Data Visualization (CIS 468)

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1 Data Visualization (CIS 468) Marks & Channels Dr. David Koop

2 Visual Encoding How should we visualize this data? Name Region Population Life Expectancy Income China East Asia & Pacific India South Asia United States America Indonesia East Asia & Pacific Brazil America Pakistan South Asia Bangladesh South Asia Nigeria Sub-Saharan Africa Japan East Asia & Pacific Mexico America Philippines East Asia & Pacific Vietnam East Asia & Pacific Germany Europe & Central Asia Ethiopia Sub-Saharan Africa Turkey Europe & Central Asia

3 Potential Solution Bubbles FACTS TEACH ABOUT HOW TO USE Share! " # $ English years Color World Regions Select Search... Afghanistan Life expectancy Albania Algeria Andorra Angola Antigua and Barbuda Size Population, total Zoom 100% OPTIONS EXPAND PRESENT [Gapminder, Wealth & Health of Nations] 3

4 Another Solution Bubbles FACTS TEACH ABOUT HOW TO USE Share! " # $ English Size: Population, total Color World Regions Select Search... Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Size Population, total OPTIONS EXPAND PRESENT [Gapminder, Wealth & Health of Nations] 4

5 What about change over years? 5

6 Another Solution showing trends over time Bubbles FACTS TEACH ABOUT HOW TO USE Share! " # $ English Income per person (GDP/capita, PPP$ inflation-adjusted) DATA DO United S Color World Regions 32k Russia 16k China Nigeria Show Search... Hide all shapes except: United States Russia Nigeria China Afghanistan Albania Algeria Andorra RESET FIND OPTIONS EXPAND PRESENT [Gapminder, Wealth & Health of Nations] 6

7 Assignment 2 Link Use Tableau and D3 to create stacked bar charts of Citibike data Due next Friday, Start now! 7

8 Visual Encoding How do we encode data visually? - Marks are the basic graphical elements in a visualization - Channels are ways to control the appearance of the marks Marks classified by dimensionality: Points Lines Areas Also can have surfaces, volumes Think of marks as a mathematical definition, or if familiar with tools like Adobe Illustrator or Inkscape, the path & point definitions 8

9 Visual Channels Position Color Horizontal Vertical Both Shape Tilt Size Length Area Volume [Munzner (ill. Maguire), 2014] 9

10 Channels Usually map an attribute to a single channel - Could use multiple channels but - Limited number of channels Restrictions on size and shape - Points are nothing but location so size and shape are ok - Lines have a length, cannot easily encode attribute as length - Maps with boundaries have area, changing size can be problematic 10

11 Cartograms [Election Results by Population, M. Newman, 2012] 11

12 Channel Types Identity => what or where, Magnitude => how much Magnitude Channels: Ordered Attributes Identity Channels: Categorical Attributes Position on common scale Position on unaligned scale Length (1D size) Tilt/angle Spatial region Color hue Motion Shape Area (2D size) Depth (3D position) Color luminance Color saturation Curvature Volume (3D size) [Munzner (ill. Maguire), 2014] 12

13 Mark Types Can have marks for items and links - Connection => pairwise relationship - Containment => hierarchical relationship Marks as Items/Nodes Points Lines Areas Marks as Links Containment Connection [Munzner (ill. Maguire), 2014] 13

14 Expressiveness and Effectiveness Expressiveness Principle: all data from the dataset and nothing more should be shown - Do encode ordered data in an ordered fashion - Don t encode categorical data in a way that implies an ordering Effectiveness Principle: the most important attributes should be the most salient - Saliency: how noticeable something is - How do the channels we have discussed measure up? 14

15 How do we get these rankings? 15

16 Test % difference in length between elements A B [Heer & Bostock, 2010] 16

17 Test % difference in length between elements 100 Answer: Left is ~5.6x longer than Right 0 A B [Heer & Bostock, 2010] 17

18 Test % difference in length between elements A B [Heer & Bostock, 2010] 18

19 Test % difference in length between elements A B [Heer & Bostock, 2010] 19

20 Test % difference in length between elements A B [Modified from Heer & Bostock, 2010] 20

21 Test % difference in length between elements 100 Answer: Right is 4x larger than Left 0 A B [Modified from Heer & Bostock, 2010] 21

22 Test % difference in area between elements A B [Heer & Bostock, 2010] 22

23 Test % difference in area between elements Answer: A is ~2.25x larger (in area) than B A B [Heer & Bostock, 2010] 23

24 Test % difference in area between elements A B [Heer & Bostock, 2010] 24

25 Test % difference in area between elements Answer: A is ~6.1x larger (in area) than B A B [Heer & Bostock, 2010] 25

26 Test % difference in area between elements B A [Heer & Bostock, 2010] 26

27 Test % difference in area between elements Answer: B is ~2.5 larger (in area) than A B A [Heer & Bostock, 2010] 27

28 Cleveland & McGill Experiments TYPE 1 TYPE 2 TYPE 3 TYPE 4 TYPE 5 100o 10oo oo 100- IhLL O_ 0A A * A B A B A B A B A B Figure 4. Graphs from position-length experiment. 40 c< 0WB a A BC D E Figure 3. Graphs from position-angle experiment. [Cleveland & McGill, 1984] 28

29 Heer & Bostock Experiments Rerun Cleveland & McGill s experiment using Mechanical Turk with more tests B A A B A B Figure 2: Area judgment stimuli. Top left: Bubble chart (T7), Bottom left: Center-aligned rectangles (T8), Right: Treemap (T9). [Heer & Bostock, 2010] 29

30 Results Summary Cleveland & McGill s Results Positions Log Error Crowdsourced Results Angles Circular areas Rectangular areas (aligned or in a treemap) Log Error [Munzner (ill. Maguire) based on Heer & Bostock, 2014] 30

31 Psychophysics How do we perceive changes in stimuli The Psychophysical Power Law [Stevens, 1975]: All sensory channels follow a power function based on stimulus intensity (S = I n ) Length is fairly accurate Magnified vs. compressed sensations [Munzner (ill. Maguire), 2014] 31

32 Ranking Channels by Effectiveness Magnitude Channels: Ordered Attributes Position on common scale Position on unaligned scale Length (1D size) Tilt/angle Identity Channels: Categorical Attributes Spatial region Color hue Motion Shape Area (2D size) Depth (3D position) Color luminance Color saturation Curvature Volume (3D size) [Munzner (ill. Maguire), 2014] 32

CSE Data Visualization. Graphical Perception. Jeffrey Heer University of Washington

CSE Data Visualization. Graphical Perception. Jeffrey Heer University of Washington CSE 512 - Data Visualization Graphical Perception Jeffrey Heer University of Washington Design Principles [Mackinlay 86] Expressiveness A set of facts is expressible in a visual language if the sentences