I received the September 2010 email newsletter from Dashboard Insight today and the sponsor for the month is Corda. Corda provides data visualization software, one of their specialties being dashboard development with the tool CenterView.

I was looking at their demo dashboards and ran across this one (click on the image to go to the source).



Does it look familiar? It's indentical to the sample sales dashboard Stephen Few presents in his book Information Dashboard Design (see Figure 8.1 on page 177).

When I read Stephen's book I wondered which tool he produced the dashboard from. Now I know, at least I hope it's from Corda, otherwise it's a complete ripoff. I suspect it's the former. Nice work!

I finished re-reading Show Me the Numbers by Stephen Few last week and in Chapter 10 there is a discussion about Scale Lines, a subsection of which is devoted to logarithmic scales. Stephen made one particular recommendation that caught my eye (p. 199):

"Whenever you want to compare differences in values as a ratio or percentage, logarithmic scales will do the job nicely. They are especially useful in time-series relationships if you wish to compare ratios of change across time."

On p. 200, Few goes on to say:

"When you use lines to encode time-series along a logarithmic scale, angles that are the same tell you that the rate of change is the same."

Personally, I try, whenever possible, to avoid using a logarithmic scale. They tend to be very difficult to interpret and, for me, it's much easier to understand a graph that shows % change on a zero-based axis. Let's look at an example of both starting with a bar chart.



On this bar chart, it's challenging, but you could make the assumption that both bars are changing at the same rate. This same chart is much easier to read as a line chart like the following:



On this line chart, it is much easier to see how both line have roughly the same angle of ascent leading you to believe that they have approximately equal rates of change.

However, if you look at the same line chart, but with a zero-based axis, you get a much different message:



You can now much more clearly see that the percentage change year to year for the two measures is very different. (NOTE: I have hidden 2001 from this chart because it is the reference point from which the measures begin.) In fact, the logarithmic scale would lead you to believe that there is a positive percent change, however, it is clear from this zero-based line chart that it's quite to opposite.

In the end, I urge caution when using logarithmic scale and my suggestion would be to use a zero-based scale to represent percentage change whenever you can.

Today I was catching up on a webinar that Tableau hosted called "Bullet graphs for monitoring and analysis" by Stephen Few. The webinar was quite good, as you would expect from Stephen, but as he was walking through a demo of how Tableau had to implement reference bands in order to include bullet graphs as an option something struck me as odd. Stephen had violated the principle of the zero-based scale.



I just so happened to be re-reading the zero-based scale section in Stephen's book "Show Me the Numbers" yesterday and in the book Stephen says (p. 169):

I was able to reproduce Stephen's graph using the Coffee Chain data source provided by Tableau, including using the exact range for the banding that Stephen used. This time, however, I started with a zero-base scale (which is the default in Tableau).



The profits look much more constant in this example. In Stephen's example, the changes month to month are much more exaggerated.

In the end, I'm sure it was done to merely illustrate the feature, but beware of how you use this feature yourself.

I was watching a presentation/webinar today and the author was reviewing growth rates across time periods. Very straight forward data, quite easy to understand, yet the message was deceiving.

My mind immediately went back to Stephen Few's critique of the way BP was praising its efforts collecting oil from the disaster they themselves caused. In this case, BP was using a cumulative bar chart, which intentionally gave viewers the false impression that containment efforts were improving. Stephen quickly pointed out this fault and presented the data as individual measurements so that you could see the real story.

Back to the session I was attending. A chart was displayed that showed growth rates across time, but as individual points. The growth rate was measured from the previous point, not from the beginning of time. This leads you to believe that a negative growth that is less negative than the last point is actually improving results, yet in fact, the situation is just getting worse.

Think of this as the inverse of the problem Stephen addressed with BP.

I took some data for automotive sales in the United States to demonstrate what I mean. The blue line respresents the growth rate from one point to the next.

A good example to consider is October to December 2008. November experienced negative growth compared to October, but it's less negative than October, so the line goes up, giving you the impression the situation is improving. December is negative compared to November, but the line continues to go up because December is less negative than November.



I feel that a more proper way to tell the story is to use a cumulative line chart. The cumulative view is represented by the orange line. Consider the same time period. From a cumulative perspective, since November is negative compared to October, the line continues to decline. November's negative value has been added to October's negative value. The same situation continues in December.

Now, look at the different stories these lines tell. The blue line indicates you are only experiencing a 1% decline, yet the orange line says you've declined close to 50%.

Believe me, I know both lines are "correct." The point I'm trying to make is that you need to be sure to indicate the point you are measuring against. Very often sales figures are stated as "versus last year," but versus last year could mean many things.

In any event, September 2009 was a terrible month for the industry.

I've started following @datavis (a.k.a. +DATAVISUALIZATION.CH) and one of the first visualizations I saw was something the author said was one "of the sweetest Infographics that can be found over there." (There being We Love Datavis.)

The very first "Inspirational Infographic" I saw on the blog post was the "Die Hard Index."



Apparently this was a student project, so I'll cut the creator of the viz a bit of slack, but @datavis should know better. While this is a "pretty" graphic, it is very difficult to gleam any insight. I just so happened to read Stephen Few's article "Our Irresistible Fascination with All Things Circular" in which he states "the primary goal of any data visualization is to inform, and that it should be carefully and knowledgably designed to support this goal based on the principles that we’ve learned through years of research about graphical perception."

The problem with this visualization is that it takes too much work to find anything useful.

Joyce Robbins, Ph.D. and Naomi Robbins, Ph.D. contributed an article to Stephen Few's monthly newsletter detailing problems with graphs and charts in college textbooks. It's well worth reading.

Quantitative Literacy Across the Curriculum: Improving Graphs in College Textbooks

To subscribe to Few's monthly Visual Intelligence Newsletter, click here.

The New York Times ran an article written by A.O. Scott back in November. The purpose is not to critique the article, but rather the, gasp, bubble chart used to rank media consumption hours.



I'm a big fan of Stephen Few and have learned a lot from Stephen and his books about effective visual design. Stephen point out that "Visual perception in humans has not evolved to support the comparison of 2-D areas, except as rough approximations that are far from accurate."

As soon as I saw this ranked bubble chart, I immediately began exploring other, more effective display mediums. Here are some examples.

I wanted to start by trying to find a way to use the bubble charts. The only method I could employ was to add color to the bubble charts, but I don't gain much at all.



Of course, the simplest way to rank data is through a simple bar chart. The first example is as intuitive as it gets; it's very easy to compare the relative size of the bars. The only purpose of this graph is to emphasize the rank.



I took this a step further. When reviewing Scott's bubble chart, I had the impression that he was emphasizing the percentage of time that we spend in each of the different medium. That led me to a bar chart that shows the contribution to the total. It's the same graphic as the ranking chart above, but this time I intentionally labelled the bars to emphasize the contribution of each activity.



I'll conclude with one of the least effective displays, the dreaded pie chart, but I think one of the pie charts is actually a bit effective. The first pie chart displays every category, which makes it impossible to compare the sizes and has way too much information.



I decided to group all but the top two categories into an "other" category to simplify the pie chart and I also ensured that they were ranked by contribution as you made your way around the pie.



Which display do you like best? Which display is most effective? My vote is for the bar chart displaying the contribution to the total.