Conceiving and Reasoning Visualization Design Options - Data Visualization: A Successful Design Process - page 78

The visualization anatomy - data
representation
The process of identifying the most effective and appropriate solution for
representing our data is unquestionably the most important feature of our
visualization design. Working on this layer involves making decisions that
cut across the artistic and scientific foundations of the field.
Here we find ourselves face-to-face with the demands of achieving that ideal
harmony of form and function that was outlined in the objectives section of Chapter
1 , Context of Data Visualization . We need to achieve the elegance of a design that
aesthetically suits our intent and the functional behavior required to fulfill the
effective imparting of information.
What we're doing here is determining how we are going to show what it is we want
to say. It is a difficult skill to master—something of a dark art—particularly given
the set of factors we need to consider and the trade-offs we might need to make. Our
task involves considering the following:
• Choosing the correct visualization "method" for the stories we're telling
• Accommodating the physical properties of your data
• Facilitating the desired degree of precision
• Creating an appropriate metaphor to depict our subject stylistically
Choosing the correct visualization method
The first matter is to determine the choice of visualization method. We aren't
necessarily committing just yet to a specific chart or graph type, though we might
have some in mind. Rather, this is about the general family or collection of chart
types as defined by their primary storytelling method.
For example, a bar chart serves the function of comparing categories of values; a line
chart, by contrast, enables us to show changes of values over time, geo-spatial data
can often be best displayed over a map.
Your choice of visualization method will be mostly driven by the outcome of your
work in Chapter 3 , Demonstrating Editorial Focus and Learning About Your Data . You've
developed your editorial thinking about the key data stories, analytical dimensions,
and the questions you're trying to answer in your visualization.
Of course, it is often likely that you have determined several different analytical
slices and you will probably need to consider different methods to appropriately
convey the stories for each one.