N328 Visualizing Information Multiple Views + Interactions Week 9: - PowerPoint PPT Presentation

N328 Visualizing Information Multiple Views + Interactions Week 9: Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

Multiple views

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

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

Small-Multiples Spark Lines Viz Wiz

Small-Multiples Drought, 1898-2012 Mike Bostock

Small-Multiples ScaFerplot Matrix w z y x Par55on aFributes (or x variables) and distribute them among different views y Example: dataset with z four variables: X, Y, Z, W w Mike Bostock

Multi form Show mul5ple representa5ons of the data Usually the views share the same data Views have different visual encoding (and oQen 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.

Multi form Based on a slide by Miriam Meyer and Alex Lex

Multi form MizBee Same data, but different scales Meyer, 2009

View Linking Views cab be linked implicitly through interac5ons Changes in one view are coordinated to all other views

Brushing and Linking Mike Bostock

Brushing and Linking

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

Details on Demand Showing addi5onal informa5on with popup views

Layering Embedding Views in the same space NodeTrix , Henry abd Fekete, 2007

Layering: Treemap http://ukdataexplorer.com/co2/

Layering: Treemap https://finviz.com/map.ashx

Layering: Treemap Disk Inventory X

Domino Dynamic View creation and linking Graz et al, 2014

Interaction

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

Types of Interaction Single View Mul+ple Views • Naviga5on • Brushing & Linking • Focus+Context • Details on Demand • Filtering and Querying Based on a slide by Alex Lex

Navigation

Navigation Pan and Zoom

Navigation Pan, Zoom, Rotate

Semantic Zoom • Content updates as you zoom in • More detail as more space becomes available [McLachlan 2008] Via Alex Lex

Overview+Detail

Overview+Detail

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”

Focus+Context

Fisheye lenses

Fisheye lenses

Fisheye lenses

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

Information Seeking Mantra Ben Shneiderman 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

Information Seeking Mantra Ben Shneiderman