History and Principles of Data Visualization (CMSC 34900-1 Topics - - PowerPoint PPT Presentation

History and Principles of Data Visualization

(CMSC 34900-1 Topics in Scientific Computing; Autumn 2014)

http://people.cs.uchicago.edu/~glk/class/HistoVis/

Gordon Kindlmann

Sept 30, 2014

How to learn about a set of numbers?

• Sets I, II, III, IV of (xi,yi)

have identical:

• • mean, variance {xi}
• • mean, variance {yi}
• • line of best fit

(least-squares sense)

Summary statistics

Anscombe’s quartet

http://en.wikipedia.org/wiki/Anscombe's_quartet

Anscombe’s message

• “A computer should make both calculations and
• graphs. Both sorts of output should be studied; each

will contribute to understanding”

• “Thought and ingenuity devoted to devising good

graphs are likely to pay off”

• “In practice,we do not know that the theoretical

description is correct, we should generally suspect that it is not, and we cannot therefore heave a sigh

• f relief when the regression calculation has been

made, knowing that statistical justice has been done.”

• (i.e. If you’re doing computations on data, you need to

see what you’re doing!)

• [Anscombe-GraphsInStatAn-1973]

REPUBLICAN DEMOCRAT

2012 Presidential Election

http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money

2012 Presidential Election

http://gizmodo.com/5960290/this-is-the-real-political-map-of-america-hint-we-are-not-that-divided

2012 Presidential Election

http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money

2012 Presidential Election

http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money

2012 Presidential Election

http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money

Clarifying distortions

http://briankerr.wordpress.com/2009/06/08/connections/ http://en.wikipedia.org/wiki/Harry_Beck

Tube map from 1908

Clarifying distortions

Harry Beck 1933

http://briankerr.wordpress.com/2009/06/08/connections/ http://en.wikipedia.org/wiki/Harry_Beck

Clarifying distortions

Joachim Böttger, Ulrik Brandes, Oliver Deussen, Hendrik Ziezold,

“Map Warping for the Annotation of Metro Maps”

IEEE Computer Graphics and Applications, 28(5):56-65, 2008

Clarifying distortions

Maps reflect conventions, choices, and priorities

“A single map is but one of an indefinitely large number of maps that might be produced for the same situation or from the same data.” Mark Monmonier “How to Lie with Maps”, 1991

Showing population flux

http://www.datapointed.net/2011/04/maps-us-population-change-2000-2010-census/ moving in moving out

Note use of (roughly)

• pponent hues in

colormap, centered around gray (neutral) to indicate zero

Different tasks for colormaps

Value of showing isocontours

Quality/Utility of colormap hinges on perceptual psychology

Affordances

http://www.kkstudio.gr/#the-uncomfortable “uncomfortable” object design by Katarina Kamprani Our experiences of the affordances in design is also part of psychology

Three main bodies of knowledge

• Cartography / Geography
• Statistics
• Psychology

Fields of Visualization

Information Visualization Scientific Visualization Data Visualization

Info- graphics Scientific Illustration

Statistics, Machine Learning Calculus, Numerical Methods

Computer Science

Computer Graphics Human-computer interaction

Perceptual Psychology

This class:

• Goal: understand the underlying principles

at play in data visualization (practice & research), and their history

• How:
• 1) Read, present, discuss the commonly

cited literature and its context

• 2) Do a project implementing a vis

method

• Why this class?

What is being visualized?

• Data = set of values (or datum) X
• Spreadsheet: {Xi}i=1..N; Xi=(ai,bi,ci,...)

coordinates may be spatial or geographical

• Function of time: X = F(t)
• Function over 2D X = F(u,v) i.e. an image,
• r volume F(u,v,w), or 3D surface F(s,t)
• Graph: X = (Vert,Edge) or (Vert,Arrow)
• Each X is a label or number (or vector of them)
• Each different type (or flavor) of number

has its own mathematical structure: “scales of measurement”

Scales of measurement

Stevens’ 4 scales of measurements

Nominal

Categorical Qualitative

Ordinal Interval Ratio

• Later scales specialize earlier scales
• Some examples of these ...

http://en.wikipedia.org/wiki/Level_of_measurement

The structure of data values

• Categorical
• Ordinal
• Interval
• Ratio

discrete continuous

Scalars Vectors Tensors

Understanding the nature of the data helps choose sensible ways to show it

quali- tative

quantitative

Value of showing isocontours

Fields of Visualization related to data

Information Visualization Scientific Visualization

{Xi}i=1..N; Xi=(ai,bi,ci,...) X = F(t) X = (Vert,Edge) X = F(u,v) X = F(u,v,w) X = F(s,t) (3D surface) X : vectors, tensors