be eight.

What Tufte might say:

What is meant by “time zones”? The common definition of this word is very different from the definition as relates to this visualization. Also, trends appear on category lines in a particular order. Is there any logic behind this order? Does it imply causality? Finally, the extremely dense text doesn’t make very judicious use of ink.

The problem of displaying a data set that is multi-variate, time-varying, and comparative in nature is inherently very difficult. Jonathan Woodring and Han-Wei Shen at Ohio State have developed a very aesthetically pleasing method of displaying such information using three dimensional color mapping. However, their solution leaves a lot to be desired in terms of the composition of a successful visualization. While the colors do make for very visually pleasing images, the visualizations themselves are very difficult to interpret. The value of any given data point is encoded in its color and its position is representative of its relation to other data collections as defined by a set vocabulary of logical operations that the tool can visualize (over, in, out, atop, and xor). In addition to the limitation on the amount of data that is provided, the visualization provides the user with neither manner of inferring what the values encoded by the color actually represent nor a physical representation of the meaning of the positioning coordinates. Essentially, the user has no manner of extracting any data out of the visualization other than two points being different over one dimension or another. A user simply looking at the output of this visualization cannot really gain much from the end result other than a pretty picture generated from a complex data set. This visualization technique seems to have potential, but in it’s current state, it is simply not a very useful mechanism for data comparison.

Above is an image of a visualization generated by this tool representing a logical combination of data points from the Supernova Initiative Data Set. The paper and additional images can be found here.