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Data Visualization (CIS/DSC 468) Definitions Dr. David Koop D. Koop, CIS 468, Spring 2017

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Page 1: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Data Visualization (CIS/DSC 468)

Definitions

Dr. David Koop

D. Koop, CIS 468, Spring 2017

Page 2: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

What is Data Visualization?

2D. Koop, CIS 468, Spring 2017

Page 3: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

What are the purposes of visualization?

3D. Koop, CIS 468, Spring 2017

Page 4: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Exploration <-> Communication Spectrum

4D. Koop, CIS 468, Spring 2017

Exploration CommunicationConfirmation

http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

Consecutive Starts by a Quarterback for a Single Team

Page 5: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Exploration <-> Communication Spectrum

4D. Koop, CIS 468, Spring 2017

Exploration CommunicationConfirmationQuestions Answers/Persuasion

http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

Consecutive Starts by a Quarterback for a Single Team

Page 6: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Where have you seen visualizations?

5D. Koop, CIS 468, Spring 2017

Page 7: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Books / Posters

6D. Koop, CIS 468, Spring 2017

[Rock 'N' Roll is Here to Pay, R. Garofalo, 1977]

Page 8: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Books / Posters

6D. Koop, CIS 468, Spring 2017

[Rock 'N' Roll is Here to Pay, R. Garofalo, 1977]

Page 9: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

FAQAlbum or artist: Search...Music Timeline

popu

larit

y

1982

ThrillerMichael Jackson

2003

Chocolate FactoryR. Kelly

2006

Back To BlackAmy Winehouse

2007

Good Girl Gone BadRihanna

2007

Marvin Gaye '50'Marvin Gaye

2014

XChris Brown

2014

In The Lonely HourSam Smith

1988

Past MastersThe Beatles

2006

The Essential Elvis Presley 3.0Elvis Presley

2009

My Kind Of ChristmasDean Martin

2010

Teenage DreamKaty Perry

2012

Four of a Kind - 200 Classic SongsThe Everly Brothers

2015

DeliriumEllie Goulding

2015

VMaroon 5

19501950

1950

19601960

1960

19701970

1970

19801980

1980

19901990

1990

20002000

2000

20102010

2010Comedy/SpokenWord/Other

World

Vocal/Easy Listening

Folk LatinReggae

Dance/ElectronicR&B/Soul

Blues Hip-Hop/Rap

Alternative/Indie

Country

Metal

Jazz Rock

Pop

Web

7D. Koop, CIS 468, Spring 2017

[Music Timeline, Google Research]

Page 10: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

What is the advantage of the second version?

8D. Koop, CIS 468, Spring 2017

Page 11: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Interaction

9D. Koop, CIS 468, Spring 2017

Page 12: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Visualization Exploration and Communication

10D. Koop, CIS 468, Spring 2017

Page 13: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

11D. Koop, CIS 468, Spring 2017

Page 14: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

12D. Koop, CIS 468, Spring 2017

Page 15: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

13D. Koop, CIS 468, Spring 2017

Page 16: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

13D. Koop, CIS 468, Spring 2017

Page 17: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

14D. Koop, CIS 468, Spring 2017

Page 18: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

15D. Koop, CIS 468, Spring 2017

Page 19: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

MTA Fare Data Exploration

15D. Koop, CIS 468, Spring 2017

A U G U S TS U N M O N T U E W E D T H U F R I S A T

2 3

10

17

24

31

9

16

23

30

SD SDHOU DETDETT OR DET

DET DETCHW COLCHWSD CHW

BOS BOSLAA LAALAADET LAA

TB TBTOR TORTORBOS TOR

BAL BALTOR TORTORTB TOR

1

8

15

22

29

1

7

14

21

28

3

6

13

20

27

2

5

12

19

26

1

4

11

18

25

1:10 1:10 10:10 8:40

8:104:10 8:10 8:10 1:10 7:05 1:05

7:05TBA 7:05

7:071:40 7:07 7:077:07 7:05 1:05

7:05 1:05 7:10 4:05

1:10TBA 7:05 7:05 1:05 7:10 7:10

YES YES YES

YES YES MY9 YES YES YES YES

TBA YES YES YES YES MY9 FOX

TBA YES MY9 YES YES MY9 YES

YES YES YES YES YES YES YES

S E P T E M B E RS U N M O N T U E W E D T H U F R I S A T

6 7

14

21

28

30

13

20

27

29

BOS BOSCHW BOSCHWBAL CHW

BOS BOSBAL BALBALBOS BAL

SF SFTORTOR TOR TORBOS

HOU HOUTB TBTBSF TB

T OR T ORCHW CHWHOUHOU HOU

5

12

19

26

28

4

11

18

25

27

3

10

17

24

30

2

9

16

23

30

1

8

15

22

29ALL GAMES ARE EASTERN TIME.

1:051:05 7:05 7:05 7:05 7:05 1:05

7:05TBA 7:05 7:05 7:05 7:10 1:05

1:10TBA 7:07

1:102:10 1:10

7:07 7:07 7:05 TBA

1:101:05 7:05 7:05 7:05 8:10 TBA

TBA YES MY9 YES YES MY9 FOX

YES YES YES YES YES YES FOX

TBA YES MY9 YES YES YES TBA

YES YES

YES YES

MY9 YES YES YES TBA

2 013 R E G U L A R S E A S O N S C H E D U L E

Page 20: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Administrivia• Course Web Site • Syllabus

- Plagiarism - Accommodations

• Textbook - Munzner (required) - Murray (available online)

• Assignments • Exams (2 + Final, 3 Total) • Registration

- Add/Drop Deadline Friday

16D. Koop, CIS 468, Spring 2017

Page 21: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Textbooks

17D. Koop, CIS 468, Spring 2017

Munzner Murray

Page 22: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Do not cheat!

18D. Koop, CIS 468, Spring 2017

Page 23: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Do not cheat • Cheating on assignments, projects, and exams is not allowed • You will receive a zero on the assignment/project/exam • It will be reported to the department and university • If it repeats, you will fail the course • You can be kicked out of the university

19D. Koop, CIS 468, Spring 2017

Page 24: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Do ask questions!

20D. Koop, CIS 468, Spring 2017

Page 25: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Do ask questions• If you are stuck on a specific issue with an assignment:

- Do email me with specific questions - Do consult books, online documentation, tutorials - Do discuss that specific issue with a classmate

• If you are asked about a question: - Do not share your code - If the questioner is trying to cheat, walk away - If you see an obvious mistake, kindly point it out - Suggest a specific function or library that may be useful

21D. Koop, CIS 468, Spring 2017

Page 26: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

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157 St1

175 StA

181 StA

190 StA

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4

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Allerton Av2•5

183 St4

Burnside Av4

176 St4

Mt Eden Av4

170 St4

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Bliss St 740 St

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61 StWoodside

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36 StM•R

90 St–Elmhurst Av7

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103 St–Corona Plaza7

111 St 7•Q48 LGA Airport

Elmhurst

Av

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Newtown

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86 St4•5•6

96 St6

103 St6

110 St6

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116 St6

72 StB•C

81 St–Museum of Natural History B•C

86 St B•C

96 StB•C

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116 StB•C

125 StA•B•C•D

125 St2•3 • M60 LaGuardia Airport

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174–175 StsB•D

170 StB•D

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Gun Hill Rd 2•5

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225 St2•5

233 St2•5

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Wakefield 241 St2

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EastchesterDyre Av5

167 StB•D

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Longwood Av6

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Elder Av6

Morrison Av Soundview 6

St Lawrence Av6

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Middletown Rd6

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51 St 6

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125 St1

168 St A•C•1 A•C

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Inwood207 St

A

215 St1

3 Av–149 St2•5

Woodlawn4

Marble Hill225 St1

231 St1

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Cypress Hills J

85 St–Forest Pkwy J

Woodhaven Blvd J•Z

104 St Z rush hours, J other times

111 StJ

121 St Z rush hours, J other times

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Jamaica Center Parsons/ArcherE•J•Z

Jackson Hts

Roosevelt Av

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Q47 LGA Airport (Marine Air Term only)

FlushingMain St

7

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Crown HtsUtica Av3•4

Saratoga Av 3

Rockaway Av 3

Junius St 3

Pennsylvania Av3

Van Siclen Av3

New Lots Av3

Sutter Av–Rutland Rd3

A•C•J•Z2•3•4•5

Westchester Sq East Tremont Av 6

Intervale Av 2•5

Prospect Av 2•5

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Van Siclen Av Z rush hrs, J other times

138 St–GrandConcourse4•5

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M60 LGAAirport

M60 LaGuardia Airport

M60 LGA Airport

Rector St1

Cortlandt St1

Cortlandt St R

South Ferry1

World TradeCenter

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207 St 1

rushhours

rushhours

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lt

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tunnel closeduntil fall 2014

runs weekends via Manhattan Br

The subway operates 24 hours a day, but not all lines operate at all times. This map depicts morning to evening weekday service. Call our Travel Information Center at 511 for more information in English or Spanish (24 hours) or ask an agent for help in all other languages (6AM to 10PM).

To show service more clearly, geography on this map has been modified. © 2013 Metropolitan Transportation Authority

visit www.mta.info

Key

August 2013

Full time servicePart time service

All trains stop (local and express service)

Local service onlyRush hour line

extension

Free subway transferFree out-of-system subway transfer (excluding single-ride ticket)

Terminal

Bus or AIRTRAINto airport

Accessiblestation

Additional expressservice

Normal service

Commuter rail service

Bus to airport

StationName

A•C

New York City Subwaywith bus and railroad connections

Police

M60

The subway map depicts weekday service. Service differs by time of day and is sometimes affected by construction. Overhead directional signs on platforms show weekend, evening, and late night service. Visit mta.info for detailed guides to subway service: click on Maps, then “Individual Subway Line Maps,” “Service Guide,” or “Late Night Service Map.” For construction-related service changes, click on “Planned Service Changes” in the top menu bar. On weekends, the Weekender website and app show construction-related scheduled service changes. This information is also posted at station entrances and on platform columns of affected lines.

Transportation Data - NYC MTA

22D. Koop, CIS 468, Spring 2017

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Definition of Visualization

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.” — T. Munzner

23D. Koop, CIS 468, Spring 2017

Page 28: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.”

24D. Koop, CIS 468, Spring 2017

Page 29: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.”

24D. Koop, CIS 468, Spring 2017

NYC Subway Fare Data

Page 30: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.”

24D. Koop, CIS 468, Spring 2017

Find Interesting NYC Subway Ridership Patterns

NYC Subway Fare Data

Page 31: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.”

25D. Koop, CIS 468, Spring 2017

Page 32: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Why People?• Certain tasks can be totally automated

- Statistical computations - Machine learning algorithms - We don’t need visualization for these tasks (although perhaps for

debugging them…) • Analysis problems are often ill-specified

- What is the correct question? - Exploit human visual system, pattern detection capabilities - Goal may be an automated solution or a visual analysis system

• Presentation - It is often easier to show someone something than to tell them a

bunch of facts about the data (and let them explore it)

26D. Koop, CIS 468, Spring 2017

Page 33: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Why Computers?

27D. Koop, CIS 468, Spring 2017

[Cerebral, Barsky et al., 2007]

Page 34: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Why Computers?

28D. Koop, CIS 468, Spring 2017

[Cerebral, Barsky et al., 2007]

Page 35: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Resource Limitations• Memory and space constraints • How many pixels do I have? • Information Density

29D. Koop, CIS 468, Spring 2017

Fig. 2. A tree of regions and major islands of the Philippines, drawnusing squarified treemaps (top) and using icicle diagrams (bottom). Thetwo diagrams on the left weight leaf nodes by geographic area, whereasthe two diagrams on the right give equal area to leaf nodes. Labels arerotated when necessary to maximize their size.

arbitrarily deep. Our analysis allows us to rank tree representations bytheir efficiency, which is useful for helping designers choose the mostefficient representation allowable within other given constraints.

Our work also quantifies an interesting difference between repre-sentations in how they distribute area across nodes. For example, theicicle diagrams in Figures 1C, 2C, and 2D allocate equal area to eachlevel of the tree: the root node has the same area as all the leaf nodestogether. Treemaps, in contrast, typically allocate more area to deepernodes. There is a tradeoff here, since we would like users to be ableto see as many deep nodes as possible (which tend to also be the mostnumerous nodes), while at the same time providing some informationabout shallow nodes (for example, to give an overview of subtrees,and/or to guide the user in zooming operations). This article developsa new metric, the mean area exponent, that describes the distributionof area across levels of a tree representation, to quantify this tradeoff.

Finally, we also present a set of design guidelines for using treerepresentations, as well as a few novel tree representations, includinga variation on squarified treemaps that allows for larger labels withinthe nodes.

2 RELATED WORK

Different tree representations, including classical node-link, icicle,nested enclosure, and indented outline, were identified decades agoin [4, 16], and an interactive version of the indented outline represen-tation (now popular in file browsers such as Microsoft Explorer) waspresented in [11]. Subsequent years have seen variations on these rep-resentations proposed. Treemaps are a relatively recent innovation,and are a kind of nested enclosure representation. Treemaps are of-ten described as space-filling, a highly desirable property for space-efficiency.

The term “space-filling” can sometimes be problematic, however.For example, a view sometimes expressed [21] holds that tree repre-sentations can be divided into two classes: (1) node-link diagrams, thatillustrate parent-child relationships with line segments or curves, and

(2) space-filling representations, which include treemaps and concen-tric circles such as Sunburst (Sunburst was described as space-filling in[27], and [21] similarly describe [2] as space-filling.) However, thesetwo classes seem to not be disjoint, because some node-link diagramsalso “fill space” [19, 20]. The 2nd class also ignores an interesting dif-ference between treemaps and concentric circles, namely that parentnodes in treemaps enclose their children, whereas parents in concen-tric circle diagrams are adjacent to their children. Finally, the term“space-filling” suggests increased space-efficiency, however it is easyto design a treemap layout algorithm that occupies all available spacewithout making good use of it, for example, by using excessively thickmargins, or by concentrating child nodes in only one corner of theirparent, leaving the rest of the parent empty and unused. Would sucha treemap cease to be considered space-filling, even though its rootnode covers all the available space? Without a precise definition of“space-filling”, we recommend being cautious about using this termto refer to a category of tree representations, since the name seemsto imply that members of the category are more space-efficient thannon-members. As an alternative, categories of representations couldinstead be based on how the nodes are drawn (e.g. representationswhere the nodes are mapped to points, and those where the nodes aremapped to areas) or on how parent-child relationships are shown (e.g.through line segments, enclosure, adjacency, or relative positioning).The space-efficiency of a given representation can be treated as a sep-arate matter, and evaluated by several metrics, as demonstrated in thisarticle.

Within the graph drawing community, a common approach for eval-uating space-efficiency is to compare the total area required by differ-ent drawings (i.e., representations) of the same graph or tree. Sinceany drawing can be scaled arbitrarily in x and y, to ensure a meaning-ful comparison, the “resolution” of the representations is fixed, oftenby requiring that nodes be positioned on a grid (i.e. with integer co-ordinates) [9]. There are problems with this general approach, how-ever, especially when comparing representations of trees rather thangraphs. For example, allowing only grid positions may be mislead-ing, because non-integer coordinates can significantly reduce total areawithout compromising the clarity of the representation or the spaceavailable for labels (Figure 3). As a potential remedy, instead of posi-tioning nodes on a grid, we might instead impose a minimum distancebetween nodes, or a minimum size for non-overlapping labels centeredover the nodes. Unfortunately, matters are complicated by the fact thatsome tree representations (such as Figures 1C, 1E, 1F, 1G) involvenodes that have an area and shape, and there may be nodes and labelsof different sizes within a single representation (e.g. deeper nodes maybe smaller and have smaller labels). This makes it less clear how toimpose a fixed resolution in a way that is fair across tree representa-tions. Note that this issue does not arise in traditional graph drawing,where nodes are typically mapped to points.

Fig. 3. A and B are adapted from a comparison in Figure 5 of [1], andshow two different graphical representations of the same tree wherenodes are constrained to positions with integer coordinates. B is clearlymore compact than A. In C, however, we have redrawn the represen-tation from A with the integer coordinate constraint relaxed, and the re-sulting graphical representation has a convex hull whose area is onlyabout 5% greater than that in B. Notice also that the minimum horizon-tal spacing between nodes in B and C is the same, allowing nodes to beoverlaid with horizontally oriented labels of the same size in both cases.

In our work, rather than comparing total area with a fixed resolution,we fix the total area available, and fix its aspect ratio. Representations

[McGuffin & Robert, 2010]

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Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively”

30D. Koop, CIS 468, Spring 2017

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Why Visual?

31D. Koop, CIS 468, Spring 2017

I II III IV

x y x y x y x y

10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58

8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76

13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71

9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84

11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47

14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04

6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25

4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50

12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56

7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91

5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

[F. J. Anscombe]

Page 38: Data Visualization (CIS/DSC 468) - UMass Ddkoop/cis468/lectures/lecture02.pdfMarvin Gaye '50' Marvin Gaye 2014 X Chris Brown 2014 In The Lonely Hour Sam Smith 1988 Past Masters The

Why Visual?

31D. Koop, CIS 468, Spring 2017

I II III IV

x y x y x y x y

10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58

8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76

13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71

9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84

11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47

14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04

6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25

4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50

12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56

7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91

5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

Mean of x 9Variance of x 11Mean of y 7.50Variance of y 4.122Correlation 0.816

[F. J. Anscombe]

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●●

●●

●●

4 6 8 10 12 14 16 18

4

6

8

10

12

x1

y 1

●●

●●●

4 6 8 10 12 14 16 18

4

6

8

10

12

x2

y 2●

●●

●●

●●

4 6 8 10 12 14 16 18

4

6

8

10

12

x3

y 3

●●

●●

4 6 8 10 12 14 16 18

4

6

8

10

12

x4

y 4

Why Visual?

32D. Koop, CIS 468, Spring 2017

[F. J. Anscombe]

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Visual Pop-out

33D. Koop, CIS 468, Spring 2017

[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]

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Visual Pop-out

34D. Koop, CIS 468, Spring 2017

[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]

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Visual Pop-out

35D. Koop, CIS 468, Spring 2017

[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]

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Visual Perception Limitations

36D. Koop, CIS 468, Spring 2017

[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]

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Visual Perception Limitations

37D. Koop, CIS 468, Spring 2017

[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]

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Another Test• https://www.youtube.com/watch?v=0grANlx7y2E

38D. Koop, CIS 468, Spring 2017

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Other Human Limitations• Visual working memory is small • Change blindness: Large changes go unnoticed when we are

working on something else in our view

39D. Koop, CIS 468, Spring 2017

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Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively”

40D. Koop, CIS 468, Spring 2017

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Design Iteration

41D. Koop, CIS 468, Spring 2017

http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

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Design Iteration

42D. Koop, CIS 468, Spring 2017

http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

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Design Iteration

43D. Koop, CIS 468, Spring 2017

http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html

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Another Design Example

44D. Koop, CIS 468, Spring 2017

[M. Stefaner, 2013]

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Definition

“Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively”

45D. Koop, CIS 468, Spring 2017

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Why Effectiveness?• “It’s not just about pretty pictures” • Any depiction of data requires the designer to make choices about

how that data is visually represented - Analogy to photography - Lots of possibilities (see quarterback study)

• Effectiveness measures how well the visualization helps a person with their tasks - How? insight, engagement, efficiency? - Benchmarks and user studies

46D. Koop, CIS 468, Spring 2017

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Effectiveness

47D. Koop, CIS 468, Spring 2017

[S. Hayward, 2015]

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Effectiveness

48D. Koop, CIS 468, Spring 2017

[@bizweekgraphics]

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Effectiveness

49D. Koop, CIS 468, Spring 2017

[S. Hayward, 2015]

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Why?

How?

What?

Why?

How?

What?

Why?

How?

What?

Analyzing Visualization

50D. Koop, CIS 468, Spring 2017

[Munzner (ill. Maguire), 2014]

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How do we create modern visualizations?

51D. Koop, CIS 468, Spring 2017

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Tools• Desktop Applications:

- Excel (see excelcharts.com) - Tableau - …

• Programming Frameworks - Processing - OpenFrameworks - d3.js - …

• Advantages and disadvantages - Speed, customization, understanding

52D. Koop, CIS 468, Spring 2017

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Overview Examples Documentation Source

Data-Driven Documents

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data tolife using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities ofmodern browsers without tying yourself to a proprietary framework, combining powerful visualizationcomponents and a data-driven approach to DOM manipulation.

See more examples.

d3.js

53D. Koop, CIS 468, Spring 2017