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Computing Now Archive | January 2014: A Snapshot of Current Trends in Visualization - IEEECS

Computing Now Archive | January 2014: A Snapshot of Current Trends in Visualization - IEEECS | DataVisualization |
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Visual Literacy: An E-Learning Tutorial on Visualization for Communication, Engineering and Business

eLab project website template
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Reading Visualizations for Beginners

Reading Visualizations for Beginners | DataVisualization |
A skilled author of data presentation will choose the right visualization
to emphasize a message. The data, chart, and supporting descriptions will
work in harmony to point out what is interesting. The reader simply goes
along for the ride. Unfortunately this is the exception more than the rule.
Many data products are a muddled mess of chart choices, obscure labeling,
and arbitrary layout. In essence, the author has passed responsibility to
their audience to find the meaning.

If you are to carry this burden of rooting out the insight in a data
visualization, you need to know where to look. The best place to start is
by focusing on the unexpected. Does the world work the way you think it
does? Or does the data show you something that challenges assumptions of
expected values? Let's take a look at a few ways to find the unexpected.

Unexpected distributions

Pie charts are designed to show how something breaks into its constituent
pieces. The slices add up to the whole, and the volume of each slice
indicates its piece of the pie. The primary insight offered in a pie chart
comes from slices that are smaller or larger than you would expect. One
weakness of the pie chart is that to discover slices that are bigger or
smaller than expected, the reader needs to compare the actual chart to what
they imagined it might look like. For example, in this pie chart the reader
might be surprised to find that confections are nearly half their diet by
volume (that’s not healthy eating).

Unexpected patterns or relationships

Plotting data in a scatterplot or bubble chart is a way to show
relationships between two or more variables. The pattern of the points may
express a correlation that is either expected or surprising. Furthermore,
outliers from this pattern are interesting because they break the mold.


Source: Data from Natural History Magazine, March 1974

This scatterplot shows animal size versus weight. The data indicates a
positive relationship between size of the animal and its top speed. Bigger
is faster, but with a lot of variation. The cheetah is an outlier with an
unusually high ratio of speed to body mass. That's interesting. (Also, it’s
good to see that humans are faster than bears. Unfortunately, a careful
reading of the underlying data reveals that the human data point is Usain
Bolt, world record holder in the 100 meter sprint.)

Unexpected trends

Trends across time are another common place to look for insights. Line
charts can make obvious the deviations compared to expected patterns or
trends. Like the pie chart, the reader needs to overlay their assumptions
on the shape of the lines. Do you expect there to be an upward trend?
Should the values remain steady over time, or is it normal to see
substantial fluctuations?


Cardiologists use Electrocardiograms (aka EKGs) to trace the electrical
activity from the heart. A healthy heart demonstrates familiar patterns in
the lines; changes to these patterns indicate problems. An experienced
cardiologist can see abnormal heart rhythms, chamber enlargement, and signs
of impaired blood flow through changes in the shape of the lines.


Data without context may offer little meaning. But adding a comparison
value—whether an industry benchmark, an organizational goal, or a
regulatory standard—brings values into focus. Comparisons across time
periods can communicate improvement or regression. Direct comparisons can
show how two or more entities rank compared to each other. Numerous
specialized data visualizations have been designed to enable quick
comparison, including bullet charts, “stop-lighting,” and leaderboards.

This dashboard compares bank brands by a series of survey questions.
Rankings and side-by-side comparison make it obvious who is performing
better for each brand performance measure.

Find a starting point

A dashboard, report, or data visualization can feel like an ocean of
information competing for your attention – like a Where’s Waldo™ puzzle.
Rather than trying to take in the whole picture at once, it’s a good idea
to focus your attention on a small piece of the picture. Focusing on a
single element can help you grasp the nature of the data, the dimensions
and metrics being displayed, and eventually how a small piece fits into the
whole. Take the following data visualization comparing hospitals by patient
experience as an example.

There is a lot going on here. The bubble chart shows three separate metrics
about each hospital. The meaning, size, and positioning of the bubbles
requires a new reader to carefully review the axes and legend to get his
bearings. The connection between the bubbles and the bar chart on the right
is not immediately obvious.

It’s a lot easier to tell the story of a single bubble.

The highlighted bubble represents a hospital, and the three metrics used to
size and position the bubble are shown in the tooltip. In fact, the
connection to the bar chart becomes obvious as Russell Hospital is
identified as one of the largest hospitals by bed size. This particular
data point may not be the most interesting or unexpected in this chart, yet
now you how a much better sense of what the data product is trying to
convey about hospitals.

Turning Data into Words

It is often said that you know you have become fluent in a foreign language
when you dream in the language. Short of this inflection point, language
students have to translate the words back into their native tongue. And so
it is with data. Without instant recognition of the meaning of a data
visual, it can be useful to convert the information into a language in
which you are familiar.

Take the example above: to understanding the message in the data, you might
translate a data point into a descriptive sentence such as “Russell
Hospital has 730 beds, tied with three other hospitals” or “Russell
Hospital’s patience experience score is near the top of all hospitals
shown.” This is a way of capturing and testing your understanding of what
you are seeing in the data.

By breaking a complex data product into its smallest pieces and finding
something comprehensible, you will start to understand both what the author
is trying to show and how to read the content.
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Data Visualization: 3 Questions for Passing the Eye Candy Test

Data Visualization: 3 Questions for Passing the Eye Candy Test | DataVisualization |
The key to data visualization isn t just providing a visual representation of data, it s providing the right kind of visualization
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