Information Visualization

Dec 2, 2016

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Slides based on materials from Chris Collins, Jeremy Bradbury, Sheelagh Carpendale, and Ilona Posner. Thank you!!

Milestone III

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Questions?

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Today

Information Visualization

Building blocks

Interaction concerns

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Disclaimer

Bertin considers:

printable, on white paper,

visible at a glance

reading distance of book or atlas

normal and constant lighting

readily available graphic means

Implications?

Good as a guideline, we need to consider context and medium of visualization use.

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Visual Variables

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Characteristics of Visual Variables

selective

associative

quantitative

order

length

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> > > > > > >>

Interaction

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Visualization Pipeline

(Card, Mackinlay, and Shneiderman, 1999)

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Why Interaction?

Datasets are too large to:

display in one view

comprehend in entirety

Interest in only subset of the data

Interest in different views of the data

Extract relevant information & transform

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Typical Interaction Techniques

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Select – mark something as interesting

Explore – show me something else

Reconfigure – show me a different arrangement

Encode – show me a different representation

Abstract/Elaborate – more or less detail

Filter – show me something conditionally

Connect – show me related itemsFall 2016

Selection

Mark something as interesting

Often combined with other techniques

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Reconfigure

Show a different arrangement

Move data items to

Enable better comparison

Avoid occlusion

Correspond to some mental model of the data

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Cone Trees Table Lens[Robertson et al., 1991]

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Filter

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Show subset of data based on condition

e.g.,

by selecting a data range

or filtering based on distance from focus

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Connect

Show related items

Single view

Heer & boyd, InfoVis 2005

Multiple viewCollins & Carpendale, InfoVis 2007

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“Overview first, zoom and filter, then details-on-

demand”

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Shneiderman, 1996

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Visualization Design

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Data Processing is Task Dependent

What are the information needs?

What errors may be introduced by processing?

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Example

The data below shows two translations (in blue) from different algorithms, for the Spanish text (in black).

word-word correspondence in superscript

numbers in brackets are the uncertainty

What tasks would this data support?

El1 español2 es3 la4 lengua5 más6 hablada7 del8 mundo9 tras10 el11 chino12 mandarín13

por14 el15 número16 de17 hablantes18 que19 la20 tienen21 como22 lengua23 materna24.

Spanish1,2 (0.90) is3 (0.90) the4 (0.94) language5 (0.39) most6 (0.25) spoken7 (0.52) [about the]8

(0.30) world9 (0.93) following10 (0.64) the11 (0.72) Chinese12 (0.87) Mandarin13 (0.80) for14 (0.24)

the15 (0.77) number16 (0.79) of17 (0.99) speakers18 (0.82) who19 (0.80) take21 (0.21) it20 (0.96) [as a]22 (0.46) mother24 (0.35) tongue23 (0.25)

Spanish1,2 (0.90) is3 (0.90) the4 (0.83) most6 (0.55) spoken7 (0.73) language5 (0.44) [in the]8 (0.89)

world9 (0.94) after10 (0.73) Chinese11,12 (0.80) Mandarin13 (0.88) by14 (0.41) the15 (0.79) number16

(0.94) of17 (0.75) speakers18 (0.84) that19 (0.62) have21 (0.22) as22 (0.40) their20 (0.10) mother24 (0.37)

tongue23 (0.18).

....

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Principles of Graphical Excellence

Complex ideas presented with clarity, precision, honesty, and efficiency.

Gives viewer the greatest number of ideasin the shortest time, with the least ink in the smallest space.

Graphical excellence often found in simplicity of design and complexity of data.

Bottom line: how you present the data will impact the conclusions people draw from it

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Integrity

6,990? 4,000?2,000?

?

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Lie Factor 14.8

(Tufte, 2001)

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Visualization Pitfalls

Data Design

Select the right data dimensions

Pitfall: Display irrelevant data relationships

Visual Design

Consider perceptual capabilities

Pitfall: Difficult to interpret, lead users to misinterpretation

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Visualization Pitfalls

Interface Design

Mode of interaction with visual and data appropriate

Pitfall: Poor interaction negates benefits from data and visual design

Understanding Target Users

Visualization design accounts for stakeholder needs and characteristics

Pitfall: Mistaken assumptions of cultural norms or user abilities leads to misinterpretation

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Lots of Cool Examples

e.g., www.visualcomplexity.com

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Today’s Messages: Information Visualization

Augments human cognition by giving people tools to represent and manipulate data

Becoming increasingly important as data sets continue to grow (in both size and type)

Lots of open research questions

Great HCI speciality for core CS people

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