Graphics Reference
In-Depth Information
Using visual analysis to find stories
The following is a quote from Ben Schneiderman:
"Visualization gives you answers to questions you didn't know you had."
In the Chapter 2 , Setting the Purpose and Identifying Key Factors , we discussed the
different intentions and motives you might have for developing a data visualization.
In most cases we think of it as something we create and provide to others. What we
sometimes neglect to consider is the potential of visualization for ourselves, when
we are the intended users looking to discover insights about a subject.
This is where we consider the application of visual analysis. Visually analyzing a
dataset, and employing both inductive and deductive reasoning, enables us—as the
designer—to learn more about our subject by exploring a dataset from all directions.
As Ben Schneiderman articulates above, and as we saw through the demonstration
of Francis Anscombe's experiment, rather than just looking at data, we are using
visualization to actually see it, to find previously undiscoverable properties of our
raw material, to learn about its shape, and the relationships that exists within.
This activity can also be described as data sketching or preproduction visualization.
We are using visualization techniques to become more intimate with our raw
material and to start to form an understanding of what we might portray to others
and how we might accomplish that.
Visual analysis requires a high degree of graphical literacy, the ability to read and
interpret data represented visually. This is something we might not really think
about too often. In fact, if we're honest, many of us would probably have to admit
that we can actually be quite passive in how we engage with a visualization or
infographic.
This activity requires a much more committed level of attention to interpretation. As
we explore the evolving visual analysis of our data, we need to be prepared to observe
the following characteristics that will lead to the identification of our key stories:
Comparisons and proportions:
Range and distribution : Discovering the range of values and the shape of
their distribution within each variable and across combinations of variables
Ranking : Learning about the order of data in terms of general magnitude,
identifying the big, medium, and small values.
 
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