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Data Visualization (CIS/DSC 468)
Definitions
Dr. David Koop
D. Koop, CIS 468, Spring 2017
What is Data Visualization?
2D. Koop, CIS 468, Spring 2017
What are the purposes of visualization?
3D. Koop, CIS 468, Spring 2017
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
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
Where have you seen visualizations?
5D. Koop, CIS 468, Spring 2017
Books / Posters
6D. Koop, CIS 468, Spring 2017
[Rock 'N' Roll is Here to Pay, R. Garofalo, 1977]
Books / Posters
6D. Koop, CIS 468, Spring 2017
[Rock 'N' Roll is Here to Pay, R. Garofalo, 1977]
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]
What is the advantage of the second version?
8D. Koop, CIS 468, Spring 2017
Interaction
9D. Koop, CIS 468, Spring 2017
Visualization Exploration and Communication
10D. Koop, CIS 468, Spring 2017
MTA Fare Data Exploration
11D. Koop, CIS 468, Spring 2017
MTA Fare Data Exploration
12D. Koop, CIS 468, Spring 2017
MTA Fare Data Exploration
13D. Koop, CIS 468, Spring 2017
MTA Fare Data Exploration
13D. Koop, CIS 468, Spring 2017
MTA Fare Data Exploration
14D. Koop, CIS 468, Spring 2017
MTA Fare Data Exploration
15D. Koop, CIS 468, Spring 2017
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
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
Textbooks
17D. Koop, CIS 468, Spring 2017
Munzner Murray
Do not cheat!
18D. Koop, CIS 468, Spring 2017
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
Do ask questions!
20D. Koop, CIS 468, Spring 2017
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
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111 ST
LINDEN B
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METROPOLITAN AV
METROPOLITAN AV
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FLUSHING AV
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MYRTLE AV
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REMSEN AV
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BLEECKER ST
BLEECKER ST
23 ST
12 AV 23 ST
50 ST 50 ST
59 ST CENTRAL PARK SOUTH
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UNIVERSITY HTS BR
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BAYCHESTER AV
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UniversityHeights
MorrisHeights
Harlem125 St
Melrose
Yankees-E153 St
Tremont
Fordham
Botanical Garden
WilliamsBridge
Woodlawn
Wakefield
LongIslandCity
9 St
14 St
23 St
33 St
Christopher St
Hunterspoint Av
Woodside
Mets–Willets Point
Flushing
ForestHills
JamaicaKewGardens
Hollis
Auburndale Bayside Douglaston
Manhasset
Plandome
PortWashington
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Broadway
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WTC
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PARK
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RIVERSIDE PARK
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TRIBECA
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KEWGARDENS
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HOWARD BEACHEASTNEWYORK
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FLATLANDS
ROCKAWAYPARK
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BOROUGHPARK
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BROOKLYNHEIGHTS
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Stapleton
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Dongan Hills
Jefferson AvGrant City
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New Dorp
Oakwood Heights S57
Bay Terrace
Great KillsS54 X7 X8
Eltingville
Annadale S55
Huguenot S55 X17 X19
Prince's Bay S56
Pleasant Plains
Richmond ValleyNassauS74/84
AtlanticS74/84
Tottenville S74/84
RICHMOND TERRACE
VICTORY BLVD
VA
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ARTHUR KILL RD
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GREATKILLSPARK
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STATENISLAND
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MARINERSHARBOR
FOXHILLS ROSEBANK
CASTLETONCORNERS
BULLSHEAD
CHELSEA
WESTERLEIGH
TODTHILL
NEWDORPBEACH
WOODROWROSSVILLE
CHARLESTON
ARDENHEIGHTS
FRESHKILLS
RICHMONDTOWN
TOTTENVILLEBEACH
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
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
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
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
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
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
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
Why Computers?
27D. Koop, CIS 468, Spring 2017
[Cerebral, Barsky et al., 2007]
Why Computers?
28D. Koop, CIS 468, Spring 2017
[Cerebral, Barsky et al., 2007]
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]
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
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]
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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Why Visual?
32D. Koop, CIS 468, Spring 2017
[F. J. Anscombe]
Visual Pop-out
33D. Koop, CIS 468, Spring 2017
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Pop-out
34D. Koop, CIS 468, Spring 2017
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Pop-out
35D. Koop, CIS 468, Spring 2017
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Perception Limitations
36D. Koop, CIS 468, Spring 2017
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Visual Perception Limitations
37D. Koop, CIS 468, Spring 2017
[C. G. Healey, http://www.csc.ncsu.edu/faculty/healey/PP/]
Another Test• https://www.youtube.com/watch?v=0grANlx7y2E
38D. Koop, CIS 468, Spring 2017
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
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
Design Iteration
41D. Koop, CIS 468, Spring 2017
http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html
Design Iteration
42D. Koop, CIS 468, Spring 2017
http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html
Design Iteration
43D. Koop, CIS 468, Spring 2017
http://kpq.github.io/chartsnthings/2013/09/19-sketches-of-quarterback-timelines.html
Another Design Example
44D. Koop, CIS 468, Spring 2017
[M. Stefaner, 2013]
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
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
Effectiveness
47D. Koop, CIS 468, Spring 2017
[S. Hayward, 2015]
Effectiveness
48D. Koop, CIS 468, Spring 2017
[@bizweekgraphics]
Effectiveness
49D. Koop, CIS 468, Spring 2017
[S. Hayward, 2015]
Why?
How?
What?
Why?
How?
What?
Why?
How?
What?
Analyzing Visualization
50D. Koop, CIS 468, Spring 2017
[Munzner (ill. Maguire), 2014]
How do we create modern visualizations?
51D. Koop, CIS 468, Spring 2017
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
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
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