H517 Visualization Design, Analysis, & Evaluation Week 6: Marks & Channels (cont’d) Tables and multi-dimensional data
Khairi Reda | [email protected] School of Informa cs & Compu ng, IUPUI Administrativia
• Coming up next week: Project 1 presentations
• 4 min presentation each + 45 sec Q&A (sharp limit!)
• No need to prepare PowerPoint slide, just bring up your vis and show it to class
• You’ll not be allowed to use your own laptop; need to be able to access the vis through a public URL
• Demo the vis, talk about your design process, challenges encountered and how you addressed them
• Audience: ask question, give feedback, critique the vis; always provide constructive comments Last week… Marks Graphical elements in an image
points (0D) lines (1D) areas (2D) volume clouds (3D) Channels (aka Visual Variables) Parameters that control the appearance of marks based on a ributes magnitude channels iden ty channels good for ordered attributes good for categorical attributes
Tamara Munzner Via Miriah Meyer How much longer?
4x
Alex Lex How much larger (area)?
5x
Alex Lex Psychophysics Steven’s Psychological power law
perceived sensation S = In physical intensity Tamara Munzner Via Miriah Meyer Heer & Bostock, 2010 Discriminability can channel differences be discerned?
Via Miriah Meyer Position Offers very good discriminability
posi on Position But this doesn’t extend to 3D!
Perspec ve distor on Occlusions Factors affecting accuracy of Length/Position judgement
aligned unaligned stacked bar chart (unaligned) Separable vs Integral channels
separable channels: can be judged individually integral channels: are viewed holis cally
separable integral
Ware 2004 Based on a slide by Miriah Meyer Chernoff faces Chernoff faces This week
Tables and multi-variate data Key attribute Visualizing Tables Visualizing Tables 1 quan ta ve a ribute 1 categorical (key) 2 quan ta ve a ributes Visualizing Tables
key attribute
key attribute Don’t use line charts for categorical a ributes!
ok: “Men are taller than bad: “The more male a women (on average)” person is, the taller he is”
ok: “Twelve year olds are ok: “The older a person taller than ten years old” the taller he/she is” Miriah Meyer Arrange Tables Mul ple columns/categories
Streit & Gehlenborg, PoV, Nature Methods, 2014 Via Alex Lex Arrange Tables 2 quan ta ve a ributes
Y
X
Z Z
X Y
What if we have and want to see more than 2 quantitative attributes at the same time? nine characteristics of Abalone (sea snails)
Wilkinson et al., 2005 Via Miriah Meyer Wilkinson et al., 2005 Via Miriah Meyer Wilkinson et al., 2005 Via Miriah Meyer Parallel Coordinates
V1 V2 V3 V4 V5
10 8 6 4 2 0
Example by Miriah Meyer Parallel Coordinates
posi ve correla on straight lines
nega ve correla on all lines cross at a single point
Wegman 1990 Via Miriah Meyer Parallel Coordinates
ProtoVis Via Miriah Meyer Do you see any correlation?
Correla ons only visible between neighboring axis pairs:
axis order ma ers
allow user to reorder axis
Fua 1999 Via Miriah Meyer Hierarchical Parallel Coordinates
Fua 1999 Hierarchical Parallel Coordinates
Instead of showing all points, show a band represen ng a cluster:
mean: opaque line
min/max: illustrated by band width with decreasing opacity from mean cluster
Fua 1999 Hierarchical Parallel Coordinates Cluster: lines that share similar shapes. Interac vely varying the similarity threshold allows us to “unpack” clusters
Fua 1999
Radial Layout Star Plot Similar to parallel coordinates, but axes radiate from a common origin
Scotch Whiskies
Via Alex Lex Arrange Tables Arrange Tables Table as a heatmap
1 2 5 4
5 0 0 1
5 6 1 2
2 1 3 1
4 1 2 1 Arrange Tables Table as a heatmap
1 2 5 4
5 0 0 1
5 6 1 2
2 1 3 1
4 1 2 1 Arrange Tables Table as a heatmap
0 0 Arrange Tables Table as a heatmap
Order is important: Clustering is o en used with heatmaps Next week
Project 1 presenta ons