ITS53031 University of Cumberland Data visualization Research Paper Reference: Kirk, Andy. Data Visualisation: A Handbook for Data Driven Design (p. 50). S

ITS53031 University of Cumberland Data visualization Research Paper Reference: Kirk, Andy. Data Visualisation: A Handbook for Data Driven Design (p. 50). SAGE Publications.(Chapter 1)

Assignment: Discuss and expand on the 4 components of Kirk’s definition with regards to visualizations:

Representation
Presentation
Data
Understanding

Research paper should be at least 3 pages (and at least 800 words), double-spaced, have at least 4 APA references, and typed in an easy-to-read font in MS Word (other word processors are fine to use but save it in MS Word format). Cover page should contain the following: Title, Student’s name, University’s name, Course name, Course number, Professor’s name, and Date. Data Visualisation
A Handbook for Data Driven Design
Andy Kirk
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? Andy Kirk 2016
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1
Defining Data Visualisation
This opening chapter will introduce you to the subject of data visualisation, defining what data
visualisation is and is not. It will outline the different ingredients that make it such an interesting recipe and establish a foundation of understanding that will form a key reference for all
of the decision making you are faced with.
Three core principles of good visualisation design will be presented that offer guiding ideals to
help mould your convictions about distinguishing between effective and ineffective in data
visualisation.
You will also see how data visualisation sits alongside or overlaps with other related disciplines,
and some definitions about the use of language in this book will be established to ensure consistency in meaning across all chapters.
1.1 The Components of Understanding
To set the scene for what is about to follow, I think it is important to start this book with a
proposed definition for data visualisation (Figure 1.1). This definition offers a critical term of
reference because its components and their meaning will touch on every element of content
that follows in this book. Furthermore, as a subject that has many different proposed definitions, I believe it is worth clarifying my own view before going further:
Figure 1.1
A Definition for
Data Visualisation
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At first glance this might appear to be a surprisingly short definition: isn’t there more to data
visualisation than that, you might ask? Can nine words sufficiently articulate what has already
been introduced as an eminently complex and diverse discipline?
I have arrived at this after many years of iterations attempting to improve the elegance of my
definition. In the past I have tried to force too many words and too many clauses into one
statement, making it cumbersome and rather undermining its value. Over time, as I have developed greater clarity in my own convictions, I have in turn managed to establish greater clarity
about what I feel is the real essence of this subject. The definition above is, I believe, a succinct
and practically useful description of what the pursuit of visualisation is truly about. It is a definition that largely informs the contents of this book. Each chapter will aim to enlighten you
about different aspects of the roles of and relationships between each component expressed.
Let me introduce and briefly examine each of these one by one, explaining where and how they
will be discussed in the book.
Firstly, data, our critical raw material. It might appear a formality to mention data in the definition for, after all, we are talking about data visualisation as opposed to, let’s say, cheese
visualisation (though visualisation of data using cheese has happened, see Figure 1.2), but it
needs to be made clear the core role that data has in the design process. Without data there is
no visualisation; indeed there is no need for one. Data plays the fundamental role in this work,
so you will need to give it your undivided attention and respect. You will discover in Chapter
4 the importance of developing an intimacy with your data to acquaint yourself with its physical properties, its meaning and its potential qualities.
Figure 1.2
Per Capita
Cheese
Consumption in
the US
Data is names, amounts, groups, statistical values, dates, comments, locations. Data is textual
and numeric in format, typically held in datasets in table form, with rows of records and columns of different variables.
This tabular form of data is what we will be considering as the raw form of data. Through tables,
we can look at the values contained to precisely read them as individual data points. We can
look up values quite efficiently, scanning across many variables for the different records held.
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Defining Data Visualisation?
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However, we cannot easily establish the comparative size and relationship between multiple
data points. Our eyes and mind are not equipped to translate easily the textual and numeric
values into quantitative and qualitative meaning. We can look at the data but we cannot really
see it without the context of relationships that help us compare and contrast them effectively
with other values. To derive understanding from data we need to see it represented in a different, visual form. This is the act of data representation.
This word representation is deliberately positioned near the front of the definition because it
is the quintessential activity of data visualisation design. Representation concerns the
choices made about the form in which your data will be visually portrayed: in lay terms,
what chart or charts you will use to exploit the brain’s visual perception capabilities most
effectively.
When data visualisers create a visualisation they are representing the data they wish to show
visually through combinations of marks and attributes. Marks are points, lines and areas.
Attributes are the appearance properties of these marks, such as the size, colour and position.
The recipe of these marks and their attributes, along with other components of apparatus, such
as axes and gridlines, form the anatomy of a chart.
In Chapter 6 you will gain a deeper and more sophisticated appreciation of the range of different charts that are in common usage today, broadening your visual vocabulary. These charts
will vary in complexity and composition, with each capable of accommodating different types
of data and portraying different angles of analysis. You will learn about the key ingredients that
shape your data representation decisions, explaining the factors that distinguish the effective
from the ineffective choices.
Beyond representation choices, the presentation of data concerns all the other visible design
decisions that make up the overall visualisation anatomy. This includes choices about the possible applications of interactivity, features of annotation, colour usage and the composition of
your work. During the early stages of learning this subject it is sensible to partition your thinking about these matters, treating them as isolated design layers. This will aid your initial critical
thinking. Chapters 7–10 will explore each of these layers in depth, profiling the options available and the factors that influence your decisions.
However, as you gain in experience, the interrelated nature of visualisation will become much
more apparent and you will see how the overall design anatomy is entirely connected. For
instance, the selection of a chart type intrinsically leads to decisions about the space and place
it will occupy; an interactive control may be included to reveal an annotated caption; for any
design property to be even visible to the eye it must possess a colour that is different from that
of its background.
The goal expressed in this definition states that data visualisation is about facilitating
understanding. This is very important and some extra time is required to emphasise why it
is such an influential component in our thinking. You might think you know what understanding means, but when you peel back the surface you realise there are many subtleties that need
to be acknowledged about this term and their impact on your data visualisation choices.
Understanding ‘understanding’ (still with me?) in the context of data visualisation is of elementary significance.
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FOUNDATIONS
When consuming a visualisation, the viewer will go
These are not just synonyms for the
through a process of understanding involving three
same word, rather they carry important
stages: perceiving, interpreting and comprehending
distinctions that need appreciating. As
(Figure 1.3). Each stage is dependent on the previous
you will see throughout this book, the
one and in your role as a data visualiser you will have
subtleties and semantics of language
influence but not full control over these. You are
in data visualisation will be a recurring
largely at the mercy of the viewer – what they know
concern.
and do not know, what they are interested in knowing
and what might be meaningful to them – and this
introduces many variables outside of your control:
where your control diminishes the influence and reliance on the viewer increases. Achieving an
outcome of understanding is therefore a collective responsibility between visualiser and viewer.
Figure 1.3
The Three
Stages of
Understanding
Let’s look at the characteristics of the different stages that form the process of understanding to
help explain their respective differences and mutual dependencies.
Firstly, perceiving. This concerns the act of simply being able to read a chart. What is the chart
showing you? How easily can you get a sense of the values of the data being portrayed?
•• Where are the largest, middle-sized and smallest values?
•• What proportion of the total does that value hold?
•• How do these values compare in ranking terms?
•• To which other values does this have a connected relationship?
The notion of understanding here concerns our attempts as viewers to efficiently decode the
representations of the data (the shapes, the sizes and the colours) as displayed through a chart,
and then convert them into perceived values: estimates of quantities and their relationships to
other values.
Interpreting is the next stage of understanding following on from perceiving. Having read the
charts the viewer now seeks to convert these perceived values into some form of meaning:
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•• Is it good to be big or better to be small?
•• What does it mean to go up or go down?
•• Is that relationship meaningful or insignificant?
•• Is the decline of that category especially surprising?
The viewer’s ability to form such interpretations is influenced by their pre-existing knowledge about the portrayed subject and their capacity to utilise that knowledge to frame the
implications of what has been read. Where a viewer does not possess that knowledge it may
be that the visualiser has to address this deficit. They will need to make suitable design
choices that help to make clear what meaning can or should be drawn from the display of
data. Captions, headlines, colours and other annotated devices, in particular, can all be
used to achieve this.
Comprehending involves reasoning the consequence of the perceiving and interpreting stages to
arrive at a personal reflection of what all this means to them, the viewer. How does this information make a difference to what was known about the subject previously?
•• Why is this relevant? What wants or needs does it serve?
•• Has it confirmed what I knew or possibly suspected beforehand or enlightened me with new
knowledge?
•• Has this experience impacted me in an emotional way or left me feeling somewhat indifferent as a consequence?
•• Does the context of what understanding I have acquired lead me to take action – such as
make a decision or fundamentally change my behaviour – or do I simply have an extra grain
of knowledge the consequence of which may not materialise until much later?
Over the page is a simple demonstration to further illustrate this process of understanding. In
this example I play the role of a viewer working with a sample isolated chart (Figure 1.4). As
you will learn throughout the design chapters, a chart would not normally just exist floating
in isolation like this one does, but it will serve a purpose for this demonstration.
Figure 1.4 shows a clustered bar chart that presents a breakdown of the career statistics for the
footballer Lionel Messi during his career with FC Barcelona.
The process commences with perceiving the chart. I begin by establishing what chart type is
being used. I am familiar with this clustered bar chart approach and so I quickly feel at ease
with the prospect of reading its display: there is no learning for me to have to go through on
this occasion, which is not always the case as we will see.
I can quickly assimilate what the axes are showing by examining the labels along the x- and
y-axes and by taking the assistance provided by colour legend at the top. I move on to scanning, detecting and observing the general physical properties of the data being represented. The
eyes and brain are working in harmony, conducting this activity quite instinctively without
awareness or delay, noting the most prominent features of variation in the attributes of size,
shape, colour and position.
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Figure 1.4
Demonstrating
the
Process of
Understanding
I look across the entire chart, identifying the big, small and medium values (these are known
as stepped magnitude judgements), and form an overall sense of the general value rankings
(global comparison judgements). I am instinctively drawn to the dominant bars towards the
middle/right of the chart, especially as I know this side of the chart concerns the most recent
career performances. I can determine that the purple bar – showing goals – has been rising
pretty much year-on-year towards a peak in 2011/12 and then there is a dip before recovery in
his most recent season.
My visual system is now working hard to decode these properties into estimations of quantities
(amounts of things) and relationships (how different things compare with each other). I focus
on judging the absolute magnitudes of individual bars (one bar at a time). The assistance
offered by the chart apparatus, such as the vertical axis (or y- axis) values and the inclusion of
gridlines, is helping me more quickly estimate the quantities with greater assurance of accuracy,
such as discovering that the highest number of goals scored was around 73.
I then look to conduct some relative higher/lower comparisons. In comparing the games and
goals pairings I can see that three out of the last four years have seen the purple bar higher than
the blue bar, in contrast to all the rest. Finally I look to establish proportional relationships
between neighbouring bars, i.e. by how much larger one is compared with the next. In 2006/07
I can see the blue bar is more than twice as tall as the purple one, whereas in 2011/12 the purple
bar is about 15% taller.
By reading this chart I now have a good appreciation of the quantities displayed and some
sense of the relationship between the two measures, games and goals.
The second part of the understanding process is interpreting. In reality, it is not so consciously
consecutive or delayed in relationship to the perceiving stage but you cannot get here without having already done the perceiving. Interpreting, as you will recall, is about converting
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Defining Data Visualisation?
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perceived ‘reading’ into meaning. Interpreting is essentially about orientating your assessment of what you’ve read against what you know about the subject.
As I mentioned earlier, often a data visualiser will choose to – or have the opportunity to – share
such insights via captions, chart overlays or summary headlines. As you will learn in Chapter 3,
the visualisations that present this type of interpretation assistance are commonly described as
offering an ‘explanatory’ experience. In this particular demonstration it is an example of an
‘exhibitory’ experience, characterised by the absence of any explanatory features. It relies on the
viewer to handle the demands of interpretation without any assistance.
As you will read about later, many factors influence how well different viewers will be able
to interpret a visualisation. Some of the most critical include the level of interest shown
towards the subject matter, its relevance and the general inclination, in that moment, of a
viewer to want to read about that subject through a visualisation. It is also influenced by the
knowledge held about a subject or the capacity to derive meaning from a subject even if a
knowledge gap exists.
Returning to the sample chart, in order to translate the quantities and relationships I extracted
from the perceiving stage into meaning, I am effectively converting the reading of value sizes
into notions of good or bad and comparative relationships into worse than or better than etc. To
interpret the meaning of this data about Lionel Messi I can tap into my passion for and knowledge of football. I know that for a player to score over 25 goals in a season is very good. To score
over 35 is exceptional. To score over 70 goals is frankly preposterous, especially at the highest
level of the game (you might find plenty of players achieving these statistics playing for the
Dog and Duck pub team, but these numbers have been achieved for Barcelona in La Liga, the
Champions League and other domestic cup competitions). I know from watching the sport,
and poring over statistics like this for 30 years, that it is very rare for a player to score remotely
close to a ratio of one goal per game played. Those purple bars that exceed the height of the
blue bars are therefore remarkable. Beyond the information presented in the chart I bring
knowledge about the periods when different managers were in charge of Barcelona, how they
played the game, and how some organised their teams entirely around Messi’s talents. I know
which other players were teammates across different seasons and who might have assisted or
hindered his achievements. I also know his age and can mentally compare his achievements
with the traditional football career arcs that will normally show a steady rise, peak, plateau, and
then decline.
Therefore, in this example, I am not just interested in the subject but can bring a lot of knowledge
to aid me in interpreting this analysis. That helps me understand a lot more about what this data
means. For other people they might be passingly interested in football and know how to read
what is being presente…
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