5.0x
AAPL

4.0x Gain / Loss Factor

0.0x
AMZN
GOOG
S&P 500
MSFT
IBM
- 1.0x
Jan 2005

time-series Data: figure 1b. stacked graph of unemployed u.s. workers by industry, 2000–2010.

Agriculture Business services Construction Education and Health Finance Government Information Leisure and hospitality Manufacturing Mining and Extraction Other Self-employed Transportation and Utilities Wholesale and Retail Trade

2000 2001 2002 2003 2004 2005

2006 2007 2008 2009 2010

Source: u.S. bureau of Labor Statistics; http://hci.stanford.edu/jheer/files/zoo/ex/time/stack.html

time-series Data: figure 1c. small multiples of unemployed u.s. workers, normalized by industry, 2000–2010.

Self-employed
Agriculture
Other
Leisure and hospitality
Education and Health
Business services
Finance
Information
Transportation and Utilities
Wholesale and Retail Trade
Manufacturing
Construction
Mining and Extraction
Government

time-series Data: figure 1d. horizon graphs of u.s. unemployment rate, 2000–2010.

Source: u. S. bureau of Labor Statistics; http://hci.stanford.edu/jheer/files/zoo/ex/time/horizon.html

chologists, and statisticians have studied how well different encodings facilitate the comprehension of data types such as numbers, categories, and networks. For example, graphical perception experiments find that spatial position (as in a scatter plot or bar chart) leads to the most accurate decoding of numerical data and is generally preferable to visual variables such as angle, one-dimensional length, two-dimensional area, three-dimensional volume, and color saturation. Thus, it should be no surprise that the most common data graphics, including bar charts, line charts, and scatter plots, use position encodings. Our understanding of graphical perception remains incomplete, however, and must appropriately be balanced with interaction design and aesthetics.

This article provides a brief tour through the “visualization zoo,” showcasing techniques for visualizing and interacting with diverse data sets. In many situations, simple data graphics will not only suffice, they may also be preferable. Here we focus on a few of the more sophisticated and unusual techniques that deal with complex data sets. After all, you don’t go to the zoo to see chihuahuas and raccoons; you go to admire the majestic polar bear, the graceful zebra, and the terrifying Sumatran tiger. Analogously, we cover some of the more exotic (but practically useful) forms of visual data representation, starting with one of the most common, time-series data; continuing on to statistical data and maps; and then completing the tour with hierarchies and networks. Along the way, bear in mind that all visualizations share a common “DNA”—a set of mappings between data properties and visual attributes such as position, size, shape, and color—and that customized species of visualization might always be constructed by varying these encodings.

Each visualization shown here is accompanied by an online interactive example that can be viewed at the URL displayed beneath it. The live examples were created using Protovis, an open source language for Web-based data visualization. To learn more about how a visualization was made (or to copy and paste it for your own use), see the online version of this article available on the ACM Queue site at http://queue.