Computer Science HCI and UI – Assignment

You have received the request to upgrade the web service you designed earlier, including the addition of collaborative tools and functionalities, social aspects, and enhanced reports. Along with these additions, the government organization asked for changes to lower human errors and increase security without harming user experiences. For this assessment, complete the following:

1. Create your project plan to address these changes/upgrades.

2. Include the UX/UI related requirement analysis, design, evaluation, implementation, deployment, and acceptance test processes.

3. List and describe what and how the web service will be changed in terms of UI/UX perspective.

4. List and describe what and how the web service will be changed in terms of IUI/IUX perspective, especially with wearable devices and augmented reality.

Need 8-10 page paper in APA format with minimum of 8 peer-reviewed citations. Must include an introduction and conclusion. No AI work.

CHAPTER

•

•· The real voyage of discovery consists not in seeking new ,,
landscapes but in having new eyes

CHAPTER OUTLINE
16.1 Introduction

16.2 Tasks in Data Visualization

16.3 Visualization by

Data Type

16.4 Challenges for Data Visualization

M arcel Proust

551

552 Chapter 16 Data Visua lization

16. 1 Introduction

Today’s users are routinely engaging with larger and more complex volumes of
data than ever before-and not just for professional situations as part of their
jobs but also for personal and recreation purposes. For example, while it is not
surprising that a business ana lyst has to process millions of sales records to
determine a valid marketing strategy, even casual users at home need to
navigate thousands of movies to find the perfect entertainment for a night in,
browse hundreds of social media updates daily to keep abreast of tl1eir circle of
friends, or scan through thousands of product reviews to find the right toaster
to buy. Regardless of application, the medium chosen to represent the data
governs the ease with which a person can perform a specific task using the
information. This means that successful designers should adapt the data
presentation based on what the user needs to do.

The best medium for many tasks and types of data is a visual representation
after all, a picture is supposedly worth a thousand words! For example, a
building blueprin t, a geographic map, and a digital photograph are generally
best presented as 2-D pictures on the computer screen and 11ot as a list of coor
dinates, colors, and shapes. Similarly, while text is the optimal presentation to
convey a single number (such as the cost of a product, the distance to the super
market, or an approval percentage), a visual presentation such as a bar chart, a
line graph, or a scatterplot is often a better choice when conveying multiple
related points in a dataset, such as average reviews for mu ltiple products, stock
values over time, or the relation between income and years of experience in a
job. This idea of data-driven pictures is called visualization and is defined as the
graphical representation of data to amplify cognition (Card, 2012; Ware, 2013).
Visualization draws upon the massive bandwidth of our visua l system to
essentia lly allovv people to “use vision to think” and dates as far back as Wil
liam Playfair’s line graphs and bar charts from 1786, Charles Minard’s flow
maps from 1869, Florence Nightingale’s rose diagrams from 1857, and John
Snow’s cholera outbreak maps from 1854 (Fig. 16.6) (Tufte, 2001; Friendly,
2006).

In terms of Norman’s gulfs of action, a macro-HCI theory describing the dif
ference between a user’s mental model and an interactive system’s state (Chap
ter 3), visualization minimizes the gu lf of eva luation because a well-designed
graphica l representation is optimized for many perceptual tasks. Based on th is
concept, Section 16.2 first presents the typical tasks that people tend to conduct
using visual analysis methods. Section 16.3 then reviews typical data types and
examples of common visualiza tion techniques designed for them. This example
based framev.rork is necessary since the visualization discipline is young and still
lacks specific macro-HCI theories for selecting the optimal visual representation

16.2 Tasks in Data Visualization 553

See also:

Chapter 7, Direct Manipu lat ion and lmmersive Env ironmen ts

Chapter 8, Fluid Navigation

Chapter 10, Devices

Chapter 11, Communication and Col laboration

Chapter 15, Information Search

that will minimize the gulf of evaluation for a dataset and task. Instead, visual
ization design is often empirical in nature.

Compared to the static visualizations of old, computer-based visualization
has the added benefit of being interactive, which opens up vast opportunities
beyond the static representations printed on paper. Similar to the above discus
sion, an effective interaction method for a visualization minimizes the gulf of
execution-the difference between user intention and system actions-by
enabling the user to easily carry out the task. However, interaction for visualiza
tion differs irt many ways from typical interfaces and user applications.

Finally, much has happened in the more than two decades since the visual
ization field was established at the end of the last century: Computers have
become faster and evolved into new forms ranging from smartphones and tab
lets to wall displays and tabletops, our society is awash in a deluge of data
drawn from every discipline and domain, and a new generation of mobile and
ubiquitous computing is turning our world into one where computing has dis
appeared into the fabric of everyday life (Dourish and Bell, 2011). This means
that many of the foundational principles that visualization researchers tradi
tionally have held to be true no longer are. Section 16.4 reviews the challenges
facing both researchers and practitioners in data visualization.

16.2 Tasks in Data Visualization

Why do people want to interact ,..,ith data? A pragmatic designer will start with the
tasks that users want to perform in order to decide how to support those using
interactive visual representations. Determining a standard set of such data analysis
tasks has been an active area of research in the visualization community for the past
two decades. One of the formative efforts i.t1 this venture was Shi1eiderman’s visual
infor,nation-seeking rnantra from 1996: “overview first, zoom and filter, then details
on demand,” which still accurately captures the high-level sensemaking process
(Klein, 2006) that users engage m when mteracting with data. Amar et al. (2005)

554 Chapter 16 Data Visua lization

approached the problem from the other direction (i.e. from the bottom up instead
of from the top down), deriving 10 low-level analytic tasks that people commonly
perform: retrieve value, filter, compute derived value, find extremum , sort, deter
mine range, characterize distribution, fu1d anomalies, cluster, and correlate. Mun
zner (2014) since filled in the gap between high-level sensemaking and low-level
analytic tasks using a typology of abstract visualization tasks, which focuses on the
why, what, and how of engaging with data at all abstraction levels.

While these efforts lay the necessary theoretical foundation for how users
engage with da ta, they do not provide concrete guidance for designers looking
to build novel visualization tools. To achieve this, this section presents a taxon
omy of interactive dynamics that combine the analysis task with the practical
operations that users need in their visualization tools (Heer and Shneiderman,
2012). The taxonomy consists of 12 task types grouped into three high-level
categories, as shown in Box 16.1: (1) data and view specification (visualize, filter,
sort, and derive); (2) view manipulation (select, navigate, coordinate, a1ld
organize); and (3) process and provenance (record, anno tate, share, and guide).
These thiee categories incorporate the critical tasks that enable iterative visual

BOX 16.1
Twelve task types fo r visua lization organized into three high-level catego ries
(adapted from Heer and Shneiderman (2012)).

Task Categories

Data and view specification

View manipulation

Process and provenance

Task Types

Visualize data by choosing visual encodings

Filter out data to focus on releva nt items

Sort items to expose patterns

Derive va lues of mode ls from source data

Select items to highlig ht, fi lter, or man ipulate

Navigate to examine high-level patterns and
low-leve l detail

Coordinate views for linked exp lorat ion

Organize mu ltiple windows and wor kspaces

Record analysis histories for revisitation,
review, and sharing

Annotate patterns to doc ument findings

Share views and annota t ions to enab le
collaboration

Guide users through ana lysis tasks or stories

16.2 Tasks in Data Visualization 555

analysis, including visualization creation, interactive querying, multi-view
coordination, history, and collaboration.

For each of the 12 task types described here, examples are given that sho\,v
case the idea using real-world and predominantly commercial visualization
tools. While this is by no means an exhaustive survey, these examples give prac
tical and operational evidence of how designers can model their interfaces to
support sensemaking of large-scale and complex data.

16.2. 1 Data and view specification
Core functionality of any data visualization tool includes basic operations to
visualize data using a visual representation, to filt er out unrelated information,
and to sort information to expose patterns. Users also need to derive new data
from the input data, such as normalized values, statistical summaries, and
aggregates. These four task types can be explained as follows:

• Visualize data by choosing visual encodings. Not surprisingly, selecting a visual
encoding for a particular dataset is the most fundamental operation for a
visualization tool. A common approach in practical visualization tools is to
simply provide a palette of available charts, allowing users to easily pick the
chart most appropriate for their data (Fig. 16.1). Microsoft Excel and Tableau

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automatica lly highlight the suitab le charts that can be used for the selected data.

556 Chapter 16 Data Visua lization

both provide such palettes; in addition, Tab leau also has a novel feature called
“show me” that automatically selects the most appropriate visualization
given the structure of the data (Mackinlay et al., 2007).

• Filter out data to focus on relevant iten1s. While the overview of a dataset is often
important in orienting the user in a visualization, eliminating unrelated in
formation from the view is critical as the user starts to investigate the da ta in
detail. Several methods for filtering exist, such as direct ly lassoing important
objects (Choi et al., 2015) or selecting intervals and values on data dimensions
using dynamic queries. Fig. 15.10 shows a hotel search interface on the Kayak
trave l website with an integra ted filter interface. The interface allows for dy
namical ly querying the hotels that match filtering criteria by changing range
sliders for price intervals and selecting features by checking boxes (review
scores, free breakfast, free internet, etc.). The results update dynamically as
the filters are changed.

• Sort items to expose patterns. Ordering da ta items according to some dimen
sion, such as age, income, or price, is vital in exposing hidden patterns in the
data. Sorting a list of items is often easily performed by clicking the header
category; toggling reverses the order.

• Derive values of 1nodels fro1n source data. Original datasets can often be aug
mented with data computed from the original, such as statistics (e.g., mean,
median), transformatio11s, and even powerful data mitung metl1ods. In fact,
calculating derived data as part of an interactive system with a user in the
loop is a nascent but growing research area called visual analytics (Keim et al.,
2008), where compu tational methods work in synergy with the user.

16.2.2 View manipulation
Much of the value of visualiza tion comes from being able to manipulate the
view on the screen, including tl1e ability to select items or regions, to navigate the
viewport’s position on a large visualization, to coordinate multip le views so that
data can be seen from multiple perspectives, and to organize the resulting
dashboards and workspaces.

• Select iterns to highlight, filter, or rnanipulate. Pointing to an item or region of
interest is common in everyday commurucation because it indicates the subject
of conversation and action. In a visuali zation tool, common forms of selection
include clicks (by mouse or by touch), mouse hover, and region selections
(e.g., rectangu lar and elliptical regions or free-form lassos) (Fig. 16.2).

• Navigate to examine high-level patterns and loiv-level detail. Visualizations often
contain more information than can be comfortably shown on screen, either

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16.2 Tasks in Data Visualization 557

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representation of the social network for all of the characters in Victor Hugo’s Les
Miserables. Characters are linked together if they appear in the same chapter in
the book. The textual annotations are connected to nodes using red lines and stay
connected as the graph layout changes. The toolbar on the upper left is part of
the VisDock toolkit and provides tools for annotation, navigation, and selection
(Choi et al., 2015).

due to the sheer 11umber of pixels or just due to high visual clu tter . For such
dense information spaces, navigation operations such as pan and zoom allow
the user to control the size and position of the viewport on the visualization
(see the navigation tools in the tool dock in Fig. 16.2 or the map contro ls in
Fig. 15.11). Not surpri singly, zooming and panning operations have now be
come common in many conventional user applications, such as Google Maps,
Adobe Photoshop, and Microsoft Word.

• Coordinate vie-ivs for linked exploration. Since each visualization technique has
its own strengths and weaknesses, practical visualization tools often include
multiple views of the same dataset so that each view illustrates a specific as
pect of the data. When using multiple views in this way, such as in a visu
alization dashboard (Few, 2013), it is customary to coordinate the views so

558 Chapter 16 Data Visualization

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a multi-view visualization tool that shows different aspects of the data in separate
views (Valcin, 2016). Selecting items in one view highlights them in others; for
examp le, the user is current ly hovering over the bar for “70k-300k” in the view tit led
“# of Records,” which causes those 124 breaches to also be highl ighted in orange in
other views, includ ing in the timel ine at the bottom.

that selecting items in one view high lights the item (or related items) in other
views (Fig. 16.3). Also see Section 12.3.1 for more on this.

• Organize rnultiple windoivs and ivorkspaces. While involving multiple views of
a dataset allows users to explore complex data using straightforward and
familiar visuali zations, this also introduc es the need for users to organ ize and
lay out the views to fit their needs. Man y tools allow for dragging and drop
ping views to achieve this, such as the Keshif tool shown in Fig. 16.3.

16.2.3 Process and provenance
If the previous two categories of tasks deal with the mechanics of creating,
manipulating, and view ing vis uali za tions, the third category encompasses
higher-level tasks for scaffolding, interpr eting, and documenting the

16.2 Tasks in Data Visualization 559

exploration process. More specifically, the tasks here involve the ability to
record the analysis, to annotate regions of interest in a visualization, to share
views with colleague s, and to guide other s through presentations of the
analysis outcome.

• Record analysis histories for revisitation, review, and sharing. Visualization tools
do not only help user s collect insights from their data, they should ideally
also support mecharlisms to record these insight s as well as the path lead
ing up to them. One approach that several tools provide is an automatically
recorded history of interactions, allowing the user to review and revisit the
exploration and even shar e it with others (Fig. 16.4).

• Annotate patterns to docun1ent findings. Most visuali zation s use data in a read
only fashion since the goal is to let the data inform the user ‘s exploration,
but some tools allow for adding metadata in the form of textual or graphical
annotations associated with the visualization (Fig. 16.2). Textual annotations
constitute labels, captions, or comments, whereas graphical annotations are
sketches, highlights, or handwritte11 notes. To be truly useful, annotations
should be data-aware so that they are associated with underlying data poiI1ts
and not just drawn as a transparent layer on top of the visualization (Heer
and Shneiderman , 2012; Choi et al., 2015). Drawing annotations on such a
tran sparent layer make them meaningless when the visualization is filtered
or reorgaruzed.

• Share views and annotations to enable collaboration. Analyzing data is often
a social activity involving multiple users working together (Heer et al.,
2009), either in de facto teams or in loose constellations of people on the

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performed.

560 Chapter 16 Data Visualization

U.S. Shark Incidents by State

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can interact with the dashboard, causing views to update dynamically. The tool
also allows for application bookmarking (storing the state for specific insights) as
well as sharing the analysis on social media platforms such as Facebook, Twitter,
and Linkedln.

internet. The implication is clear: to support the ana lysis life cycle fully,
visual analytics tools should support social interaction. This could include
simple functionality to export shareable formats of charts (PDF, PNG,
JPG, etc.) and datasets (CSV, JSON, XLS, etc.) from a visualization tool as
well as more advanced sharing mechanisms such as application bookmark
ing and publishing visualiza tions on the web (Fig. 16.5).

• Guide users through analysis tasks or stories. As visua lization tools become
increasingly available to casual users looking to get insight into their own
data-such as their social networks, personal finances, or local communities
there is also an increased need to guide these novices through appropriate
approaches to analy ze their data. Simjlarly, carefully crafted data stories can
help explain even complex phenomena using a combination of visua lization s,
annotations, and textual descriptions (Fig. 16.6).

London’s 185-1 Cholera Outbreak:
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Mapping the Results

16.3 Visualization by Data Type 561

Snow’s Analysis
Focus on Broad St.

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Web-based visua lization of London’s 1854 cholera outbreak showing physician
John Snow’s use of visualization to find its source. This visualization was created in
Tableau using its Story Points feature, which allows users to build a narrative from
data. The horizontal list of five boxes at the top of the display are the main points in
t he story, and v iewers can be automatically guided through the story by moving to
each poin t from left to right.

16.3 Visualization by Data Type

The visualization field currently lacks a unified theory that can recommend the
optimal visualization technique given the data type to represent and the tasks
that the user wants to perform. Furthermore, most data typ es do not have a
straightforward mapping from symbolic to visual form; consider the complete
works of William Shakespeare, a hundred years of temperature data for a thou
sand weather stations across the United States, or an organizational chart that
changes over time as people move through the ranks – none of these da tasets
can be trivially rendered in graphical form. Finally, all visualization techniques

562 Chapter 16 Data Visua lization

have their strengths and weaknesses. The net result is that selecting the appro
priate visualization technique for a dataset and task is still very much a design
proble1n, similar to interaction design as a whole.

To help designer s find appropriate visualizations, this section gives arl
overview of seven common data types and some represen tative visualization
techniques for each type (Box 16.2). Similar to the section on tasks (Section 16.2),
this section is not intended to be exhaustive, but rather to provide some concrete
examples and guidelines on these de sign clloices.

• 1-D linear data. Linear data types are one dimensional-such as program
source code, textual documents, dictionari es, and alphabetica l lists of
nam es- and can be organized in a sequential manner. Text, in particular, is
a linear data type because it is designed to be read in sequence, so the chal
lenge is to represent the data in such a way that not every word needs to be
read and the paralJel nature of visualization can be leveraged. Tag clouds and
word clouds, which scale social tag s versus words based on their frequency
and arrange them in a 2-D space, originated on the social photo sharing web
site Flickr (Viegas and Wattenberg, 2008) and have quick ly become the most
common text visualization technique (Fig. 16.7). More advanced text visual
iza tions exist that also mak e good use of position, phrase s, and relations; the
typographic maps created by Axis Maps (Fig. 16.9) and also later rep licated
by Afzal et al. (2012) are one examp le.

• 2-D space data. Planar data include geographic maps, floor plans, and
newspaper layouts. Each item in the collection covers some part of the to
tal area and may or may no t be rectangular. Each item has task -domain

BOX 16.2
Summary of data types and example visualization techniques associated with each.

Data Type

1-D linear

2-D space

3-0 volume

Multi
dimensional

Temporal

Tree

Network

Visualization Techniques and Systems

Tag clouds, Wordle, PhraseNets, paral lel tag clouds

Geographic information systems (GIS), self-o rgan izing maps

Volume render ing, medical visual ization, molecule
visualization

Tableau, parallel coo rdinates, scatte rplot mat rices

Google Finance, EventFlow, Life l ines, TimeSearcher

Treemaps, degree of interest trees, space trees

Node-link diagrams, adjacency matrices, NodeXL, Cytoscape

16.3 Visualization by Data Type 563

attributes, such as name, owner, and value, and interface-domain features,
such as shape, size, color, and opacity. Many systems adopt a multiple
layer approach to dealing with map data, but each layer is 2-0. User tasks
include finding adjacent items, regions containing certain items, and
paths between items and performing the seven basic tasks . The canonical
examp le is geographic information sys tems, such as Google Maps a11d Esri
ArcGIS, but the John Snow example (Fig. 16.6), the New York Tirnes 2012
electoral map (Fig. 16.8), and typographic maps (Fig. 16.9) are also forms of
2-D maps.

• 3-D volume data. Real-world objects, such as molecules, the human body,
and buildings, have volume and complex relationships with other items.
Computer-assisted medical imaging, architectural drawing, mechanical
design, chemical structure modeling, and scientific simulations are built to
handle these complex 3-D relationships (Fig. 16.10). Users’ tasks typically
deal with continuous variables such as temp era ture or density. Results are
often presented as volumes and surfaces, and users focus on relationships of
left/ right, above/below, and inside/ outside. In 3-D applications, users must

564 Chapter 16 Data Visualization

Gun SuK::100-s and Homicides in the Uniled Slates: 2000-

2014

Media coverage of gun vie>’ence in the United States primarily centers around homic ides or mass murders HCM’ever. there is one type of gun vioience that goes
almost unmonbonod: su1odos From 2D00·2014. thorn wore wMo thorn wom

Glide on !he stRte tiles below to see trends in qun suicides compRred to rzun homicides

Gun Suicides Gun Homicides

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27 states averaged 7 3 or more suicides per 100.000 people whi’8 only two states had an average homrClde rate of 7 3 or more per 100.000 p~ple.

Gun Oe11th?1o per 100,000

I #RevilPro;oct I @sirvi.talot

FIGURE 16.8
Geographic visualization of gun deaths (suicides versus homicides) in the Uni ted
States from 2000 to 2014 using data from the Center for Disease Control and
Prevention (CDC). Instead of using the actual geographic boundaries of the
individual states, this map replaces states with uniform hexagons that have
been co lor-coded using the color scale on the bottom right. The benefit of this
representation is to prevent large states from dominating the visua l appearance of
the overall map. The hexagons have been placed so that they largely preserve the
topology of the original map. (https://public .tableau.com/profi le/matt .chambers#!/
vizhome/TheGunProblemWeDont Mention/TheGunProblemWeDontMent ion)

cope with their position and orientation when viewing the objects and must han
dle the potential problems of occlusion and navigation. Chapter 7 gives some more
insight into the challenges and opportunities in 3-D immersive environments.

• Multidimensional data. When the rlumber of data dimensions for a dataset ex
ceeds the three dimensions that can be trivially rendered using 3-D graph
ics (Fig. 16.10), the dataset is said to be multidimensional. Such data are
commonly found in relational databases as well as spreadsheets, where the
columns become data dimensions and the rows become data points or items.
Most tools for multidimensional visualization manage the large number of
dimensions by using multiple views to show different aspects of the data.
Microsoft Excel and Tableau (Fig. 16.1) are examples of such tool s; in Tableau,
multiple charts can also be connected (Section 16.2.2). Tableau also allows
analysts to assemble multiple views into visualization dashboards (Few, 2013),
wh ich provide easily referenced insight into new data as they come in. A few

16.3 Visualization by Data Type 565

FIGURE 16.9
Typographic map of Washington, DC, created by Axis Maps. A typographic map
consists entirely of text organized into shapes using co lored labe ls of streets,
parks, highways, shore lines, and neighborhoods. Whi le this map took a skilled
cartographer hundreds of painstaking hours to create, Afzal et al. (2012) later
proposed an automatic approach taking mere minutes.

FIGURE 16. l 0
Two 3-D visua lizations created using the Visualization Toolkit (VTK), a commercial
software development library by Kitware, Inc. (http://www .kitware.com /). The left image
shows flow density around the space shutt le using a rainbow co lor scale. The right
image shows a CT scan of a human head with cross-sectional planes through the data.

566 Chapter 16 Data Visualization

visualization techniques disp lay a large number of data dimensions in the
same view, the most well-known being parallel coordinate plots (Inselberg,
2009). In a parallel coordinate plot, each parallel vertical axis represents a di
mension, and each item becomes a line connecting va lues in each dimension
(Fig. 16.11).

• Temporal data. Virtually all datasets can have a temporal component by
collecting data points over time; examples include electrocardiograms, stock
market prices, or weather data. Temporal data are separate from 1-D data in
that they have different relations for time points versus tim.e intervals and in
that they are sometimes linear, sometimes cyclical, and sometimes branching
(Aigner et al., 2008). Data can either be continuous (Fig. 16.12) or consist of
discrete events (Fig. 16.13) that have a start and finish time and may overlap.
Frequent tasks include finding all events before, after, or during some time
period or moment and in some cases comparing periodical phenomena. The
Gapminder tool (Fig. 12.9) visualizes time-changing data using animation
and trails.

• Tree data. Hierarchies or trees are collections of items where each item
(except the root) has a link to one parent item. Items and the links between
parents and chi ldren can have multiple attributes. Interactions can be ap
plied to items and links as well as to structural properties-for example, for

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FIGURE 16. 11
Parallel coordinate visualization of cars from the 1970s and 1980s created using the
D3 library. Thi s visualization supports axis f ilt ering where selecting data ranges on
the dimens ion axes fil ters out the cars th at do not meet all of the cr iteria (gray lines) .

.,. Jat1’1′ Ft.b’l:I Mar’ l)

2011 2012
< .,

FIGURE 16.12

Apr’lS

2013

16.3 Visualization by Data Type 567

Jul’ l :I

2014

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e .OJl•l .-Uli • . IXIC •11.1n, e JNX•5.40 ..
15’~

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Google Finance line graph showing the year-to-date performance of three stock
market indices: the Dow Jones Industrial Average (.OJI, blue), the NASDAQ
Composite (.IXIC, red), and the S&P 500 (.INX, yellow). The overview window at the
bottom shows several years from 2011 to 2015; grabbing the window allows for
panning and resizing the detail view (top).

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The EventFlow (http://www.cs.umd.edu/hcil/eventflow) temporal event visualization
system used to visualize sequences of innovation activities by Illinois companies.
Activity types include research, invention, prototyping and commercialization.
The timeline (right panel) shows the sequence of activities for each company. The
overview panel (center) summarizes all the records aligned by the first prototyping
activity of the company. In most of the sequences shown here, the company’s first
prototype is preceded by two or more patents with a lag of about one year between
the last patent application and the first prototype.

568 Chapter 16 Data Visualization

Olo …… -• I SOO

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FIGURE 16. 14
The S&P 500 Market Monitor by Visua l Action (http://www.visualaction.com/),
a web -based treemap visualization showing stock performance of the 500
la rge companies making up the S&P 500 index on the NYSE and NASDAQ
stock markets. Each rectangle rep resents a compa ny, sized according to its
marke t capitalization, co lored based on its one-day change, and organized
into sectors .

a company organizationa l chart, is it a deep or shallow hierarchy, and how
many employees does each manager supervise? Interface representations of
trees can use an outline style of indented labels or a more graphical style such
as a node-link diagram. Treemaps are a space-filJing approach that shows
arbitrary-sized trees in a fixed rectangular space (Shneiderman et al., 2012).
Treemaps have been app lied successfully to many app lications, from U.S.
budget proposals to stock market data to (Fig. 16.14).

• Network data. Unlike trees, networks have no single root but instead represent
arbitrary relationships between items. In addition to tree tasks, network users
often wan t to know about the shortes t or least costly paths connecting two
items or traversing the entire network. Node -link diagrams are one type of
interface representation (Fig. 16.2), but layout algorithms are often so complex
that user interaction is limited when large networks are shown, and filter
ing becomes important. Another option is to display an adjacency matrix,
with each cell rep resenting a potential link and its attribute va lues. Network
visualization is an old but sti ll imperfect art because of the complexity of
relationships and user tasks. New interest in this topic has been spawned by
visuali zation tools for social networks, such as NodeXL (Fig. 16.15 as we ll as
Fig. 11.1).

16.4 Cha llenges for Data Visualization 569

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F GURE 16.15
Socia l network visua lization built using NodeXL (Hansen et al., 2010) of 191 Twitter users
tweet ing w ith the hashtag “#G20AntalyaSummit” on November 9, 2015. The hashtag
refers to the 2015 G-20 summit held in Antalya, Turkey, on November 15-16, 2015. The
users have been grouped and laid out in boxes based on the contents of the tweets.
NodeXL (https ://nodexl.codeplex.com/) allows social scientists to collect, analyze, and
visua lize network graphs using a familiar interface that plugs into Microsoft Excel.

16.4 Challenges for Data Visualization

The task and data type task taxonomj es above help organize the field of data
v isuali za tion. Commercial visualization tools are incr easingly adopting man y of
these techniques. Furthermore, the number of visualization books-those that
are oriented toward students and researchers, such as Munzner (2014) and Ware
(2013), as well as those oriented tow ard designers and practiti oners, such as Yau
(2013) and Few (2013, 2015)- is incr easing and will serve to incr ease the audi
ence of visualization further. Nevertheless, there are still many challenges that
researchers and practitioners alike need to face to create successful tools:

• Importing and cleaning data. Deciding how to organize input data to
achieve a desired result often takes more thought and work than expected.

570 Chapter 16 Data Visualization

Getting data into the correct format, filtering out incorrect items, normaliz
ing attribute values, and coping with missing data can also be burdensome
tasks. This activity is also called data wrangling (Kandel et al., 2011) and has
resulted in a commercial product called Trifacta Wrangler (https:/ /www
. trifacta.com/) as well as Datawatch Monarch (http:/ /www .datawatch.com).

• Integrating data mining. Data visualization and data mining originated from
two separate lines of research. Visualization researchers believe in the
importance of involving users in the loop, while data mining researchers
believe that statis tical algorithms and machine learning can be relied on to
find interesting patterns. Some consumer purchasing patterns, such as spikes
in demand before snowstorms or corre lations between beer and pretzel pur
chases, stand out when properly visualized. However, statistical tests can be
helpful in finding more subtle trends in consumer desires or demographic
linkages for product purchases. Increasingly, researchers are combining the
two approaches to create visua l tools where users are aided by powerfu l com
putational algorithms; this research area has often been called visual analytics
and keeps the human in the loop but augments human capabilities with the
computer in a synergistic way.

• Viewing big data. A general challenge to data visual izatio n is the management
of large volumes of data. Many tools-e ven commercia l ones-can deal with
on ly a few thousand or tens of thousands of items whi le maintaining rea l-time
interactivity. Even if a tool is designed to manage larger datasets, there are
two ad.ditiona l limitations that quickly come into play: the number of avail
able pixe ls to show data on the screen and the number of individua l points
that can be perceived in practice by the user. Large disp lays can remedy the
forme r limitation (Chapter 10), at least to a point, but the human perceptual
limits are more difficult to circumvent. Instead, crafty visuali zation designers
and researchers must tum to data abstraction (Shneiderman, 2008; Elmqvist
and Fekete, 2010), where individual data points are partitioned, clustered,
or sampled into smaller and more manageable numbers. Recent advances in
interactive big data analytics (Fisher et al., 2012) are investigating solutions
to these problems, including partial queries, increme11tal visua lization, and
streaming data.

• Achieving universal usability. Making visualization tools accessib le to diverse
users regardless of their backgrounds, technical disadvantages, or personal
disabilities is necessary when the tools are to be used by the public, but it
remains a huge challenge for designers (Plaisant, 2005). For example, ,,isu
ally impaired users may need to use text-based alternatives to the visual
display. Sonification uses non-speech audio to convey data and can be used
for graphs, scatterplots, and tables as well as potentially more complex data
representations. Tactile displays (Figs. 10.25 and 10.26) can also be used to

16.4 Challenges for Data Visualization 571

convey data but are currently not widely available (even if 3-D printing is
making this kiI1d of “physical” visualization (Jansen et al., 2013) increasingly
plausible). Users with color deficiencies can be provided with alternative
palettes or tools to customize the display colors. For example, the popular
red/ green palette of colors can be complemented by an alternative blue/
yellow palette. Color Brewer (Fig. 12.10) and VisCheck (http:/ /vischeck.com/)
offer guidelines on color schemes that work for those with color vision
impairment.

• Supporting casual users. The original audience for visuali zation was scientists,
doctors, and engineers, and to this day, many tools are targeted to profession
als within science, engineering, medicine, business, and journa lism. How
ever, with the rising tide of public data and the advent of powerful web-based
visuali zation toolkits such as D3 (Bostock et al., 2011), visuali zation software
is leaving the exclusive domain of the office and is entering the kitchens, liv
ing rooms, and, indeed, bedrooms of millions of people worldwide. This
development has been dubbed casual visualization (Pousman et al., 2007) in
that it encompasses non-expert users exploring data with personal rather
than work-motivated relevance for different purposes than typical profes
sional usage, such as for awareness, reflection, and social insight. Accepting
this more inclusive definition of the audience for visualization also means that
the potential for lasting impact increases manifold. In fact, as visualization
gains traction for casual users, a new user group can be discerned: those who
are using the visualization tools for professional purposes and have expert
level skill in their own domain but who have little training, inclination, or
resources to achieve expertise in the use of visualization. Such data enthusiasts
or causal experts, for lack of a better term, represent an additional opportunity
for visualization to aclueve more widespread adoption in the future.

• Dissemination and storytelling. With the radically larger potential audience for
today’s visualization comes the need to better convey the findings from a vi
sual analysis without resorting to overly complex visual representations. Tl1is
focus on dissemination has caused both visualization researchers and practi
tioners to adopt the notion of storytelling to build narrative visualizations that
explain their findings using locations, characters, and plot (Segel and Heer,
2010; Kosara and Mackinlay, 2013). In fact, Tableau released its 11ew Story
PoiI1ts mode (Fig. 16.6) in 2013 to help people create these data stories them
selves.

• Adapting to any device. Better performance, advanced graphics, touch dis
plays, mobile computing, and natural interfaces-needless to say, much has
happened in the field of computing during the 25-odd years since visualization
was introduced. However, visualization has been curiously resistant to chal
lenging the status quo of the personal computer (Lee et al., 2012). Embracing

572 Chapter 16 Data Visualization

this revolutionary leap in technology would enable an equa lly transformative
leap for visualization in several ways. First of all, many of these ne\-v comput
ing platforms encompass large, multi-user displays (e.g., Figs. 10.19, 10.20,
10.21, and 10.22), which will facilitate collaborative and social visualizatiorl
(Isenberg et al., 2011). Second, harnessing mobile devices would enab le us to
apply ubiquitous computing to anytime and ai1ywhere sensemaking of data
(Elmqvist and Irani, 2013). Third, this new generation of pen-, touch-, or ges
ture-based – almost “natural” – interaction may yield increased fluidity and
flexibility, better freedom of expression, and reduced indirection between the
person , the technology, and the data (Lee et al., 2012). In fact, the notion of a
visualization may even transcend digital devices and take physical form (left
side of Fig. 10.25).

• Evaluation. Data visuali zation systems can be very complex. The analysis is
rarely an isolated short- term process, the tasks are high-level, and users may
need to look at the same data from different perspectives over a long period
of time (Carpendale, 2008). They may also be able to formulate and answer
questions they didn’t anticipate having before looking at the visualization
(making it difficult to use typical empir ical studies technique s, where sub
jects are recruited for a short time to work on imposed tasks). Finally, while
discoveries can have a huge impact, they occur very rarel y and are unlikely
to be observed during a study. Insight-based studies, as described by Sarai ya,
North, and Duca (2005), are one first step. Case studies report on users in their
na tur al environments doing rea l tasks. They can describe discoveries, col
laborations among users, frustrations of data cleansing, and the excitement of
data exploration, and they can report on frequency of use and benefits gained
(Perer and Shneiderman, 2008). The disadvantage of case studies is that they
are very time-consuming and may not be replicable or applicable to other
domains.

Practitioner’s Summary

Data vis u alization is moving out of research laboratories with a growing
number of commercia l products now available, such as Tab leau Software ®,
TIBCO Spotfire, Trifacta Wrangler, Datawatch, IBM Cognos ®, Visual Action,
and Macrofocus. Meanwhile, the web has become a prime platform for
visualization, with the 03 toolkit now almost having been elevated to a stan
dard for such web-based visua lizations, and commercia l tools routinely pro
viding mechanisms to pub lish, share, and discuss visualizations using the

Researcher’s Agenda 573

web. While practical tools are increasingly providing interactions and ,,isua l
representations almost as soon as they are proposed by the scientific commu
nity, successful designers need to familiarize themselves with both standard
data -drive n tasks as well as visualization techniques to be able to 11avigate
and select the most suitable one.

Researcher’s Agenda

As data visualization is becoming mainstream, the increased exposure
and impact are also calling for more fundamental, streamlined, and usable
research that can be quickly adapted in commercial tools as well as included
in infographics and web-based visualizations on the internet. Specific future
challenges for data visualization include impro ving the wra11gling of data
prior to being able to visua lize a dataset, continued integration of automatic
algorithms with humans in the loop to facilitate analytical reasoning, and a
renewed emphasis on big data to tackle the truly wicked problems of our
society and world. This broadening appeal also mean s that several social fac
tors of visualization are becoming more important tl1an ever, including uni
versal usabi lity, casual users, multiple device platforms, and the focus on
dissemination through storytelling to lower e11try barriers. Finally, as with
all of HCI, improvement is only pos sible in the pre sence of mea surement, so
evaluaho11 remains a challenge for visualization researchers and practitio
ner s alike.

574 Chapter 16 Data Visualization

WORLD WIDE WEB RESOURCES

www. pearsonglobaleditions . com / shneiderman

• Crossfilter, a JavaScript library for multivariate filtering: http://square

.g ith u b. io/crossfi lter/
• Cytoscape, a graph visualization platform: http://www.cytoscape

.org/

• D3, a web-based visualization toolkit: http://d3js.org/
• FlowingData, a showcase of effective visualization and analysis:

http://f lowingdata.com/
• Gephi, a graph visual ization platform: http://geph i.github.io/
• ggplot2, an R implementation of the grammar of graphics: http://ggplot2

.org/

• Keshif, a multi-view data exploration too l for multi-dimensional data:
http://keshif.me/

• Lyra, a visualization design platform with no programming requirements:

http://id I .cs. wash i ngto n .edu/p roj ects/lyra/
• NodeXL, an Excel plugin for graph visualization: https://nodexl.codeplex

.com/
• Polymaps, a JavaScript I ibraryforcreating dynamic maps: http://polymaps

.org/
• Processing.js, a JavaScript port of the Processing library: http://

processingjs.org/
• Raphael, a JavaScript library for vector graphics: http://raphaeljs.com/

• SHIVA, a web application for online visualization: http://www.viseyes.org/
• Vega, a declarative grammar for visualization: https://vega.github.io/
• VisDock, a JavaScript library for interaction in visualization: https://goo.gl/414pu1

Discussion Questions

1. Produce a definition of data visualization. Explain how it caters to the per
ceptual abilities of humans.

2. Describe a taxonomy of interactive dynamics that combine the analysis task
with the practical operatio11s that users need in their visualization tools of in
teractive dynamics that results in task types for data visualization (Heer and
Shneiderman, 2012).

3. Differentiate between tree data and network data.

4. Describe three challenges data visualization researchers face when trying to
build an interface. Suggest solutions to conquer these problems.

References 575

References

Afzal, Shehzad, Maciejewski, Ross, Jang, Yun, Elmqvist, Niklas, and Ebert , David S.,
Spatial text visualization using automatic typographic maps, IEEE Transactions on
Visualization and Con1puter Graphics 18, 12 (2012), 2556-2564.

Aigner, Wo lfga ng, Miksch, Silvia, Muller, Wo lfgang, Schum ann, Heidru n, and
Tominski, Christian, Visual methods for analyzing time-oriented data, IEEE
Transactions on Visualization and Conzputer Graphics 14, 1 (2008), 47-60.

Amar, Robert, Eagan, James, and Stasko, John T., Lo\-v-level components of analytic
activity in information visualization, Proceedings of the IEEE Syn1.posiu1n on Inforn1ation
Visualization, IEEE Computer Society, Washington, DC (2005), 111- 117.

Bostock, Michael, Ogi evets ky , Vadim, and Heer, Jeffrey , D3: Data-driven
documents, IEEE Transactions on Visualization and Co·mputer Graphics 17, 12 (2011),
2301- 2309.

Card, Stuart, Information visua liza tion, in Jacko, Julie (Editor), The Hu,nan-Computer
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lit1,11ions 21- i

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( An Ou!bfeak Begins Colecting the Data

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Mapping the Results

16.3 Visualization by Data Type 561

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Focus on Broad St.

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16.3 Visualization by Data Type

562 Chapter 16 Data Visua lization

BOX 16.2
Summary of data types and example visualization techniques associated with each.

Data Type

1-D linear

2-D space

3-0 volume

Multi
dimensional

Temporal

Tree

Network

Visualization Techniques and Systems

Tag clouds, Wordle, PhraseNets, paral lel tag clouds

Geographic information systems (GIS), self-o rgan izing maps

Volume render ing, medical visual ization, molecule
visualization

Tableau, parallel coo rdinates, scatte rplot mat rices

Google Finance, EventFlow, Life l ines, TimeSearcher

Treemaps, degree of interest trees, space trees

Node-link diagrams, adjacency matrices, NodeXL, Cytoscape

16.3 Visualization by Data Type 563

564 Chapter 16 Data Visualization

Gun SuK::100-s and Homicides in the Uniled Slates: 2000-2014

Glide on !he stRte tiles below to see trends in qun suicides compRred to rzun homicides

Gun Suicides Gun Homicides

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® AZ ® 1<}) AR 0 0 © ® 81 UT ® • e ® $ NM "" 0 & i'I ® ®® LA e ee G ® @ e HI ~ ,. e 27 states averaged 7 3 or more suicides per 100.000 people whi'8 only two states had an average homrClde rate of 7 3 or more per 100.000 p~ple. Gun Oe11th?1o per 100,000 I #RevilPro;oct I @sirvi.talot FIGURE 16.8 Geographic visualization of gun deaths (suicides versus homicides) in the Uni ted States from 2000 to 2014 using data from the Center for Disease Control and Prevention (CDC). Instead of using the actual geographic boundaries of the individual states, this map replaces states with uniform hexagons that have been co lor-coded using the color scale on the bottom right. The benefit of this representation is to prevent large states from dominating the visua l appearance of the overall map. The hexagons have been placed so that they largely preserve the topology of the original map. (https://public .tableau.com/profi le/matt .chambers#!/ vizhome/TheGunProblemWeDont Mention/TheGunProblemWeDontMent ion) cope with their position and orientation when viewing the objects and must han dle the potential problems of occlusion and navigation. Chapter 7 gives some more insight into the challenges and opportunities in 3-D immersive environments. • Multidimensional data. When the rlumber of data dimensions for a dataset ex ceeds the three dimensions that can be trivially rendered using 3-D graph ics (Fig. 16.10), the dataset is said to be multidimensional. Such data are commonly found in relational databases as well as spreadsheets, where the columns become data dimensions and the rows become data points or items. Most tools for multidimensional visualization manage the large number of dimensions by using multiple views to show different aspects of the data. Microsoft Excel and Tableau (Fig. 16.1) are examples of such tool s; in Tableau, multiple charts can also be connected (Section 16.2.2). Tableau also allows analysts to assemble multiple views into visualization dashboards (Few, 2013), wh ich provide easily referenced insight into new data as they come in. A few 16.3 Visualization by Data Type 565 FIGURE 16.9 Typographic map of Washington, DC, created by Axis Maps. A typographic map consists entirely of text organized into shapes using co lored labe ls of streets, parks, highways, shore lines, and neighborhoods. Whi le this map took a skilled cartographer hundreds of painstaking hours to create, Afzal et al. (2012) later proposed an automatic approach taking mere minutes. FIGURE 16. l 0 Two 3-D visua lizations created using the Visualization Toolkit (VTK), a commercial software development library by Kitware, Inc. (http://www .kitware.com /). The left image shows flow density around the space shutt le using a rainbow co lor scale. The right image shows a CT scan of a human head with cross-sectional planes through the data. 566 Chapter 16 Data Visualization visualization techniques disp lay a large number of data dimensions in the same view, the most well-known being parallel coordinate plots (Inselberg, 2009). In a parallel coordinate plot, each parallel vertical axis represents a di mension, and each item becomes a line connecting va lues in each dimension (Fig. 16.11). • Temporal data. Virtually all datasets can have a temporal component by collecting data points over time; examples include electrocardiograms, stock market prices, or weather data. Temporal data are separate from 1-D data in that they have different relations for time points versus tim.e intervals and in that they are sometimes linear, sometimes cyclical, and sometimes branching (Aigner et al., 2008). Data can either be continuous (Fig. 16.12) or consist of discrete events (Fig. 16.13) that have a start and finish time and may overlap. Frequent tasks include finding all events before, after, or during some time period or moment and in some cases comparing periodical phenomena. The Gapminder tool (Fig. 12.9) visualizes time-changing data using animation and trails. • Tree data. Hierarchies or trees are collections of items where each item (except the root) has a link to one parent item. Items and the links between parents and chi ldren can have multiple attributes. Interactions can be ap plied to items and links as well as to structural properties-for example, for ♦COtlOfflt' {~ ) ""'.,n d~lao.-11.1 (cc) po.., , l,tlp) -lg~ tb ) 0-40 ~.II i i ) ,.., ... .,, ., ., 220 , .000 .. •DO 200 70 4,$00 110 ... .. ... 4,000 "' .. •• ' " '°''j 20 0 ,.. 0 FIGURE 16. 11 Parallel coordinate visualization of cars from the 1970s and 1980s created using the D3 library. Thi s visualization supports axis f ilt ering where selecting data ranges on the dimens ion axes fil ters out the cars th at do not meet all of the cr iteria (gray lines) . .,. Jat1'1' Ft.b'l:I Mar' l) 2011 2012 < ., FIGURE 16.12 Apr'lS 2013 16.3 Visualization by Data Type 567 Jul' l :I 2014 O•c 17, 201~ • Oct 30 . 2015 e .OJl•l .-Uli • . IXIC •11.1n, e JNX•5.40 .. 15'~ I :V I 2ois Google Finance line graph showing the year-to-date performance of three stock market indices: the Dow Jones Industrial Average (.OJI, blue), the NASDAQ Composite (.IXIC, red), and the S&P 500 (.INX, yellow). The overview window at the bottom shows several years from 2011 to 2015; grabbing the window allows for panning and resizing the detail view (top). Q Set WlflCIOw8r TIIM': ~1 .. ~ 3 ~ kC WIIICIOw 8'-{ llfllltc-t '!1] ... , • ., i/J~ ■ IMic:JAP~~ • lll !il • lilWtnlfOn m ~~-~Pt 1N 2/ttl ■Col'MM,roalitaloft 111 OO SeMclM 0 0 0ese5KIAI I FIGURE 16.13 •- • I I II I •' ·I ~ I ··I 111 I •' I ' I lJ \ I .. l , __ _ I MP T«twdoQ I l,IU0tfS._ l'IC I Mkrd..n• °" ,--- • • • • The EventFlow (http://www.cs.umd.edu/hcil/eventflow) temporal event visualization system used to visualize sequences of innovation activities by Illinois companies. Activity types include research, invention, prototyping and commercialization. The timeline (right panel) shows the sequence of activities for each company. The overview panel (center) summarizes all the records aligned by the first prototyping activity of the company. In most of the sequences shown here, the company's first prototype is preceded by two or more patents with a lag of about one year between the last patent application and the first prototype. 568 Chapter 16 Data Visualization Olo ...... -• I SOO Mk rosoft corp. MSn $53.77 J.75% S•r: ln!ff””‘D … lf dlnel .. y
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16.4 Challenges for Data Visualization

• Importing and cleaning data. Deciding how to organize input data to
achieve a desired result often takes more thought and work than expected.

570 Chapter 16 Data Visualization

16.4 Challenges for Data Visualization 571

572 Chapter 16 Data Visualization

Practitioner’s Summary

Researcher’s Agenda 573

Researcher’s Agenda

574 Chapter 16 Data Visualization

WORLD WIDE WEB RESOURCES

www. pearsonglobaleditions . com / shneiderman

• Crossfilter, a JavaScript library for multivariate filtering: http://square

.g ith u b. io/crossfi lter/
• Cytoscape, a graph visualization platform: http://www.cytoscape.org/
• D3, a web-based visualization toolkit: http://d3js.org/
• FlowingData, a showcase of effective visualization and analysis: