Interaction For Visualization Harvard, 2015 Jean-Daniel Fekete - - PDF document

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Interaction For Visualization Harvard, 2015 Jean-Daniel Fekete - - PDF document

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2/25/2015 Interaction For Visualization Harvard, 2015 Jean-Daniel Fekete INRIA Thanks to Pierre Dragicevic , John Stasko and Yvonne Jansen for sharing some slides Coverage of this Lecture Interaction in information visualization This

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Interaction For Visualization

Harvard, 2015 Jean-Daniel Fekete

INRIA

Thanks to Pierre Dragicevic, John Stasko and Yvonne Jansen for sharing some slides

Coverage of this Lecture

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Interaction in information visualization This lecture

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Coverage of this Lecture

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Interaction in information visualization This lecture

Why interact?

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Why interact?

 Perception requires action

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Lederman and Klatzky, 1987 (link)

Why interact?

 Perception requires action

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Vogt and Magnussen 2007 (link)

Eye movements of a layperson Eye movements of an artist

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Why interact?

 Perception requires action

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Valdis Krebs (link)

Why interact?

 Perception requires action

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Photo appaloosa (link)

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Why interact?

 Perception requires action

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Bret Victor (link)

Why interact?

 Perception requires action

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Bret Victor (link)

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Why interact?

 Is this interacting?

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Definition of interaction

 Static content  Dynamic content

  • Animated content

Change independently from the user

  • Interactive content

Change as a result of user actions

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Definition of interaction

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Why interact with a computer?

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Shoestring budget travel guide 2012

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Shoestring budget travel guide 2012

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Shoestring budget travel guide 2012

Why interact with a computer?

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 There is too much to be shown  There are many ways to show it  Let the user dynamically control what to show and how to show it 

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Example 1: Dynamic Queries

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Williamson and Shneiderman, 1992

Example 1: Dynamic Queries

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Williamson and Shneiderman, 1992

1:29

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Example 2: Fisheye Views

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Sarkar and Brown, 1992

Example 2: Fisheye Views

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Sarkar and Brown, 1992 (see also Furnas, 1986)

1:08

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Example 3: Brushing

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Beker and Cleveland, 1987

Example 3: Brushing

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Beker and Cleveland, 1987

17:50

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Taxonomies of interaction

 What?

  • What is the user doing?

 Why?

  • Why is the user doing it?

 How?

  • How is the user doing it?

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The Visualization Pipeline

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The Visualization Pipeline

Raw Data Selection Representation Presentation Interaction From [Spence, 2000]

The Visualization Pipeline

The Visualization Pipeline

Data Analytics Abstraction Spatial Layout Presentation View

Data Transformation Spatial Mapping Transformation Presentation Transformation View Transformation From [Card et al., Readings in Information Visualization]

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The Visualization Pipeline

[Card, Mackinlay, Shneiderman, Readings in Information Visualization: Using Vision to Think, 1999]

From Ed CHI Illustration de J. Heer Interaction

The Visualization Pipeline

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond) Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond) Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond) Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond) Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

Taxonomies of interaction

 What?

  • What is the user doing?

 Why?

  • Why is the user doing it?

 How?

  • How is the user doing it?

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Tasks

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Analytical Tasks

 Shneiderman, 1996:

  • 1. Overview: Gain an overview of the entire

collection

  • 2. Zoom : Zoom in on items of interest
  • 3. Filter: Filter out uninteresting items
  • 4. Details-on-demand: Select an item or

group and get details when needed

  • 5. Relate: View relationships among items
  • 6. History: Keep a history of actions to support

undo, replay, and progressive refinement

  • 7. Extract: Allow extraction of sub-collections

and of the query parameters

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Analytical Tasks

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  • 1. Overview

Stephen Few, 2006 (link) Software: TimeSearcher 2

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Analytical Tasks

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Stephen Few, 2006 (link) Software: TimeSearcher 2

2-3. Zoom and Filter

Analytical Tasks

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Stephen Few, 2006 (link) Software: TimeSearcher 2

2-3. Zoom and Filter

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Analytical Tasks

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Stephen Few, 2006 (link) Software: TimeSearcher 2

  • 4. Details on demand

Analytical Tasks

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 Visual Information Seeking Mantra

(Shneiderman, 1996)

Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand Overview first, zoom and filter, then details on demand

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Analytical Tasks

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 Amar, Eagan and Stasko, 2005

  • Retrieve Value
  • Filter
  • Compute Derived Value
  • Find Extremum
  • Sort
  • Determine Range
  • Characterize Distribution
  • Find Anomalies
  • Cluster
  • Correlate

Analytical Tasks

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 Yi et al, 2007

  • 1. Select: mark something as interesting
  • 2. Explore: show me something else
  • 3. Reconfigure: show me a different arrangement
  • 4. Encode: show me a different representation
  • 5. Abstract/Elaborate: show me more or less

detail

  • 6. Filter: show me something conditionally
  • 7. Connect: show me related items
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Taxonomies of interaction

 What?

  • What is the user doing?

 Why?

  • Why is the user doing it?

 How?

  • How is the user doing it?

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How?

 Interaction technique

  • “An interaction technique is the fusion of

input and output, consisting of all software and hardware elements, that provides a way for the user to accomplish a task”

(Tucker, 2004)

 Types of interaction techniques

  • Input: mouse, touch, keyboard, speech,...
  • Shneiderman: Command-line interfaces
  • vs. Direct manipulation interfaces

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Interaction Styles

 Command line interface

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Select house-address From atl-realty-db Where price >= 200,000 and price <= 400,000 and bathrooms >= 3 and garage == 2 and bedrooms >= 4

Interaction Styles

 (In)Direct manipulation

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How?

 Interaction technique

  • “An interaction technique is the fusion of

input and output, consisting of all software and hardware elements, that provides a way for the user to accomplish a task”

(Tucker, 2004)

 Types of interaction techniques

  • Input: mouse, touch, keyboard, speech,...
  • Shneiderman: Command-line interfaces
  • vs. Direct manipulation interfaces
  • Beaudouin-Lafon: Instruments with

different degrees of directness

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Taxonomies of interaction

 What?

  • What is the user doing?

 Why?

  • Why is the user doing it?

 How?

  • How is the user doing it?

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Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement

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Problem

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FilmFinder, HCIL

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Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement

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Filtering Techniques

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 Dynamic Queries

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

Filtering Techniques

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 Visual-Level Dynamic Queries

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Filtering Techniques

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 Dynamic Queries + Zooming

Spotfire Software

Filtering Techniques

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 Dynamic Queries Specified Visually

Time Searcher (Hocheiser, 2003)

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Filtering Techniques

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 Dynamic Queries for Volumetric Data

Sherbondy et al, 2004

Filtering Techniques

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 Incremental Text Search

Name Voyager (Wattenberg, 2005)

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Problem

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Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement

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Navigation Techniques

 Pan & Zoom  Focus + Context

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Pan & Zoom

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Pan & Zoom

70 Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

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Pan & Zoom

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Pan & Zoom

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Pan & Zoom

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 Semantic Zoom

Pan & Zoom

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 Semantic Zoom

Bade et al, 2004 (link)

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Pan & Zoom

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 Space-Scale Diagrams

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  • 1. Pan
  • 2. Zoom
  • 3. Pan and zoom

1. 2. 3.

Furnas and Bederson, 1995 Space-Scale Diagrams: Understanding Multiscale Interfaces (link)

Problem

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Where am I?

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Navigation Techniques

 Pan & Zoom  Focus + Context

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Focus + Context

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 Space Distorsion

  • Fisheye Views of Graphs

Sarkar and Brown, 1992

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Focus + Context

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 Space Distorsion

  • Fisheye Menus

Bederson, 2000

Focus + Context

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 Space Distorsion

  • Perspective Wall

Mackinlay, Roberston and Card, 1991

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Focus + Context

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 Space Distorsion

  • Melange

Elmqvist et al, 2010

Focus + Context

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 Space Distorsion

  • Melange

Brosz, Carpendale and Nacenta, 2011

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Focus + Context

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 Table Lens

Rao and Card, 1994

Focus + Context

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 Generalized Fisheye Views

Furnas, 1986 Generalized Fisheye Views

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Focus + Context

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 Generalized Fisheye Views

Furnas, 2010 A Fisheye Follow-Up: Further Reflections on Focus + Context

Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement

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Multiple Views

88 Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

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Multiple Views

Overview + Detail Magic Lenses Coordinated Views Animated Transitions

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Problem

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Where am I?

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Multiple Views

Overview + Detail Magic Lenses Coordinated Views Animated Transitions

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Overview + Detail

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Panning a large graph

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Overview + Detail

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Panning a line chart

Overview + Detail

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Browsing Multiple Views

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Overview + Detail

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Browsing Multiple Views

Jansen et al, 2013

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Rogowitz and Treinish, 1995

Problem

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Multiple Views

Overview + Detail Magic Lenses Coordinated Views Animated Transitions

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Magic Lenses

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Bier et al, 1993

(Manfred’s Talk)

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Magic Lenses

Movable filters for dynamic queries

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Fishkin and Stone, 1995

Magic Lenses

Exentric Labeling

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Fekete and Plaisant, 1999

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Magic Lenses

Color lenses

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Elmqvist et al, 2010

Magic Lenses

Edge lenses

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Wong, Carpendale and Greenberg,

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Problem

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Heer and Roberston, 2007

Multiple Views

Overview + Detail Magic Lenses Coordinated Views Animated Transitions

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Coordinated Views

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Beker and Cleveland, 1987

Brushing & Linking Scatterplots

Voigt, 2002

Coordinated Views

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Brushing Parallel Coordinates

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Coordinated Views

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Brushing Parallel Coordinates

Coordinated Views

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Brushing & Linking Histograms

Chris North, 2001

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Coordinated Views

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Brushing & Linking Everything

Turkay et al, 2010

Coordinated Views

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Colored Brushing & Linking

Chris North, 2001

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Coordinated Views

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Linking with Dynamic Queries

Spotfire Software

Problem

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Heer and Roberston, 2007

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Multiple Views

Overview + Detail Magic Lenses Coordinated Views Animated Transitions

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Animated Transitions

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Heer and Roberston, 2007

00:19

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Animated Transitions

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With coordinated selection and edition

Histomages (Chevalier et al, 2012)

Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement

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Rearrangement

Interactive Stacked Histograms

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Dix and Ellis, 1998

Rearrangement

Interactive Stacked Histograms

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Dix and Ellis, 1998

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

Rearrangement

Time-Series Alignment

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Lifelines 2 (Wang et al, 2008)

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Rearrangement

Sorting

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Rao and Card, 1994

Rearrangement

Matrix Reordering

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Bertin, 1977

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Rearrangement

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Star Coordinates

Candogan, 1992. Video from Lehman and Theisel, 2013.

Rearrangement

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Dust & Magnet

Yi and al, 2005

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Rearrangement

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 Dust & Magnet

Yi and al, 2005

01:46

Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement

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Families of infovis interaction techniques

 Filtering techniques  Navigation techniques  Multiple views  Rearrangement  Pitfalls  Beyond the desktop

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Pitfalls

#1 - Interaction has a cost

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130

Pitfalls

#2 - Controls take screen real-estate

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Pitfalls

#3 - Few other techniques are self- explanatory

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touch devices

Sadana and Stasko, 2013

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tabletop devices

Isenberg and Carpendale, 2008

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134

wall-sized displays

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[Jansen et al., Tangible Remote Controller for Wall-sized Displays. CHI’12]

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Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

Jansen and Dragicevic 2013 (www.aviz.fr/beyond)

(view level) (data level) (visual level)

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[Isenberg et al. , Hybrid Images for Large Viewing Environments, InfoVis’13] [Isenberg et al. , Hybrid Images for Large Viewing Environments, InfoVis’13]

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 Interaction with the physical world

140 141

physical visualizations

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142

[Mark Wilson. How GM is saving cash using legos as a data viz tool. April 2012]

tinyurl.com/physvis

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[Kruszynski & van Liere, Tangible Props for Scientific Visualization, Virtual Reality 13 (4) 2009]

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14 4

[Stefaner & Hemmert, emoto data sculpture, http://www.nand.io/visualisation/emoto-installation]

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[PARM: Projected Augmented Relief Models, University of Nottingham, 2012]

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Relief (Leithinger et al, 2009)

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Relief (Leithinger et al, 2009)

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Inform (Leithinger et al, 2013)

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