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METHODS IN DATA VISUALIZATION: A guide for the Energy professional John Maxwell and Natalie Ballew
August 2013
INTRODUCTION
The purpose of this paper is to examine the field of information visualization, and define the ways and extent to
which an Energy and Earth Resource (EER) professional would benefit from imploring the effective methods of
presenting data visually. As issues of natural resources and economics become more complex in their interactions
and outcomes, simple data tables will no longer suffice to communicate concerns or conclusions. The ability to
take raw data and translate it into a meaningful argument is the true test of a professional whose job it is to
provide decision support. EER professionals work directly with decision makers; knowing how to frame issues and
evidence with a strong link to data is an extremely valuable skill to bring to fact‐based decision making. Exploring
how to use data to “communicate a concern, rather than just to show data” (Lima) can prove useful to an EER
professional.
The visual display of information is not a new idea. Hieroglyphics and cave drawings were among the first
examples, packing descriptions, stories, and knowledge into simple, easily understood drawings. Astronomers Carl
Sagan and Frank Drake created a graphic to communicate across all forms of intelligent life that was attached to
the Pioneer spacecraft in 1972 (Figure 1). While it is unknown whether other forms of life would understand the
graphic, the design elements within are simple, using line, proportion, and proximity to describe the layout of the
solar system, relative size of the spacecraft to a human being, and the hydrogen atom.
Figure 1 NASA image of Pioneer 10 plaque, 1972
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An effective visual informational display can squeeze multiple levels of information into a single graphical
representation that is easier to understand than spreadsheets of data or a long string of words. As Galileo stated in
1610 (via Edward Tufte’s Beautiful Evidence), “…the disputes which for so many generations have vexed…are
destroyed by visible certainty, and we are liberated from wordy arguments.” Advanced computing power and the
exponential expansion
of
available
data
makes
the
idea
of
“visible
certainty”
a much
more
powerful
concept
than
“wordy arguments”. An observation unaccompanied by visual evidence is not readily welcomed these days, so the
ability to harness the data and turn it into a more tangible piece of information has great strength in
communicating concerns.
EER has a vast landscape of available data that lend well to visualization. From crude oil spot prices to
stratigraphic columns to groundwater model outputs, analysis of data and systems require a keen eye and a grasp
of the basics of visualization theory. This paper will cover several concepts from authors in visualization
techniques, and foundational principles and guidelines for best data visualization practices. The next few
paragraphs will cover introductions to these concepts. Part II of the paper will cover two specific EER topics as
examples of how to apply some of these principles. Part III frames the importance to the EER professional to have
data visualization skills, and will hopefully encourage deep thought about how other data should be represented
visually.
PART I: KEY CONCEPTS, PRINCIPLES, AND GUIDELINES TO DATA VISUALIZATION
To begin thinking about data and its connection to energy and earth resources fields, the following two quotes
illustrate how data can be thought of as a natural element in today’s world.
“Information gently
but
relentlessly
drizzles
down
on
us
in
an
invisible,
impalpable
electric
rain.”
– Hans Christian von Baeyer, Information: The New Language of Science
“Today we live invested with an electric information environment that is quite as imperceptible
to us as water is to a fish.”
–Marshall McLuhan, Counterblast
The quotes (also cited in Visual Complexity: Mapping Patterns of Information by Manuel Lima) spark thinking on
the ubiquitous nature of data and the necessity and importance of data visualization.
Data is integrated into aspects of everyday life. Humans are constantly gathering a wealth of new
information from social interactions, nature, the surrounding environment, and technology. The ability to sort
through a large amount of data, provide a conduit for the information to pass through, and to frame and anchor an
argument to a logical conclusion at the end of the conduit is an emerging necessity for the EER professional.
Colin Ware, the Director of the Data Visualization Research Lab at the Center for Coastal and Ocean
Mapping at the University of New Hampshire, specializes in advanced data visualization and has a special interest
in applications of visualization for ocean mapping. Ware describes visualization in his book Information Visualization as an ”external artifact which supports decision‐making.” Visualizations can provide an ability to
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comprehend huge amounts of data, allow for the perception of emergent properties that were not anticipated,
facilitate hypothesis formation, and reveal qualities not only about the data itself, but also about the way it was
collected (Ware). However, poorly designed visualizations can distract from the benefits of a well‐executed
visualization. Details on how to avoid the downfalls of an excellent visualization will be discussed later.
Part of
what
makes
a good
visualization
is
how
the
image
is
physically
processed
by
the
brain
taking
in
information. Information that can deliver substantiated data for policy decisions is most useful when integrated
into a process that “…leverages the capabilities of the [human] visual system to move a huge amount of
information into the brain very quickly.”
Figure 2 The visualization process. (Ware)
As shown in Figure 2, the movement of information from its abstract data form into the brain’s visual
processing unit undergoes a process that shapes the data in visualization to allow for information to move from
point A (abstract data) to point B (brain) in an explanatory framework (Illinksy). The best visualizations incorporate
methods of good design (an art‐intensive angle) and solid scientific, statistical, and mathematical methods (a
science‐intensive angle). Taking strong points from each angle creates a feedback loop that takes raw data and
transforms or manipulates it to create a tangible map of patterns, connections, and structures out of intangible
evidence (Ware/Lima). Effectively combining the art and science aspects of visualization creates a clear and strong
path for data exploration, manipulation, and broader context and application. The EER professional should
differentiate themselves in the area between the data and the visual response elements of the reader’s brain.
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Making data easy to understand with direct causality and comparisons made will deliver high‐quality returns for
the concept and evidence that should be offered quickly and accurately to decision makers.
The fine line of science and art within data visualization is becoming thinner as computer processing
power progresses ad infinitum and delivers tools able to analyze multivariate problems in a more approachable
form. A
few
of
the
numerous
programs
available
will
be
discussed
in
Part
II.
Understanding
the
theories,
processes,
and opportunities available to translate raw data into decision‐making tools can prove ineffective and even
disastrous without holding up to a quality standard of basic design principles and guidelines associated with
making visual displays of information.
Best Practices: Visualization Creation Workflow In sync with the recent prevalence of large amounts of complex data, or “big data”, there has been an explosion of
literature in data visualization and best practices. A grasp of these best practices can merge with available tools to
create the best display of data. Most of this literature is derived from the work of Edward Tufte, who has written a
series of
books
on
data
presentation.
In
the
book
The Visual Display of Quantitative Information
Tufte
describes
what makes graphical excellence, and these principles are incorporated into the workflow presented here.
Graphics should be presented in a simple, yet multi‐dimensional way so that the viewer can focus on the
data and what can emerge from the data, rather than focus on the methods implored to create the graphic. The
first step to achieving graphical excellence is to adhere to the basic design principles and elements. This workflow
does not incorporate the basic design elements, but they include the following. Basic design principles are
achieved through use of the elements (also see Appendix I).
Design Elements
Line: Graphical
features
such
as
axes,
gridlines,
tick
marks,
etc.
should
be
minimized
to
let
the
data and important information shine through in any graphic.
Color: Our minds do not put an order on the colors of the rainbow, so it is more effective to use
shades of a color when depicting magnitudes or importance. Harsh or vibrant colors can distract
the eye from important information in the data; colors found in nature are often more pleasing.
Shape: Do not use overly caricaturized images to represent data as they will be distracting.
Simple shapes that serve multiple purposes (labels and data points, for example) are effective.
Texture: Texture can be implored to add to the information in shapes, but should be minimized
and used in a subtle manner.
Space: Space is very important to visualizations. A large amount of information in a small area
can allow
the
viewer
to
more
easily
compare
data
and
can
promote
connections
between
the
data; however, information that is too tightly fit can be difficult to read.
Form: Form is a three‐dimensional aspect that should be considered when making three‐
dimensional interactive visualizations. When making two‐dimensional visualizations, it is also
important to consider how a three‐dimensional object will be translated onto a two‐
dimensional plane.
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The workflow presenting in Figure 3 incorporates design functions, principles, elements, and guidelines outlined in
the various literature available on data visualization and graphical excellence.
Figure 3 Visualization Creation Workflow. *Design elements, described earlier, should also be used in creation.
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One of the most important elements of the data behind a graphic is the presence of more than two
variables. These types of displays have more depth and room for expansion than displays with two variables alone.
Multivariate displays include familiar graphics: maps, bar‐charts, scatterplots, line graphs, etc. More effective
displays include narrative graphics of time and space and relational graphics. Narrative graphics show data moving
over space
and
time
and
is
a great
way
to
incorporate
a larger
number
of
variables.
Tufte
uses
the
Charles
Joesph
Minard graphic of Napoleon’s 1812 Russia campaign as an example of a narrative graphic (Figure 4 below).
Figure 4 Minard's chart of Napoleon's 1812 Russia Campaign
This chart tells the story of Napoleons troops’ journey to Russia and back. This chart incorporates complexity that
includes six variables, including time, geographical location, army size, and temperature, in a subtle way. It tells a
story rather than just giving the data. This chart not only fulfills the six principles, but also fulfills the basic
principles of graphical excellence, giving the viewer the most amount of information, using the least amount of ink
(Tufte). Narrative graphics present quantitative and sometimes qualitative information and lead the viewer to
deduct a conclusion and explore further potentials about the topic presented.
A basic understanding of design and graphics principles allows full attention on data manipulation and
mastering available processing tools. For any EER professional, it is ideal to be able to take in a large amount of
data and present it in a way that is easy to digest so that further discussion on what story the data is telling can be
pursued. It is important to an EER professional to have this skill because much of the information in the EER field
covers multiple topics, such as water, finance, energy, and commodities. Reigning over interdisciplinary data
requires the knowledge of the best way to display the data, and final purpose and audience should also be
considered in the type of visualization used.
In the next section, we will explore the methods used with several different types of data representative
of the EER field: economic and energy data visualized using area charts, parallel sets, and excel graphs; a network
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analysis of bibliographic records to determine emergence and prevalence of a particular topic; and a tree map
visualization of groundwater model runs to view the effects of different pumping parameters. We will discuss the
purpose for selecting each method, and what works and what could be improved with each visualization.
PART II: EXAMPLE DATA SETS AND VISUALIZATIONS
ENERGY AND ECONOMIC DATA The practice of displaying economic data as well as energy data is, in itself, a very large sub‐category of
the data visualization field. The goal is to display these two types of data together and to determine the best way
to show a large number of country data over about a 30‐year time period. The use of time in this case becomes the
primary differentiator and trend type to make comparisons among the data.
The questions that the data seek to answer have to do with the ways in which energy and the economy
effect each
other
and
create
feedback
loops.
There
is
vigorous
debate
as
to
whether
the
greater
the
consumption
of energy results in greater economic growth or if the relationship works the vice versa, in which greater economic
growth results in greater energy use. This relationship and interaction among different types of primary energy
sources is the principle issue that John Maxwell’s thesis explores. Using the work of Carey King and his
investigation of “net energy measures” in the United States, the dataset is 44 countries over a time period from
1978 to 2010. The data visualization will explore different trends and comparisons between countries and the
differing causality relationships. The Tufte analytical criteria are the foundation for the way the data will be
displayed and explained.
There are several different programs that can be used to show this energy and economic information.
Here the data is displayed in Microsoft Excel, Tableau, a parallel sets program, and some exploratory steps into R.
All of these programs can be useful for creating the visualization of data that is required for the proper and clear
display of information.
The first data visualization is a simple chart representing the percentage of gross domestic product (GDP)
spent on energy and the percentage change in GDP over the time between 1978 and 2010. This chart was created
in Microsoft Excel and is a simple line chart. Putting data into Excel initially helps to look at a trend for these two
measurements over time. The default for this chart had blue and purple lines (Figure 5), which did not look good
enough to create a differentiation of the changes over time. Since color is not naturally ordered in the sense that
there is
a natural
rule
for
reds
and
blues
to
create
a hierarchy
(Appendix
II)
it
is
up
to
the
creator
to
determine
where there is a need to vary the color. The idea of color helps the reader to differentiate the points that the
author is trying to make.
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Figure 5 Initial Excel line graph
Figure 6 Revised Excel line graph
In Figure 6, the revised version of Figure 5, a title was added, the x‐axis was shifted, made less busy and larger. The
legend was deleted and title was added. The legend for the lines were added and labeled in the same color as the
lines. Avoiding defaults in Microsoft Excel has become nearly an iron‐clad rule. The labeling in this case is also put
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on top to anchor the fact that for the most part, the percentage of GDP spent on energy has been greater than the
percentage of the GDP which changes.
The next few figures were created in Tableau and are also part of an out‐of ‐the‐box visualization software
created explore and analyze data visually. Tableau uses drag‐and‐drop types of intuitive visualization creation.
Tableau works
very
well
for
spatial
information
and
can
help
to
create
maps
and
other
location
‐based
information.
This first figure (Figure 6) is part of the energy information and was one of the first attempts that the
creator took towards creating a visualization in Tableau. It is a very busy graph and many lessons have been
learned since this first attempt.
Figure 7 Tableau Visualization 1
The next few graphics were created after research in the data visualization field and taking a couple of
Tableau tutorials.
Figure 8 is an area chart representing the amount of expenditures spent on different types of energy
sources as well as the amount of energy consumed in energy units in the world between 1978 and 2010. The area
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chart uses different colors to represent the types of energy showed in the key to the right. This chart emphasizes
the amount the world spends on crude oil and how the amount of energy has not changed dramatically, while the
aggregate amount of expenditures has increased by nearly 400 percent.
Figure 8 Area Chart: Worldwide energy expenditures and energy consumption
Figures 10 and 11 are attempts to use a different color palette and match colors across space and time.
The bubble chart uses area and color to demonstrate the amount of GDP that each country spends on energy. The
scale at the top is the same in the two figures. The size of the bubbles and proximity is uses the Gestalt principles
of proximity. Arrangement of the countries is spaced to not overwhelm the reader (Lima). The following two
figures are examples of Gestalt principles in practice and how to classify different aspects of proximity and
arrangement.
Figure 9 Connectedness is a powerful grouping principle that is stronger than a) proximity, b) color, c) size, or d) shape.
Connectedness using smooth continuous lines is easier to understand than abrupt lines.
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Figure 10 World Map Energy %GDP
Figure 11 Bubble chart energy %GDP
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If all the large bubbles in Figure 11 were right on top of each other there would not be enough context for the
reader to gain much appreciation for what the data is expressing. The next level to this visualization would be for
the bubbles to be arranged in the order of the map. The map shows the countries across the world and how there
is a relationship between location and how the country is spending its GDP. This becomes relevant since oil is the
most traded
economy
and
the
amount
of
world
GDP
spent
on
crude
oil
was
the
highest
and
there
seems
to
be
a
correlation between the amount spent on oil and those countries that are producing oil.
Figure 12 is a visualization of a parallel set, which is a way to visualize multiple variables and their
relationship to each other. The pink box is what is called brushing and can show where the average values lay
within the data set. Parallel sets are a way to display large amounts of discrete pattern data. In this plot, there are
several dimensions, which are represented by each vertical line. The vertical line is a new dimension on which the
scale changes. This type of visualization can give insight into the effect of evaluating a set of data based on changes
within the dataset. The effect of color in this visualization offers a couple of benefits as there is a stratification that
carries along the lines throughout the dimensions of the values.
Figure 12 Parallel set
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Figure 13 Scatterplot made in R
Figure 13 is a basic scatter plot graph created in R and a formatted scatterplot matrix created with the
xdmv tool, which was also used to create the parallel set visualization. Scatter plots are an excellent way to present
discrete pattern data containing two dimensions, but can also be used to represent three dimensions when there
is variation in size or color within the scatterplot as depicted in Figure 14.
Figure 14 Three‐dimensional discrete data. The third dimension is given by a) point size, b) gray value, and c) phase of
oscillatory point motion. (Ware)
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BIBLIOGRAPHIC NETWORK and GROUNDWATER DATA In exploration of a topic or picking a question to research, it is useful to be aware of what kinds of research is
already going on in the area of interest. Exploration of bibliographic networks, which show the connectedness of
documents, is a way to set the stage for a particular research question and define the relevancy of a topic. An
example of
a bibliographic
network
here
was
created
using
groundwater
uncertainty.
There
are
multiple
informatics tools that analyze the existing network of publications in this topic; here Sci2 was used.
Sci2 has the ability to import numerous references from the Web of Science database and creates a
network visualization to explore the relevancy and connectedness of a topic. My broad search yielded more than
2,000 results in Web of Science; Figure 15 shows the resulting visualization. (Appendix IV is a step‐by‐step guide to
creating this visualization in Sci2.)
Figure 15 Sci2 network analysis
Links (edges) between circles (nodes) indicate occasions when one article referenced another one. The size and
color of the nodes, each representing an individual article/citation indicate the times that the article has been cited
by other sources that it is connected to. Ideally, a label showing either titles or journal topics would be more
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useful, but a limitation on knowledge to manipulate the final image prevented this and is being pursued further.
Within the program, however, a mouse hovering over each node and edge reveals which article is represented and
connected to another. These descriptors allow for comparison on the major clusters of citations.
From a design perspective, the way the visualization is initially created from the algorithm in Sci2 results
in nodes
that
are
all
the
same
size
and
color
with
thick
lines
outlining
each
node.
Alterations
to
the
design
can
be
made to incorporate design principles and make the network more visually appealing and easier to sift through for
the viewer. The layout of Figure 15 is one of several options in Sci2; this layout (GEM) creates clusters that help to
guide the visual reader.
Networks are a powerful tool to use especially when looking for emergent properties. Appendix III shows
fifteen other types and styles of network visualizations. Networks can go beyond the bibliographic realm and can
be used to explore interactions among multiple variables, exposing key relationships. From an EER perspective, a
telling network visualization that exposes a relationship between variables that had not been considered or well‐
understood before can be exceptionally useful in decision‐making situations.
After an initial exploration of the relevancy of a topic, it is time to explore that data available. In terms of
groundwater uncertainty, the dataset here is a group of groundwater simulation runs (10,256 to be exact). This is a
daunting amount of numbers to begin to wrap ones head around, so it is useful to be able to take the set piece‐
wise to get a feel for what attributes the data carries and what those pieces are doing. As with the Energy and
Economy data earlier, several runs of the data were put into Excel as a line graph to determine any trends (Figure
16). With large datasets it is useful to get a feel for what the general trends in the data are with a basic
visualization, like a bar chart, line graph, or scatter plot, to determine the next best step.
Figure 16 Water table levels from one data run.
400
500
600
700
800
900
1000
1100
1200
1300
1 2 3 4 5 6 7 8 9 10
F e e t
Year
Water Table
Levels
from
Groundwater
Availability
Model:
Barton Springs Aquifer, Zones 1‐11
Zone 1
Zone 10
Series3
Series4
Series5
Series6
Series7
Series20
Series21
Series22
Series23
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In this graph, a general trend in the change of the water table over a ten‐year period can be observed. Each line
represents a zone denoted within the groundwater model. In line with the Excel rule mentioned previously, all
Excel graph defaults were overridden. The color and weight of the line were changed to create a more visually
appealing and easy‐to‐read chart. Font size and location of numbers on the axes were changed. Horizontal
gridlines were
minimized
so
as
not
to
distract
from
the
data.
Figure
16
is
missing
descriptors
for
each
series,
although, these are unimportant in this particular process as there are still further steps to be made with the data
and this graph was simply used for an initial grasp on what the dataset contains.
The line graph in Figure 16 includes only one out of thousands of model runs. The next question in this
process is if there is any variation among the numerous data runs. To explore this, a treemap was created for a
100‐count handful of the data. Treemaps are based in hierarchical data as represented by Figure 17.
Figure 17 a) a treemap representation of hierarchical data. Areas represent the amount of data stored in the tree data
structure. b) the same tree structure, represented using a conventional node‐link diagram (Ware).
The hierarchical structure was not well‐defined in the groundwater data yet, but an initial image (Figure 18)
determined that
there
was
variation
in
each
of
the
runs.
Figure 18 Initial Tree Map
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Significant data manipulation needs to be done on the dataset still, but this treemap is a start to showing how this
type of visualization can be used. Here, only one month of data for each model run is used, but for a truly effective
treemap, this would need to use each months run, or an average of some sort. The important aspect in this image
is the use of color, mild and not overwhelming, and the tone. With color variation, it is best to use different tones
of one
color
since
the
human
mind
does
not
assign
a hierarchy
to
color,
with
exception
to
reds
and
green,
which
have a general connotation associated with them. The proximity of the boxes within the treemap allow for easy
comparison. This image still lacks labels, but the software has a hover feature like in Sci2.
With this particular dataset, much more statistical analysis needs to be completed, as mentioned, but
these few graphics give an initial idea of where to go next in data analysis. In the future, a network analysis and
visualization would be ideal for this dataset. Being able to view links of the groundwater parameters present in the
data runs, particularly the link between spring flow, water table level, and pumping, can be useful in real‐life
decision scenarios in determining how to best manage groundwater. If a valid and effective visualization is created
with this dataset, the process can then be replicated for other regions with pressing water issues.
PART III: FURTHER THOUGHTS
This paper has covered a small selection of the vast amounts of information that exists in regards to data
visualization. There are a multitude of ways to design and craft data visualization. Over the duration of the
independent study course, we have seen several platitudes that are called the “data visualization guidelines” and
we have quoted and displayed several of them in the paper in conjunction with the Visualization Creation
Workflow. Figure
19
below,
the
Source
Trinity
(Illinsky
and
Steele),
displays
the
connection
between
data,
design,
and viewer and can aid in how design should be approached from a broader perspective.
Figure 19 The Source Trinity
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This figure shows how all three participants of a visualization, reader/data/designer, should be viewed. The
interactions between the reader and designer and between reader and data are the two to focus on within an EER
context. One of the functions of a visualization is to make a point to the reader. If the visualization is trying to
demonstrate positive judgement, strategies should be used in a way that will inform the reader and will “…[aim]
for a neutral
presentation
of
the
facts
in
such
a way
that
will
educate
the
reader…”
(Illinksy/Steele).
The
Source
Trinity can inform the average person with the critical information that the visualization is trying to display and not
with another dimension to the data which seeks to convince the reader of a specific type of view. In this sense, the
designer is not inserting themselves into the visualization to make an editorial judgment with the data. This is a
more formal role for visualizations in an academic or information‐providing role.
The second and perhaps more important relationship in the Source Trinity is the reader‐designer
relationship. This is where the designer introduces a normative point to the visualization and is clearly advocating a
position with the design elements which they have chosen and to persuade the reader of the information to share
the point of view with the designer. In this situation, the designer is taking the data that has been manipulated and
transformed into a visualization where they are taking a viewpoint and applying that slant to the visualization. This
is especially important to understand if the visualization is in a policy or consulting setting.
Understanding the relationship between data visualization and decision‐making is the chief concern of
this paper. The EER professional has a duty to recognizing that the role of decision support through visual
quantitative and qualitative analysis to show what must be done within a system, company, nation or the world
and use data visualization to craft that analysis in the best way possible.
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Appendix I
Ware (Data Types Matrix)
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Appendix II
Illinksy and Steele Data Visualization Encoding Guidelines:
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Appendix III 15 Types and Styles of Network Visualization
Arc Diagram Area Grouping Centralized
BurstCentralized Ring
Circled Globe Circularities Elliptical implosion
Flow chart Organic Rhizome Radial Convergence Radial Implosion
Ramification Scaling Circles Segmented and
Radial ConvergenceSphere
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Sources (left to right, top to bottom)
1. https://www.google.com/search?q=arc+diagram&source=lnms&tbm=isch&sa=X&ei=Tjr5Ud2XIZKiqwHp14D4BA&ved=0CAkQ_AUoAQ&bi
w=1920&bih=995#facrc=_&imgdii=_&imgrc=LWZhD‐q801x0iM%3A%3BmpcLhHiGSkGi1M%3Bhttp%253A%252F%252Fwww.e‐
rna.org%252Fr‐chie%252Fimages%252Foverlap.png%3Bhttp%253A%252F%252Fwww.e‐rna.org%252Fr‐chie%252F%3B930%3B557
2. http://www.computationalgroup.com/tigertiger/cb/index.html
3. http://www.isi.edu/division7/publication_files/heuristics.pdf
4. http://d3.do/en/wp‐content/uploads/2011/10/circle.jpg
5. http://www.telegeography.com/telecom‐maps/
6. http://musicovery.com/
7. http://www.visualcomplexity.com/vc/project_details.cfm?id=339&index=339&domain=
8. http://www.visualcomplexity.com/vc/project_details.cfm?id=72&index=72&domain=
9. https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&docid=ZhaID_NefM5giM&tbnid=8ak99VobCTiefM:&v
ed=0CAMQjhw&url=http%3A%2F%2Fdcook020.grads.digitalodu.com%2Fblog%2F%3Fp%3D37&ei=UkH5UbqpLYvoqAGs8oDYBA&bvm=bv.
49967636,d.aWM&psig=AFQjCNHth_tHiFHZgqUxJaW7rZ4YROKXNA&ust=1375376068803955
10. http://www.visualcomplexity.com/vc/project_details.cfm?id=278&index=278&domain=
11. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1103573
12. http://www.schmuhl.org/graphopt/
13. http://pages.cs.wisc.edu/~pavlo/papers/graphdrawing06.pdf
14. http://www.visualcomplexity.com/vc/project_details.cfm?id=142&index=142&domain=
15. http://moebio.com/spheres/english.html
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Appendix IV
Science to Science (Sci2) Tool Tutorial
Download the latest version of the Sci2 tool: https://sci2.cns.iu.edu/user/welcome.php
Sci2 Manual:
http://wiki.cns.iu.edu/display/SCI2TUTORIAL/Science+of+Science+%28Sci2%29+Tool+Manual?from=1oMh
Access the Web of Science database through the UT Library system:
http://www.lib.utexas.edu/indexes/titles.php?let=W
Collect Records from Web of Science
In Web of Science, conduct a search on your topic of choice. Once you have your results, follow these steps:
1. At the bottom of the search results page is a light gray box entitled “Output Records.” You can export 500
records at a time. You can also select the records you want to download.
Select “Records” and enter “1” to “500” in the step 1.
Select “Full Record” and “Cited References” in step 2.
Select “Save to other Reference Software” in the dropdown in step 3. “Save as Tab‐delimited
(Windows/Mac)” also works.
Click “Save”. Save the exported records to a file folder, and name appropriately (by search topic). If
you will be exporting in multiple batches, it is helpful to name your files appropriately (_a, _b or _1,
_2) because you will compile all records in the next step.
Repeat the process until all records have been exported.
2. Open the first exported file. At the beginning of the file you will see “FN Thomson Reuters Web of
Knowledge VR 1.0” and at the end you will see “ER EF”. These notations signify to Sci2 the start and end of
the records. If these exist more than once in the records used in Sci2, only the first portion will be
analyzed. In the first file, delete the “ER EF” at the end. Save text file. This will be your compilation file.
3. Open the second exported file. Delete “FN Thomson Reuters Web of Knowledge VR 1.0” at the beginning
and delete
“ER
EF”
at
the
end.
Copy
the
remaining
text
and
paste
into
the
compilation
file.
Repeat
this
for
all remaining files except for the last one.
4. Open the last exported file. Only delete “FN Thomson Reuters Web of Knowledge VR 1.0” from the
beginning. Keep “ER EF” to signify then end of records. Copy and paste into compilation file. Save.
5. Rename the compilation file to have the extension “.isi”. ISI format is the output format from the Web of
Science database that contains author, citation, and full abstract information.
6. You are now ready to being analysis with Sci2!
Sci2: Creating a Co‐citation Network
The Sci2 menu is arranged left to right to go with the workflow. Files can be loaded then can be prepared,
preprocessed, analyzed, and visualized. The Console window documents operations performed on the data. The
Schedule window
indicates
the
progress
of
your
operations
shows
what
operations
have
been
performed.
The
Data Manager tab shows the evolution of your data after you have processed it.
1. File > Load.
Select .isi file
“Load” dialogue box will appear. Select “ISI flat format”.
2. Data Preparation > Extract Directed Network
Source Column > Cited References
Target Column > Cite Me As
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Extract
This creates a directed network by placing a directed edge between the values in a given column to
the values of a different column
3. Data Preparation > Extract Bibliographic Coupling Network
4. Analysis > Networks > Network Analysis Toolkit (NAT)
This performs a basic analysis on the network, calculating clusters, self ‐loops, parallel edges,
number of
nodes,
number
of
edges,
and
density
of
a network
(in
the
Console
window).
This
allows
you to get a feel for the network and find any errors that may be present in the data.
5. Select “Bibliographic Coupling Similarity Network” in the Data Manager window.
6. Preprocessing > Networks > Extract Edges Above or Below Value
In dialogue box, enter “4” in “Extract from this number” box. This algorithm gets rid of any nodes
that are outside of the range you are interested in.
7. With the new edges selected, Preprocessing > Delete Isolates
With “With isolates removed” selected, Visualization > Networks > GUESS
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WORKS CITED
Illinsky, Noah and Steele, Julie. Designing Data Visualizations : Representing Informational Relationships.
Sebastopol: O'Reilly Media, 2011. Ebook Library. Web. 12 Jul. 2013.
Lima, Manuel. Visual Complexity: Mapping patterns of information. New York: Princeton Architectural Press, 2011.
Tufte, Edward
R.
Beautiful Evidence.
Cheshire:
Graphics
Press,
2006.
Tufte, Edward R. The Visual Display of Quantitative Information (2nd ed.). Cheshire: Graphics Press, 1983.
Ware, Colin. Information Visualization : Perception for Design. Burlington: Elsevier Science, 2012. Ebook Library.
Web. 3 Jul. 2013.
Yau, Nathan. Visualize This: the FlowingData guide to design, visualization, and statistics. Indianapolis: Wiley Pub.,
2011.
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