Beautiful graphics: Singles in the U.S. and Viagra emails

On Carl Bialik's Blog (The Numbers Guy for the Wall Street Journal) he has interesting post on single women in New York City. You can find his post at the link above.

What I found interesting was not just the post (if you're married, as I am, finding single women isn't much of a concern) but the graphic he linked to, shown above and a link here. The graphic presents a quick way to see where most single people live, color coded by gender; men are blue, women are orange. At a glance you can see density of singles and make quick comparisons of sizes based on the circle size. Each circle not only tells the number of singles, and the gender, but also location given the placement of the circle on the underlying map of the United States.

Graphics such as these are simply wonderful. They show data in a clear, easy to use manner, that allows the reader to quickly see trends or numbers that a table would obscure.

Here's another beautiful graphic:

This graphic comes from the current issue of American Scientist and it is in Brian Hayes's column How Many Ways Can You Spell V1@gra? His column is very good; I recommend you click the link and read it.

The graphic though, is truly wonderful. He shows you various spellings of Viagra (the article is about the proliferation of spam and Viagra is a common spam email) along the left column. The color bars help organize the different spellings (red for ordinary alphabetic characters; yellow for accented characters; olive for spellings with numbers and other non-alphabetic characters; brown for spellings with spaces or hyphens; and blue for correct spellings. Time is along the bottom, left to right and the number of emails (he uses his received spam for data) is shown by the area of the disks. From this it's easy to see that:

Vertical correlations within the table suggest that many of the mailings were coordinated and may have been conducted by the same individuals or groups.

With a glance you can see patterns in the data, relate pieces of data to each other, and quickly see what the author is trying to show you.