I had a similar reaction when reading this chart but I will let our reader take center stage.
The back of today's New York Times' Week in Review section devotes half of its space to a lame infographic that wastes space and has a major error (which has been corrected lazily in the version now online at http://www.nytimes.com/interactive/2008/10/14/opinion/20090531_OPCHART.html )
The chart shows the recent decline (or in some cases, rise) of retail sales at 27 common mall chains. My main objection is that the top half of the chart is useless. Yes, it provides a baseline from which shrinkage in area (visual metaphor of income = floor space) in the bottom half can represent the relative declines in sales, but this is redundantly handled better by color. The only part of the chart that I got any information from was the bottom half, and it took me a while before I figured out why the top half was even there.
Meanwhile, the error in the printed version is that the +5-10% stores were colored light green while the +0-5% store (Burger King) was colored dark green. It should have been vice-versa. The online version simply swapped the colors in the legend, rather than on the map itself, which works logically but begs the question: why do you have dark red at one end of the spectrum and light green at the other, with dark green in the middle?
Thank you for listening-- just blowing off a little steam here. It's a lot of wasted space and I'll bet the New York Times paid a lot for it.
Reference: "Op-Chart: The Fall of the Mall", New York Times. (Ed: I am not sure why the date is given as October 2008.)
New York Times has a great pointer to the Global Warming Art website. The author Robert Rohde wants to popularize environmental science by visualization of the data. There are many interesting charts and well worth repeated visits.
The pie charts, the colors, the whole works. Most troubling is that each pie has its own sorting scheme, and because the text labels were not reproduced in the smaller pies, the reader is sent scrambling around to find the right labels.
In addition, these pie charts, as with almost every other pie chart, fail the self-sufficiency test. Without all the data printed next to each sector, the reader is simply unable to judge the size of each sector.
Further, the aggregate data (larger pie) may not be as relevant after realizing that the smaller pies show very different patterns. The following junkart version tries to bring out this fact by treating both dimensions (type of greenhouse gas; source of emission) equitably.
While I picked on this particular chart, I must say I support Robert's effort and wish him luck in this very well-intentioned project.
Since the proportions add up to 100 percent, this multiple-choice question appears to allow only one answer, even though, as the text said, there were two acceptable answers! It would be useful to label those two choices separately. We'd also want to see how the question was phrased.
Seen differently, the Tetris chart is a 4x25 matrix with each cell representing one hundredth of the respondents.
Reference: "Name, Please? High School Seniors Mostly Don't Know", New York Times, April 19 2009.
An amazing amount of data is being visualized here. Mousing on the mapwill pick up the specific data for each county. There is a bar up top for discovering the evolution over time. It would be great if there is an animation button so the map can be played out without clicking. An animated gif will also do (similar to the disease map we featured some time ago).
The colors on the first map represent the origin of the top ethnic group in each county. Within each group, the tint of the color further displays the percentage of the population that group accounts for. The subgroups appear to be 0-2%, 2-5%, over 5%. The last subgroup is very wide.
Not so keen on the second map with all those bubbles. They show the number of people from each country by county. The bubble size is proportional to population. Every version of this map looks the same because the population is concentrated in the cities and the interior is sparsely populated, no matter what ethnic group.
Regardless, this is another laudable effort by the crew at theTimes.
Reference: "Immigration Explorer", New York Times, March 10 2009.
From Bernard L., another exemplary effort by the Times. This one really got me excited.
The set of line graphs shows how demographics of students in American schools have evolved in the last two decades. Here, I selected New York City schools, and the tool sensibly decided to compare those with New York State schools (gray line).
There is so much to learn from one simple chart:
The blue and gray lines are almost parallel everywhere, which tells us that in terms of the change in demographic composition, New York City pretty much resembled New York State during this entire period.
However, in terms of demographic composition, rather than the change in composition, New York City schools are very different from the rest of the state, in that the proportion of white is lower by a third while that of minorities are much higher, especially black and Hispanic students.
State-wide (as well as city-wide), black and white students have been declining as a proportion while Hispanics and Asians have increased.
The extent of the change is immediately visible, Asians have jumped from 7% to 14% for example.
From a graph design perspective, the execution is very clean. Data labels are limited to the first and last values. A small multiples concept is used with the ethnic groups placed side by side. A great awareness of foreground and background as well. And imagine how much data has been visualized here, and be impressed. You can look at any county in the country.
Here's one where the county change does not exactly mirror the state change (Napa in California):
Reference: "Diversity in the classroom", New York Times, March 12 2009.
The chart on the right which compares the unemployment picture across past recessions has many good features.
It uses a very sensible metric, counting the percentage change from peak employment. I have mentioned the superiority of this type of presentation compared to plotting a time series of unemployment rates. The following is an example of a standard graphic using gray bands to indicate recessions. The difference is obvious.
Here is the previous post that dealt with the drop in market capitalization of banks since the peak which screamed out for this type of treatment.
It handles the foreground-background issue very well. A number of similar charts is circulating in which every single line has a different color. Here, the designer clearly tells us the current recession is the foreground and all the past recessions form the background. It looks as if the 1981-3 recession is slightly highlighted with a darker orange to draw attention to the fact that it is most similar to the current situation. I find this unnecessary because the association is clear even without the darker hue; however, the designer does this with a very light touch so this is just a question of taste.
The original graph threw us off our sense of scale. It seemed to be saying all these oil companies are roughly the same size but one grew much faster than the others. The red color and the setting off of the data above the title of the chart seemed to announce some important find.
The junkart version on the right reversed everything to our normal sense of scale. It is a version of the bumps chart, one of my favorites.
So we find that Total is the smallest of these oil companies, about half the size of ExxonMobil -- you wouldn't know that from those abysmal bubbles! Adding to the problem is that the growth data is used to sort the companies while the actual production data is hidden in the data labels.
Total is indeed growing faster but BP is not far behind. The fall of ExxonMobil and Royal Dutch Shell is equally intriguing.
Chris P pointed us to the "Financial Comeback" calculator, surely a well-meaning joke from the folks at the Times.Here is how one gets to make a 40% loss back in just six years!
Surely, someone has to tell them about simulation. They have to assume a probability distribution on the annual returns, and show us some sample paths. Using the average annualized historical return in essence wipes out all variability and no wonder it's smooth sailing upwards. Eternal optimist.
Here is Chris' comment:
The bad news is that the range of values it offers does not include the return on the market from last year (-31% to -36%). I guess they are optimistic.
The interactive features of this chart, however, impressed me. The smooth adjustment to the chart as one slides the control, including the automatic choice of appropriate axis labels, is very nice indeed.
Okay, they want to trademark the name of the calculator so perhaps this is serious.
Reference: "Calculate your financial comeback", Jan 6 2009.