1 / 27

Information Visualization (Part 1)

Information Visualization (Part 1). cs3724: HCI. The Problem. Data. Human. Data Transfer. Goal: Insight How?. Human Vision. Highest bandwidth sense Fast, parallel Pattern recognition Pre-attentive Extends memory and cognitive capacity (Multiplication test)

quant
Download Presentation

Information Visualization (Part 1)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Information Visualization(Part 1) cs3724: HCI

  2. The Problem Data Human Data Transfer Goal: Insight How?

  3. Human Vision • Highest bandwidth sense • Fast, parallel • Pattern recognition • Pre-attentive • Extends memory and cognitive capacity • (Multiplication test) • People think visually Impressive. Lets use it!

  4. Find the Red Square:

  5. Which state has highest Income? • Relationship between Income and Education? • Outliers?

  6. College Degree % Per Capita Income

  7. Scenarios = Data + Tasks • Data categories: • Spatial (1,2,3D) • Multi-dimensional • Tree • Network • Text, documents

  8. User Tasks Excel can do this • Easy stuff: (1 or few items) • Min, max, average, % • Exact queries, known item search • Hard stuff: • Patterns, trends, distributions, changes over time, • outliers, exceptions, • relationships, correlations, multi-way, • combined min/max, tradeoffs, • clusters, groups, comparisons, context, • anomalies, data errors, • Paths, … Visualization can do this!

  9. DataMaps • demo

  10. Spotfire • Mapping data to graphics (x, y, size, color, shape…) • Multiple views: brushing and linking • Dynamic Queries • Details window Cars data

  11. Visual Mapping: Step 1 • Map: data items  visual marks Visual marks: • Points • Lines • Areas • Volumes • Glyphs

  12. Visual Mapping: Step 2 • Map: data items  visual marks • Map: data attributes  visual properties of marks Visual properties of marks: • Position, x, y, z • Size, length, area, volume • Orientation, angle, slope • Color, gray scale, texture • Shape • Animation, time, blink, motion

  13. Example: Spotfire • Film database • Film  dot • Year  x • Length  y • Popularity  size • Subject  color • Award?  shape

  14. TableLens (Eureka by Inxight) • Visual encoding of cell values • Details expand within context (fisheye) • Sorting Cars data

  15. Focus+Context • Hyperbolic Tree (star tree) • Radial; shrink with distance to center • Drag to navigate • Scalability? • Xerox PARC, Inxight • http://startree.inxight.com/

  16. Treemaps on the Web • Map of the Market: http://www.smartmoney.com/marketmap/ • People Map: http://www.truepeers.com/ • Coffee Map: http://www.peets.com/tast/11/coffee_selector.asp

  17. “Squarified” TreeMap • http://www.research.microsoft.com/~masmith/all_map.jpg

  18. SequoiaView • http://www.win.tue.nl/sequoiaview/ • HW #3

  19. Context is Important!

  20. Information Visualization 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

  21. What is Information Visualization? The use of computer-supported, interactive, visual representations of abstract data to amplify cognition

  22. My definition: Show me the data!

  23. Keys points • Power of visual system • scenario = data + tasks • Mapping data to graphics & visual properties • Interaction for what doesn’t fit in visual rep. • Examples: multi-dimensional, trees • Mantra: Overview first • Choice of representation matters

  24. What’s the Big Deal?

  25. Presentation is everything!

More Related