acm.org/detail.cfm?id=1780401/. All example source code is released into the public domain and has no restrictions on reuse or modification. Note, however, that these examples will work only on a modern, standards-compli-ant browser supporting scalable vector graphics (SVG). Supported browsers include recent versions of Firefox, Safari, Chrome, and Opera. Unfortunately, Internet Explorer 8 and earlier versions do not support SVG and so cannot be used to view the interactive examples.

time-series Data

Sets of values changing over time—or, time-series data—is one of the most common forms of recorded data. Time-varying phenomena are central to many domains such as finance (stock prices, exchange rates), science (temperatures, pollution levels, electric potentials), and public policy (crime rates). One often needs to compare a large number of time series simultaneously and can choose from a number of visualizations to do so.

Index Charts. With some forms of time-series data, raw values are less important than relative changes. Consider investors who are more interested in a stock’s growth rate than its specific price. Multiple stocks may have dramatically different baseline prices but may be meaningfully compared when normalized. An index chart is an interactive line chart that shows percentage changes for a collection of time-series data based on a selected index point. For example, the image in Figure 1a shows the percentage change of selected stock prices if purchased in January 2005: one can see the rocky rise enjoyed by those who invested in Amazon, Apple, or Google at that time.

Stacked Graphs. Other forms of
time-series data may be better seen in
aggregate. By stacking area charts on
top of each other, we arrive at a visual
summation of time-series values—a
stacked graph. This type of graph (some-
times called a stream graph) depicts
aggregate patterns and often supports
drill-down into a subset of individual
series. The chart in Figure 1b shows the
number of unemployed workers in the
U.S. over the past decade, subdivided by
industry. While such charts have prov-
en popular in recent years, they do have
some notable limitations. A stacked
graph does not support negative num-
bers and is meaningless for data that
should not be summed (temperatures,
for example). Moreover, stacking may
make it difficult to accurately interpret
trends that lie atop other curves. Inter-
active search and filtering is often used
to compensate for this problem.

statistical Distributions
Other visualizations have been de-
signed to reveal how a set of numbers
is distributed and thus help an analyst
better understand the statistical prop-
erties of the data. Analysts often want
to fit their data to statistical models, ei-
ther to test hypotheses or predict future
values, but an improper choice of mod-
el can lead to faulty predictions. Thus,
one important use of visualizations is
exploratory data analysis: gaining in-
sight into how data is distributed to
inform data transformation and mod-
eling decisions. Common techniques
include the histogram, which shows the
prevalence of values grouped into bins,
and the box-and-whisker plot, which can
convey statistical features such as the
mean, median, quartile boundaries, or
extreme outliers. In addition, a number
of other techniques exist for assessing
a distribution and examining interac-
tions between multiple dimensions.