H517 Visualization Design, Analysis, & Evaluation Week 9: - - PowerPoint PPT Presentation

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H517 Visualization Design, Analysis, & Evaluation Week 9: - - PowerPoint PPT Presentation

Apr 08, 2024 752 likes •1.29k views

H517 Visualization Design, Analysis, & Evaluation Week 9: Multiple views + Interaction Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI http://www.ufoera.com/images/ufo/ufo-hotspots-map_117.png http://popvssoda.com

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H517 Visualization Design, Analysis, & Evaluation

Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

Week 9: Multiple views + Interaction

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http://www.ufoera.com/images/ufo/ufo-hotspots-map_117.png

http://popvssoda.com

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Con-nuous maps

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Maps

  • Landmarks
  • Discrete data
  • Choropleth
  • Con5nuous data
  • Projec5on
  • Cartograms
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Cartogram

A map in which areas are scaled and distorted rela5ve to a data aFribute

Land Area Emile Levasseur, 1868

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Rectangular cartogram

Wikipedia

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Brush vs Kerry, 2004

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http://www-personal.umich.edu/~mejn/cartograms/

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http://www-personal.umich.edu/~mejn/cartograms/

Popula-on

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http://www-personal.umich.edu/~mejn/cartograms/

GDP

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http://www-personal.umich.edu/~mejn/cartograms/

people living with HIV/AIDS

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http://www-personal.umich.edu/~mejn/cartograms/

spending on healthcare

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Multiple views

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Views

Varia-on: show the data in different ways Eye over memory: use display space instead of working memory

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One form, multiple views

Par55on data into subsets and distribute among different views Visual Encoding is the same in all views

Small Multiples

Nick Elprin, Domino

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Small-Multiples

Spark Lines

Viz Wiz

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Small-Multiples

Drought, 1898-2012

Mike Bostock

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Small-Multiples

ScaFerplot Matrix

Mike Bostock

x y z w w z y x

Example: dataset with four variables: X, Y, Z, W Par55on aFributes (or variables) and distribute them among different views

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Multi form

Show mul5ple representa5ons of the data Usually the views share the same data Views have different visual encoding (and oVen depict different aFributes) Ra-onale: it is difficult to show all aFributes in a single monolithic view. Mul5form views give us freedom to use different visual encodings for different aFributes.

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Multi form

Based on a slide by Miriam Meyer and Alex Lex

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Multi form

MizBee

Meyer, 2009

Same data, but different scales

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View Linking

Views cab be linked implicitly through interac5ons Changes in one view are coordinated to all

  • ther views
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Brushing and Linking

Mike Bostock

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Brushing and Linking

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Explicit Linking

Views can be linked explicitly through visual links Links typically connect the same (or similar) data items in different views Limita-ons: can occlude and lead to visual cluFer, although smart algorithms can route links to minimize side effects

Steinberger et al., 2011 Geymayer et al, 2014

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Details on Demand

Showing addi5onal informa5on with popup views

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Layering

Embedding Views in the same space

NodeTrix, Henry abd Fekete, 2007

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Layering: Treemap

http://ukdataexplorer.com/co2/

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Layering: Treemap

https://finviz.com/map.ashx

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Disk Inventory X

Layering: Treemap

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Domino

Graz et al, 2014

Dynamic View creation and linking

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Interaction

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Why interact with visualizations?

  • Explore data that is big / complex
  • Won’t fit within the visualiza5on
  • Look at different representa5ons of the same

data

  • Interac-on engages our cogni-on
  • We understand things beFer when we “play”

with them

  • Allows us to observe cause-and-effect

rela5onships beFer

Based on a slide by Alex Lex

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Types of Interaction

Based on a slide by Alex Lex

Single View

  • Naviga5on
  • Focus+Context
  • Filtering and

Querying

Mul-ple Views

  • Brushing & Linking
  • Details on Demand
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Navigation

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Navigation

Pan and Zoom

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Navigation

Pan, Zoom, Rotate

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Semantic Zoom

  • Content updates as

you zoom in

  • More detail as more

space becomes available

[McLachlan 2008] Via Alex Lex

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Overview+Detail

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Overview+Detail

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Limitations of Pan and Zoom Navigation

  • Pros
  • Intui5ve and familiar
  • Fast to use if you know the target
  • Cons
  • Can get lost in the details and loose track of context
  • Visually disrup5ve to the “mental map”
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Focus+Context

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Fisheye lenses

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Fisheye lenses

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Fisheye lenses

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Distortion

  • Pros
  • Context plus Focus
  • Less disrup5ons to the “mental map”
  • Cons
  • Not suitable for rela5ve spa5al judgments
  • Target Acquisi5on problem
  • Not intui5ve compared to pan-and-zoom interfaces

Based on a slide by Alex Lex

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Information Seeking Mantra

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 Ben Shneiderman

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Information Seeking Mantra

Ben Shneiderman