1. 1/59 Lecture 02: Data Mapping September 15, 2015 COMP 150-2 Visualization

2. 2/59 Admin Assignment 0 -- no demo TA Office hours posted Assignment 1 posted (more later) Piazza accounts Online study by Tara Kola: http://www.surveygizmo.com/s3/2229492/cc41525a dfa6 Reminder: Lab 1 is due on Thursday!! Also, someone please let me know when we have less than 15 mins left in the class

3. 3/59 How do you design a visualization? (note, this might be a trick question)

4. 4/59 Better Question: How do you design a data visualization?

5. 5/59 What is Data Visualization? A mapping of data attributes to visual attributes What are data attributes? What are visual attributes?

6. 6/59 Good vs. Bad Ways An objective analysis to a visual design Reverse the thought process – What if we don’t start by thinking about visual designs first (and how data would map onto it)… This is how Excel wants you to think… But it’s backwards But instead we start with data design and figure out what visual marks can “support” it? Consider: Scatterplot Barchart

7. 7/59 http://d3js.org/

8. 8/59 Common Visualization Design

9. 9/59 General Use of Glyphs

10. 10/59 General Use of Glyphs

11. 11/59 Chernoff Faces (1973)

12. 12/59 A Note on Chernoff Faces What do you think?

13. 13/59 A Note on Chernoff Face Chernoff faces was invented by Herman Chernoff (1973) Based on the idea that human perceptions are specifically tuned to detect facial features and expressions Study have shown that detecting differences in Chernoff faces is not pre-attentive (Morris et al. 1999)

14. 14/59 Detecting Faces Kindlmann et al. (2002)

15. 15/59 Detecting Faces Kindlmann et al. (2002)

16. 16/59 General Mapping? What data attributes map well to visual attributes? How do you know if the mappings are good? Objective measure: see set theory notes below Perceptual and cognitive measures: future lectures Subjective measure: aesthetics, preference, tasks, etc.

17. 17/59 Color Shape

18. 18/59 Set Theory Bijection (one visual attribute, one data attribute) Surjection (multiple visual attribute to one data attribute) Every element in Y has 1 or more corresponding element in X Injection (One to one mapping, but not all data elements are mapped) Every element in X has a mapping in Y, but not true in reverse Other scenarios?

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20. 20/59 Interaction Effects An example of interference between icon spacing (representing a linear variable) and icon brightness (representing a more general scalar field). Areas of high brightness create false lower-spacing regions. Acevdeo, Laidlaw. “Subjective Quantification of Perceptual Interactions among some 2D Scientific Visualization Methods”, TVCG 2006.

21. 21/59 Interaction Effects Process for creating the stimuli for the data resolution identification task. (a) Shows a vertical sine-wave dataset. (b) Shows the same dataset with amplitude values a linearly decreasing from left to right. (c) Shows the final appearance of the datasets used for this task, where we also linearly move the zero value of the sine-wave from a/2 at the top of the image to 1−a/2 at the bottom. (d) Shows how subjects would mark the area where they perceive the sine-wave pattern.

22. 22/59 What If? What if 1.The number of DATA VARIABLES is greater than VISUAL VARIABLES? 2.The number of VISUAL VARIABLES is greater than DATA VARIABLES?

23. 23/59 Exercise Consider a line graph How many variables can you fit into a line graph? What about a barchart?

24. 24/59 Exercise Consider a line graph How many variables can you fit into a bar chart? What about a barchart? For 2-dimensional data, are these two visualizations the same?

25. 25/59 Structure and Form Image courtesy of Barbara Tversky

26. 26/59 Structure and Form Image courtesy of Barbara Tversky

27. 27/59 Visualization Process

28. 28/59 Data Definition A typical dataset in visualization consists of n records (r 1, r 2, r 3, …, r n ) Each record r i consists of m (m >=1) observations or variables (v 1, v 2, v 3, …, v m ) A variable may be either independent or dependent Independent variable (iv) is not controlled or affected by another variable For example, time in a time-series dataset Dependent variable (dv) is affected by a variation in one or more associated independent variables For example, temperature in a region Formal definition: r i = (iv 1, iv 2, iv 3, …, iv m i, dv 1, dv 2, dv 3, …, dv m d ) where m = m i + m d

29. 29/59 Basic Data Types Nominal Ordinal Scale / Quantitative Interval ratio Def: A set of not-ordered and non-numeric values For example: Categorical (finite) data {apple, orange, pear} {red, green, blue} Arbitrary (infinite) data {“12 Main St. Boston MA”, “45 Wall St. New York NY”, …} {“John Smith”, “Jane Doe”, …}

30. 30/59 Basic Data Types Nominal Ordinal Scale / Quantitative Interval ratio Def: A tuple (an ordered set) For example: Numeric Binary Non-numeric

31. 31/59 Basic Data Types Nominal Ordinal Scale / Quantitative Interval ratio Def: A numeric range Interval Ordered numeric elements on a scale that can be mathematically manipulated, but cannot be compared as ratios For example: date, current time (Sept 14, 2010 cannot be described as a ratio of Jan 1, 2011) Ratio where there exists an “absolute zero” For example: height, weight

32. 32/59 Basic Data Types (Formal) Nominal (N){…} Ordinal (O) Scale / Quantitative (Q)[…] Q → O [0, 100] → O → N → {C, B, F, D, A} N → O (??) {John, Mike, Bob} → {red, green, blue} → ?? O → Q (??) Hashing? Bob + John = ?? Readings in Information Visualization: Using Vision To Think. Card, Mackinglay, Schneiderman, 1999

33. 33/59 Operations on Basic Data Types What are the operations that we can perform on these data types? Nominal (N) = and ≠ Ordinal (O) >, <, ≥, ≤ Scale / Quantitative (Q) everything else (+, -, *, /, etc.) Consider a distance function

34. 34/59 Half-Way Point (Find your partner)