package radiantadvisors visual design series

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It’s a cognitive truth: our visual system is enormously influential in

our brains. As an information learning and processing mechanism,

our brains are our best tool in decoding and making sense of

information presented visually. The best data visualizations are

designed to properly take advantage of “pre-attentive features”

– a limited set of visual properties that are detected very rapidly

(typically 200 to 250 milliseconds) and accurately by our visual

system, and are not constrained by display size.

Through pattern recognition, color recognition, and counting,

data visualization exploits elements of our brain’s intrinsic

horsepower to help us better see and understand data. With self-

service and user-oriented data visualization tools, we can leverage

our natural hardwiring to layer visual intuition on top of cognitive

understanding to interact with, learn from, and reach new insights

from our data. We can also enter a visual dialogue with our data

to find new answers and ask new questions.

This first brief in a four-part series will take a high-level look at:

• A distilled introduction to the science of data visualization

• Key cognitive ingredients to have a visual dialogue with data

• How to curate meaning in the data through visual cues

DATA VISUALIZATION IS A SCIENCE

The brain is a remarkable organism. Even with recent advances in

cognitive science and perceptual psychology, our understanding

of the brain is still somewhat primitive. There are many questions

on how our brains work that we still have yet to answer – and

even more questions that we have yet to figure out even how to

ask. However, after much research we do know that the brain is

a highly visual mechanism. We know that areas like Wernicke’s

Area and Broca’s Area are designed for the comprehension and

processing of language, and we know that the Visual Cortex – the

bi-hemispherical part of the brain responsible for processing visual

information – lights up when presented with colors and shapes.

Being able to see and understand data requires more than simply

drawing up a collection of graphs, charts, and dashboards. Data

visualization is a creative process, and we can learn to enrich it

by leveraging years of research on how to design for cognition

and perception. Think of successful data visualization from a visual

science perspective, and consider the careful balance of the art

of visual design and curation alongside the observations and

insights of data science. The most meaningful data visualizations

will be the ones which correctly present complex information in a

way that is visually meaningful, memorable, and actionable.

Data visualization should work to establish visual dialogue –

to leverage our cognitive visual hardwiring and the power of

perception to have a “conversation” with the data to glean new

information in salient, memorable, and lasting ways. The data

visualization is the tangible byproduct of when art and science

come together to facilitate a visual discussion of data.

KEY COGNITIVE ELEMENTS IN VISUALIZATION

Any work of art relies on core visual principles and elements. Three

key building blocks of visual analysis are pattern recognition, color

use, and counting. They are interrelated and can be integrated to

create meaning visually from data.

Patterns and Organization

The way we perceive patterns is one of our most interesting

cognitive functions. Patterns – the repetition of shapes, forms, or

textures – are a way of presenting information to help our brains

discriminate what is important from what is not. There are patterns

around us every day that we may not even recognize – for example,

the way television show credits list actors in a series (generally the

top star first and the second last, making the first and final data

points in the pattern the most significant). Patterns are how our

brains save time decoding visual information: by grouping similar

objects and separating them. The Gestalt principles of design

emphasize simplicity in shape, color, and proximity and look for

continuation, closure, and figure-ground principles. The German

word gestalt translates to “shape form,” or pattern.

I N S I G H T S E R I E S

THE SCIENCE OF DATA VISUALIZATION

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When we look at any data visualization, one of the first things that

the brain does is look for patterns. We discriminate background

from foreground to establish visual boundaries. Then we look to

see what data points are connected and how (otherwise known as

perceptual organization) – whether it is through categorical cues

like dots, lines, or clusters, or through other ordinal visual cues

like color, shapes, and lines. Typically, there are five core ways to

apply pattern recognition:

Proximity – Objects that are grouped together or located close

to each other tend to be perceived as natural groups that share

an underlying logic. Clustered bar graphs and scatter plots utilize

this principle.

Similarity – This principle extends proximity to also include items

that are identical (or close). This gives the brain two different

levels of grouping: by the shared, common nature of objects, as

well as how close they are. Geospatial and other types of location

graphics utilize this principle.

Continuity – It is easier to perceive the shape of an object as part

of a whole when it is visualized as smooth and rounded – in curves

– rather than angular and sharp. Arc diagrams, treemaps, and

other radial layouts use this principle.

Closure – Viewers are better able to identify groups through the

establishment of crisp, clear boundaries that help isolate items

and minimize the opportunity for error (even if items are of the

same size, shape, or color). This effect would be applied, for

example, in a clustered bar chart to add additional organization

to the pattern by alternating shading the area behind groups of

bars to establish boundaries.

Patterns – These help to establish clear visual organization,

composition, and layout. Once we can see patterns in information,

we can next layer visual intuition on top of cognitive understanding

to come to new conclusions. This is where color and counting

come in.

Color Use

Colors and shapes play a large part in patterns. Color (or the lack

of) differentiates and defines lines, shapes, forms, and space.

However, the use of color in design is very subjective, and color

theory is a science on its own. Colorists study how colors affect

different people, individually or in a group, and how these affects

can change across genders, cultures, those with color blindness,

and so on. There are also many color nuances, including overuse,

misuse, simultaneous and successive color contrast, distinctions

between how to use different color hues versus levels of

saturations, and so on. For this discussion, let’s focus simply on

when and how to use color in visualization to achieve unity.

In The Functional Art, Cairo writes, “The best way to disorient

your readers is to fill your graphic with objects colored in pure

accent tones.” This is because pure colors – those vibrant “hues

of summer” that have no white, black, or gray to distort their

vibrancy – are uncommon in nature, so they should be limited

to highlight important elements of graphics. Subdued hues

– like gray, light blue, and green – are the best candidates for

everything else. Most colorists recommend limiting the number

of colors (and fonts and other typography) to no more than two

or three to create a sense of unity in a visual composition. Unity is

created when patterns, colors, and shapes are in balance.

When thinking about color use in your data visualization, focus on

how you are applying your color efforts as visual targets:

Perceptual Pop-Out is the use of color as a visual beacon or target

to pre-attentively detect items of importance within visualization.

The shape, size, or color of the item here is less important than its

ability to “pop out” of a display. Consider a visit to the eye doctor,

when your vision is tested by the ability to spot a flash of color in

a sea of darkness.

Conjunction Target is the inefficient combination of color and

shape. Rather than giving target feature one visual property,

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conjunction targets mix color and shape. That distracts and

causes visual interference, making visual analysis and other

cognitive processes slower and more difficult. Thus, it is prudent

to maximize cues like perceptual pop-out, while not inadvertently

interrupting the pre-attentive process with conjunction targets.

As illustration, the figure

below is inspired by a picture

made by visualization guru

Stephen Few. It is much

harder to see the number

6 in sequences without the

benefit of shading. Likewise,

we can take advantage

of perceptual pop-out by

adding an additional color

element into the picture. This example shows how elements of

pattern recognition, color, and shape can be used together while

avoiding the clutter of conjunction targets.

Counting and Numerosity

Color and counting work in tandem, as do counting and patterns.

Relatively new research shows that the brain has an ordered

mapping, or topographical map, for number sense, similar to

what we have for visual sense and other pre-attentive features.

There are two relevant counting conversations within the scope

of data visualization. First is how data visualizations try to

reduce counting by clustering or similar approaches designed

to replace similar data objects with an alternative, smaller data

representation. Histograms take this approach. Visual spacing on

linear scales versus logarithmic scales is another example.

Second is numerosity – an almost instantaneous numerical

intuition pattern that allows us to “see” an amount (number)

without actually counting it. This varies among individuals (people

with extreme numerosity abilities are known as “savants”).

Numerosity itself is not an indicator of mathematical ability. For

most of us, numerosity gives us the ability to visually “count”

somewhere between two and ten items. We can further enhance

numerosity with visual elements like color. As an exercise, glance

quickly at Figure 1 above. How many orange sixes do you see? If

you “see” 7, you are correct. Most types of data visualization will

include a numerosity effect, however those that take advantage

of data reduction processes will be most beneficial for numerosity.

Consider scatter plots, histograms, and other clustering

visualizations.

CONCLUSION

The downside to being able to visually create meaning so quickly

and efficiently is that our brains can betray us and leave us with a

wrong idea – visual bias. Data visualizations are intended to clearly

and effectively communicate the correct information and insight.

Thus, we should pay close attention to recognizing key cognitive

elements in visualization, and how these should be used together

to craft a meaningful representation of data. This Research Brief

has highlighted some key cognitive elements affected by data

visualization. The next Brief in this series will review the visual

elements of formal analysis that are building blocks to visual

discovery.

This brief was originally published by the International Institute of

Analytics (IIA) in July 2015

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Radiant Advisors is a leading strategic research and advisory firm that delivers innovative, cutting-edge research and thought-leadership to transform today’s organizations into tomorrow’s data-driven industry leaders.To learn more, visit www.radiantadvisors.com

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When you first look at a data visualization, you may not realize just

how much careful thought and effort has gone into crafting it. ata

visualizations are multi-faceted, not just because of their ability

to represent multiple types and layers of data in a meaningful

way, but because to craft the visualization itself requires attention

to every detail from color choice, to the methods in which the

important pieces of information are connected into patterns, to

the layout and typefaces used throughout the graphic.

In the previous brief, The cience of ata isualization, we

focused on features color, patterns, and counting that exploit

our brains’ intrinsic pre-attentive horsepower and form the basis

of a visual dialogue with data. In this brief, we’ll take a deeper

look into how to establish visual meaning of data through the

intentional selection of specific elements. These elements lines,

textures, shapes, and typography are some of the visual cues

that influence how our eyes move around a visual to separate

important areas from unimportant areas. These visual cues guide

us to organize the information to facilitate meaning through visual

discovery.

LI

The most basic building block of visual analysis, lines have several

purposes in data visualization. They are used to create complex

shapes discussed in the following section), to lead us visually

through or to) different areas of the visualization, or as a way to

layer texture on a visual surface.

Lines are especially potent tools to reinforce patterns or order.

This is because they offer powerful cues for our brains to perceive

whether objects are intended to grouped or linked together.

onsider a diverse group of different colored shapes. With its

pre-attentive capabilities, the brain will automatically group

them by shape and color. Adding a line to connect a subset of

common shapes will add more connectedness, and produce a

more powerful pattern see Figure 1).

Figure 1 – This figure shows the added patterning influence of a

line as a visual cue.

Lines can be used as labels, directional cues, or they can also

be used as a way to create texture in data visualization. Texture

is one of the more subtle design elements to include in data

visualization, but worth a mention due to its relation to lines and

shapes and our previous discussions on color).

Texture is defined as the surface characteristics or, the feel) of

a material that can be experienced through the sense of touch

or the illusion of touch). It can be used to accent an area so that

it becomes more dominant than another, or for the selective

perception of different categories. olors, shapes, and textures

can be combined to have further levels of selection. Finally,

textures of increasing size can represent an order relation. Because

it is usually accompanied with real adjectives like rough, smooth,

or hard, texture may seem intrinsically three-dimensional real).

It can also be two-dimensional. onsider how some graphs take

advantage of angles like points or circles).

In data visualization, lines are one of the best ways to represent

texture. For example, when used in tandem with color, lines can

create texture through what is called color weaving to produce a

tapestry of woven colors to simultaneously represent information

about multiple co-located color encoded distributions. For

example, color weaving is similar to the texturing algorithms

and techniques used to visualize multiple layers in topographical

maps, weather maps, and or other climatology visualizations.


VISUAL DESIGN BUILDING BLOCKS

1 Pre-attentive features are defined as a limited set of visual properties that are detected very rapidly and accurately by our visual system, and are not constrained by display size



HAP

In his article The Tall ffice Building Artistically onsidered,

Louis ullivan 1 6) made a statement that has forever affected

how we approach the premise of shapes and forms. He said All

things in nature have a shape, that is to say, a form, an outward

semblance, that tells us what they are, that distinguishes themselves

from another. ver the years, this form follows function refrain

has been taken both literally and completely misinterpreted it has

also been described as coarse essentialism by data visualization

design gurus like Alberto airo). Today, in visual design, it is a

powerful mantra primarily applied to the relationship between

the forms of design elements, i.e., shapes and lines) to the

informational function it is intended to serve.

As forms, shapes are one of the ways that our brains understand

patterns. This is a time saving technique: we will immediately

group similar objects and separate them from those that look

different. o, shapes and forms should be used to achieve what

the visual is attempting to communicate about the data. Again,

refer to Figure 1 above. This visual shows, at a glance, the impact

that shapes can have when looking at information.

hapes are formed with lines that are combined to form squares,

triangles, circles, and so on. They can be organic irregular shapes

found in nature circles, etc.) or geometric shapes with strong

lines and angles like those used in mathematics). Likewise, shapes

can be two-dimensional ) or three-dimensional ). These

shapes expand typical two-dimensional shapes to include length,

width, and depth they are things like balls, cylinders, boxes,

and pyramids. In data visualizations like pictograms, infographic

forms and shapes can be expanded significantly through the issue

of icons and other symbolic elements as extensions of traditional

shapes. onsider, for example, the use of shapes of people in lieu

of dots or other shapes.

Further, like so many other elements of design, color has a hand

in shape selection. This is particularly relevant in two ways. First,

make sure you are using color priority in choosing a shape if you

want to use circles to emphasize areas of opportunity for sales

agents on a map, use green circles instead of red or orange).

econd, be aware of color contrast and luminance between

shapes. The higher the luminance contrast, the easier is it to see

the edge between one shape and other. If the contrast is too low,

it can be difficult to distinguish between similar shapes or to

even distinguish them at all.

Like many visual cues, there is often no one right way of

encoding visualization properly through the use of shapes and

forms. any times it becomes less a question of correct and

more a consideration for what is easier for the viewer. As an

example, both scatterplots and bar charts can be used to represent

absolute variables. Both of these also support the description of

the use of shapes, forms, and colors to aid visual meaning of data.

T P G APH

Generally speaking, when we think of typography within the

context of a data visualization, we think in terms of two choices

of type categories: serif versus sans serif. While the origin of the

word serif is unknown, a common definition for it has come

to be feet. Thus, serif typefaces like Times ew oman or

Baskerville, are those with feet. Building on the previous,

the word sans comes from the French without. Thus, sans serif typefaces are those without feet. erif fonts are usually

considered to be more traditional, formal typefaces, while sans

serif typefaces tend to have a more contemporary, modern feel.

While these rules of thumb exist, there are no absolutes of when

or when not to use a serif versus a sans serif typeface.

In fact, in his book Data Points: Visualization That Means

Something, athan au pointedly notes that while there has been

much discourse on the best typeface, there has yet to be any true

consensus. This goes to further emphasize that typeface selection

is highly variable and depends much on personal preference. As

key takeaways, consider these three points:

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First, be conscious of the amount and types of

typefaces and fonts used in a single visualization

Second, different typefaces and fonts have

different connotations and perceptions

And, while there is no absolute rule on typeface

selection, general rules of thumb apply

That said, there are a couple of important points to keep in mind

when making a typeface or font selection. First, typography, like

another other visual element, is no stranger to bias. There are some

typefaces for example, omic ans that have been reduced to

a sort-of comic strip application and are not taken seriously. thers

like Baskerville and Palatino conjure up nostalgia imagery due

to their historical use in vintage graphics. ome typefaces have

been custom-created for use in advertising like those fonts used

in tar Wars or Back to the Future, for example and are pigeon-

holed into their use in genre-related opportunities which is not

necessarily a bad thing, but one to be aware of). any typefaces

are also said to have personality. Like omic ans, others may come

with a more light-hearted or conservative personality. atching

type personality with the tone of the message in the visualization

is certainly not an exact science.

A good technique to see if you’re choosing appropriate fonts is to

use a font that seems completely opposite of what you’re trying

to convey. eeing how wrong a typeface can look will help

you make a more appropriate selection. hristoph Papenfuss’

blog, Performance Ideas, shares a visualization that is a perfect

example of how fonts should not be used in data visualization in

a 11 spending report from Papenfuss’ hometown in Germany.

The visualization has a long list of errors in design. pecific to

typography choices, it uses text that is so dense it is rendered

almost completely unreadable. ou can see graphic at http:

www.performance-ideas.com 1 6 poor-visualizations .)

The most pointed advice one can be given on typography is

this: use typeface and fonts with a purpose. It is easy to dismiss

the importance of these selections, possibly because we are so

conditioned to read text that we have become accustomed to

focusing on the content of the words and not what they look like

visually. However, the visual appearance of words can and does)

have just as much effect on how a document is received as the

content itself. Fonts can create mood and atmosphere they can

give visual clues about the order in which a document should be

read, and which sections are more important than others. Fonts

can even be used to control how long it takes someone to read a

document. Like colors, typefaces are typically chosen in corporate

style guides and other branding design decisions. It is valuable to

understand why typography is as important in data visualization as

any other design element.

P TTI G IT T G TH

ou might have noticed a theme in many of these discussions on

visual cues. They all seem to tie back to one or more pre-attentive

features discussed in the first brief. It is easy to see how visual

elements like lines, textures, shapes, colors, and typography

stimulate cognitive pre-attentive features in our brains that are

so critical in visual analysis. Hence, these are the building blocks

of visual discovery, intended by design to be layered upon each

other and used in mix-and-match fashion to make the most of the

visual capacity of data visualization.

This brief was originally published by the International Institute of

Analytics (IIA) in August 2015

adiant Advisors is a leading strategic research and advisory firm that delivers innovative, cutting-edge research and thought-leadership to transform today’s organizations into tomorrow’s data-driven industry leaders.To learn more, visit www.radiantadvisors.com




The first two briefs in this series The cience of ata isualization

and isual esign Building Blocks) have attempted to disentangle

the compounding factors that affect visual cognition, including

perceptual pop out, pattern recognition, numerosity, etc.

These previous briefs have effectively set the foundation

for understanding the science of data visualization and key

visual design building blocks that make data visualizations so

immediately meaningful by leveraging our brains’ pre-attentive

horsepower.

This brief, esigning for isual xperience, moves beyond the

premise of achieving efficacy in balancing art and science, to

understanding how to create a visual experience for complex

information through the lens of data visualization. We focus

on the value of data visualization as a form of communication

and storytelling, and we emphasize achieving balance within a

triangle of design constraints that amplify the viewer experience

of visualization and capitalize on visual memorability and learning.

WE REMEMBER VISUALLY

ecently, attributes-based visual recognition has received a lot of

attention with in-depth studies occurring in both academia and

industry. any of these studies have provided clear data and

learning opportunities. We’ve long known that we can remember

upwards of 1 , images at one time, and that we can recall

these images accurately at an percent recollection rate. This

speaks volumes to a person’s visual capacity and the power of

pictures as recollection engines in the brain. We also know now

that visualizations that blend information with influential features

such as color, density, and content themes such as recognizable

icons) significantly and reliably increase learning, memorability,

and recall. Further, seeing and interacting with an image in

combination with traditional written and verbal instruction has

been consistently associated with higher levels of retention and

understanding of salient ideas.

The Great Less is ore ebate

There is ongoing debate in the visualization community regarding

the role that a visualization type plays in data understanding and

memorability. And, like all great debates, there are convincing

arguments on both sides of the topic.

The conventional view argues that visualizations should be

devoid of chart junk and curated with as few design elements

as possible, and that simplifying a visualization increases

memorability and information saliency without the distractions

that lead to potential misinformation and conclusions. upported

by many data visualization gurus, this position is reinforced by

many psychology lab studies.

However, there is a body of research that argues against

lightweight design: chart junk might actually improve retention

and force a user to expend more cognitive effort to be able to

understand and learn from the visual, thereby increasing both

knowledge and understanding of the underlying data.

Figure 1 isual representations and examples of) the Less is ore approach

The original visualizations can be found at: ooblogs and The preadsheet Page


DESIGNING FOR VISUAL EXPERIENCE



Less is ore

A recent collaborative research project by computer scientists

from Harvard and cognitive scientists from IT1 explored

cognitive memorability of visualizations. The researchers began

with hopes of uncovering empirical evidence to support the

theory that while each of our sets of memories is unique, we have

the same algorithm embedded in our minds to convert visual

communication to memory, and thereby learn and retain learning.

sing a publicly available data set, the researchers augmented

an object and scene annotations to include spatial, content, and

aesthetic image proprieties). ome of their research findings were

surprising. For example, one highlight was that unusualness and

aesthetic beauty are not associated with high memorability, but

instead negatively correlated with memorability. This challenged

the popular assumption that beautiful images beauty being a

function of aesthetics) are more valuable in memory currency.

Another interesting find was that a visualization is instantly

and overwhelmingly more memorable if it includes a human-

recognizable element, such as a photograph, person, cartoon, or

logo. These types of elements essentially provide our memory

with a visual cue to build a story around, linking back to our

most primitive form of visual communication: symbolism, or the

practice of representing things by symbols. This finding creates

a compelling case for the use of icons in visualizations such as

infographics, which often rely on symbols to communicate large

or complex data in straightforward ways.

ore is ore

n the other side, a study2 on what makes a visualization

memorable began with researchers building a broad, static

visualization taxonomy of the large variety of data visualizations

in use today. The researchers collected nearly 6, visual

representations of data from various publications and used a

wide range of attributes to categorize these images. ext, the

researchers exposed the images to participants via Amazon

echanical Turk) and tested the influence of features such as

color, density, and content theme) on participants’ memorability.

The study confirmed previous research findings that faces

and human-centric scenes are more memorable than others.

pecifically, people and human-scale objects contributed most

positively to the memorability of images. The results of this study

also confirmed that certain design principles make visualizations

inherently more memorable than others, irrespective of a view’s

individual context and biases.

However, along with validations this study also revealed some

contrary findings. For example, visualizations with low data-to-

ink ratios and high visual densities more chart junk) were actually

more memorable than minimalist data visualizations. Likewise,

unique visualizations that left a lasting impression were more

memorable than traditional, common graphs bar charts or line

graphs which are considered part of the data visualization canon.

THE VISUAL EXPERIENCE BEGINS WITH A PICTURE

The above arguments aren’t to say that bad data visualizations

those that fail to take into account design considerations or

those that incorrectly graph information are okay. f course,

they’re not. But, that said, the research does reinforce the

power of pictures as a key part of how we communicate, learn,

and remember. oreover, it does support the idea that there

is more than one way to communicate effectively through data

visualization.

Through our intrinsic hard wiring to communicate, learn, and

remember visually, we develop the capacity for visual dialogue.

This visual dialogue is paramount to the experience of visualization.

onceived by athan nobler, former chair of the niversity of

onnecticut’s art department, visual dialogue is the exchange

that occurs between the artist, his work, and its consumer. It builds

upon the basis of visual memorability and literacy.

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1 Isola, P., iao, ., Torralba, A., liva, A. What makes an image memorable? I onference on omputer ision and Pattern ecognition P ), 11. Pages 1 -1 .


ognitive psychologists describe how the human mind, in its

attempt to understand and remember, assembles bits and

pieces of experiences and information into a story. To aid in this

story-building process, we automatically conjure images visual

cues associated with the story. This is the core of visual data

storytelling, but it speaks inherently to the power of pictures as

a visual experience. ata visualizations highlight the connections

between visualizations, design, and elements of science, and they

allow audiences to explore and develop a connection through

personal insight and experience.

THE PICTURE SUPERIORITY EFFECT

The concept of The Power of Pictures is not new it has been

described as an argument that’s both old and perennial. As an

example, consider this quote from a turn of the century text on

nglish composition: In some respects words cannot compare

in effectiveness with pictures. The mere outlines in a Greek vase

painting will give you a more immediate appreciation of the grace

and beautify of the human form than the pages of descriptive

writing. A silhouette in black paper will enable you to recognize a

stranger more quickly than the most elaborate description in the

world.

The Picture uperiority ffect P ) recognizes that concepts

learned by viewing pictures are more easily and more frequently

recalled than those learned purely by textual or other word-form

equivalents including audio or other information that is learned

by hearing). evelopmental molecular biologist ohn edina

quantified the P in the following way: when we read text, three

days later we only remember 1 percent of the information. et,

text combined with a relevant image is more likely to be recalled

at a much higher rate 6 percent in three days.

Whether art, memories, or illustrations for educational purposes,

people are drawn to pictures. This is because of the experience:

pictures conjure emotions, memories, and insights. They

stimulate new thinking. In this context, even more important than

our incredible ability to remember huge quantities of images with

a highly accurate recollection rate is the fact that we remember

them better than words. And we remember the images long after

we have forgotten the words that go with them. While words and

definitions vary from language to language, visualization as a

human communication mechanism is universal.

Figure isual representation and example of) the Picture uperiority ffect

TRIANGLE OF FORCES

In a previous brief in this series, we noted that data visualization

is a highly curated hybrid of forces and elements: when you first

look at a data visualization you may not realize just how much

careful thought and effort went into designing it. In this brief,

we’ve discussed two research views on when a data visualization

accurately conveys a message and when it reaches the tipping

point of too much.

Frequently, the intent of visual design is to clearly and effectively

communicate a single message. The best data visualizations are

those where nothing stands between the visual’s message and its

audience. While research is ongoing and the debate continues,

the simple truth is that when visual cues are used correctly, they

can bring data to life and give it more context, meaning, and

resonance. ata visualizations should be focused on the message

in the data, and visual enhancements like hue, saturation, size,

and color) should be used for emphasis rather than explanation.

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2 Borkin, ., o, A., Bylinski, ., Isola, P., unkavalli, ., liva, A., Pfister, H. 1 ). Proceedings from I I F I 1 : What akes a isualization emorable?. Atlanta, GA: I .


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very graphic is shaped by a triangle of constraints: the tools and

processes that make it, the materials from which it is made, and

its purpose or utility. This idea of design constraints as a triangle

of forces came from acob Bronowski, a mathematician, whose

discussions on context and visualization had a large impact. In

The hape of Things Bronowski wrote: The object to be made

is held in a triangle of forces If the designer has any freedom, it

is within this triangle of forces or constraints.

However, Bronoswki’s triangle of forces is not fixed. ach of its sides

can move, and as they do, the other sides in tandem so that they

move along with it. ach move of one side puts strain on the other

two. Thus, it is important to not only recognize the parameters of

the triangle of forces, but to strive for balance within it.

Figure isual representation of the Triangle of Forces Theory

This brief was originally published by the International Institute of Analytics (IIA) in

September 2015

L I

emorability is an intrinsic feature of visual information and

is reproducible across a diverse spectrum of visualization

mechanics. isuals that blend information with influential features

are significantly more memorable. We have high visual memory

capacity and can recall thousands of images for a long time. ore

importantly, concepts learned by viewing pictures are more easily

and more frequently recalled than those learned purely by textual

or other word-form equivalents.

ertain design principles make visualizations consistently more

memorable than others, irrespective of a viewer’s individual context

and biases. isuals that blend information with influential features

are able to create a visual experience for the viewer and, therefore,

are significantly more memorable. eeing and interacting with an

image also has been consistently associated with higher levels of

retention and understanding of salient ideas. Poorly designed data

visualizations that misrepresent data may very well be memorable

but for all the wrong reasons, making them possibly dangerous.





Previous briefs in this visual design series have stepped through

the science, artistic curation, and various cognitive processes

enlisted in building a data visualization. In each of these briefs,

we’ve covered assorted design considerations for developing

data visualization for improved visual discovery and analytics.

However, so far we approached this primarily through a lens

of functionality and principles for use. In addition, there is a

substantial contribution that the individual consumer of the

visualization brings to the table in how data visualizations are used

and understood, whether it’s an analyst user, business leader,

customer, and so on. However, although this fact is acknowledged

and a wealth of long-standing literature covers the technical

aspects of both design and visualization, the collaborative, social,

and organizational aspects are less studied.

In this fourth and final brief, we tackle some of the key elements

of designing for influence by exploring the importance of viewer

perception and how this can be leveraged through certain

applications of techniques used to influence the user. This is the

human element of visualization, not the scientific, artistic, or

cognitive, and it plays a critical role in communicating through

data visualizations. In particular, interactivity and emotion

(storytelling) are two ways we can leverage data visualization to

influence our audience.

TH P W F I W P PTI

Perception is not a passive experience. ecent research in the

psychology of perception indicates that visual perception is

an exploratory and active process wherein we do not see a

sequence of static images, but a continuous flow of changing

scenes and imagery. Thus, we have to actively look around

to get a more comprehensive image of the information being

presented. To support this, the use of data visualization is often

aptly characterized as an active and exploratory process with the

goal to yield sometimes-complex insights. uring the course of

this process, data analysts and other users routinely generate

hypotheses, and then test them against the data that is visualized.

This visual analysis process is akin to the scientific method: we

guess what we think the information presented is intended to

mean (or could mean) and then look for additional data points

and visual cues to refine and confirm that.

This process of visual querying supports the notion that

perception is indeed active as opposed to passive. Figure 1,

which is borrowed from is aster’s ix, Pohl, and llis’ book,

Mastering the Information Age1, illustrates this process by

indicating ten steps or aspects of the active nature of processing

visual information.

Figure 1 From ix, Pohl, and llis, this visual illustrates a simplified view of the

broad visual analytics process

Though data is core to the analytical process, the human is

fundamentally at the heart of visual analytics. Therefore, the user

is in a position to affect multiple aspects of the visual process,

including visual perception, human interaction, and problem

solving analysis and activities driven by data visualization.

While there are multiple cognitive and perceptual processes

that will affect user biases and responses to data stimuli, we

must remember that visualized data is largely used by people.

Further, these people may or may not have received formal data

analysis education or possess detailed knowledge of visual design

principles. It is people who use a visual to make decisions and

take action.


DESIGNING FOR INFLUENCE

1 http: www.vismaster.eu news mastering-the-information-age



Also, while reactions will to some degree vary from person to

person, the important takeaway is that people are different, and

we have to be aware of these variances and accommodate them.

Instead of focusing this final brief on more of the wide context of

core elements for successful visual design, we narrow the focus

on the human aspects between a user and a data visualization

in terms of interaction and analytical processes and emphases.

With this in mind, we can better grasp the influence of actions

like interacting with visual stimuli or inviting emotion with visual

data storytelling.

I ITI G I T A TI I AL I

In data discovery, we often describe the discovery process as

iterative and agile. Further, we assume that analysts working

with data require the ability to move quickly and fluidly through

information and tools to extract, explore, and uncover new

meanings and analytical models generally referred to as

insights in previously unexplored information, or even in new

configurations of blended data. Another important element,

especially for the use of data visualizations as a vehicle to visual

discovery, is interactivity the ability for users to interact directly

with the data and the accompanying visualization to deepen

analytics insight.

Interactivity supports the type of visual thinking that drives visual

discovery. Without interactivity, visual discovery falls short of its

intended purpose and its analytical functionality is constrained

by the limitations of static imagery. However, with the right

interactivity, data visualization becomes a natural extension of

the users’ thought process. Interactivity, then, is the element that

allows users to play with data: to manipulate information into

patterns, theorize about meanings, project interpretations, explore

possibilities all while balancing the fixed content, context, and

relationships of the data with creativity and imagination.

Interactive capabilities in data visualization should support and

facilitate the users’ intentions when visually exploring data sets.

onsider the following list of interactive category types suggested

by researchers i et al2:

• elect: identify and select items of interests, possibly as a

precursor to another operation

• xplore: activities that include movements such as zooming,

panning, resampling, etc.

• econfigure: spatially rearrange the data e.g., sort, rotate,

change attributes assigned to axis)

• ncode: alter visual appearance e.g., change view or adjust

attributes like color, size, and or shape)

• Abstract laborate: show more or less detail drill down or up)

• Filter: select show data matching specific conditions or criteria

• onnect: highlight data related items e.g., brushing)

A word of caution here: the work of an analyst is influenced by a

host of cognitive biases, and many of these are set into motion

by the way information is “fed” into the perceptual cognitive

processes enlisted through data visualization. Interactive

visualization designs may influence biases and vice versa), and

thus we need to aware of how interactive visualizations affect

these and work to minimize them. For example, consider the

difference that a data visualization printed on paper may yield

insomuch as the users’ ability to visually move through the data by

drilling down or expanding certain elements, and how much more

valuable this process would natively be in a digital environment.

ITI G A I A WITH TI

Award-winning writer and director obert c ee said that the

most powerful way to motivate people to action is by “uniting

an idea with an emotion. The best way to do that is by telling

a compelling story that weaves in a lot of information and also

arouses the listener’s emotion and energy. c ee argues that a

good story fulfill s a profound human need to grasp the patterns

of living not merely as an intellectual exercise, but within a very

personal, emotional experience. A good story turns a one-sided

narrative into a conversation it influences an audience to action.

isual data stories should always put data at the forefront

of storytelling. However, data stories differ from traditional

P2/3

2 i, . ., ah ang, ., tasko, . T., acko, . A. ). Toward a deeper understanding of the role of interaction in information visualization. Visualization and Computer Graphics, IEEE Transactions on, 13 6), 1 -1 1.


P3/3

storytelling that typically chains together a series of causally related

events to progress through a beginning, middle, and end with the

goal of delivering a message. While data stories can be similarly

linearly visualized, they can diverge. ore important, data stories

can also be interactive to invite discovery, solicit new questions,

and or offer alternative explanations.

What makes visual data storytelling different from other forms of

storytelling is the complexity of the content being communicated

within the constraints of effective data visualization. It is essentially

information compression that condenses information into

manageable pieces by focusing on what’s most important, and

pretending it is bound entirely within the visualization(s) used

to illustrate the message. To that end, graphical techniques and

interactivity can enforce various levels of structure and narrative

flow. For example, consider an early reader picture book, which

illustrates salient points with a visual sometimes interactive to

emphasize key points of learning. And, though static visualizations

have long been used to support storytelling, today an emerging

class of visualizations that combine narratives with interactive

data and graphics is taking more of the spotlight in conveying

visual narratives.

For the purposes of storytelling, the basic plots of visual data

stories can be articulated as the following:

• hange over time see a visual history as told through a

simple metric or trend

• rill down start big, and get more and more granular to

find meaning

• oom out reverse the particular, from the individual to a

larger group

• ontrast the this or that

• pread help people see the light and the dark, or reach of

data (disbursement)

• Intersections things that cross over, or go from less than to

“more than” (progression)

• Factors things that work together to build up to a higher-

level effect

• utliers a powerful way to show something outside the

realm of normal

As a best practice, visual data stories are most effective when

they have constrained interaction at various checkpoints within

the narrative, allowing the user to explore the data without

veering too far away. isual cues for storytelling include things

like annotations to point out specific information using color to

associate items of importance without having to tell them or even

visual highlighting (e.g., color, size, boldness) to connect elements.

ome data visualization tools are being enhanced with storytelling

capabilities, too, and vendors in the space are supporting the use

of this engagement and influencing technique as part of their

tool’s platform. Tableau, for example, has its story points lik its

storytelling snapshots ellowfinBI its storyboard.

L I

Interactivity and storytelling both affect how the user experiences

a data visualization and are primary means of influencing audience

response to information presented.

With interactivity, a user is invited to get hands-on with the data,

and explore the information with visual thinking that drives

visual discovery. Thus, the data visualization becomes an extension

of the user’s thought process and enables them to “play” with

data and discover their own insights. Likewise, storytelling is an

appropriate way to unite an idea with emotion in data visualization.

The best data stories are influential: they are interactive to invite

discovery, engaging to solicit new questions, and thought-

provoking by offering alternative explanations to convey

compelling visual narratives.




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