methods in data visualization a guide for the energy professional.pdf

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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



16

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.



19

Appendix I

Ware (Data Types Matrix)



20

Appendix II

Illinksy and Steele Data Visualization Encoding Guidelines:



21

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



22

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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



24

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



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.

methods in data visualization a guide for the energy professional.pdf

Documents