Lecture 5: DATA MAPPING & VISUALIZATION. November 3 rd, Presented by: Anum Masood (TA)

Transcription

1 1/59 Lecture 5: DATA MAPPING & VISUALIZATION November 3 rd, 2017 Presented by: Anum Masood (TA)

2 2/59 Recap: Data What is Data Visualization? Data Attributes Visual Attributes Mapping What are data attributes? Data types? What are visual attributes? What does mapping mean?

3 3/59 Data Type Taxonomy 1D, 2D, 3D Temporal Multi-dimensional (nd) Tree Network Others?

4 4/59 Connecting Data To Visualization Data have attributes (dimensions) Visualizations have attributes (dimensions) Can the two map to each other? Jacques Bertin, Semiologie Graphique (Semiology of Graphics), 1967.

5 Visual Channels 5/59

6 Elements of Visualization 6/59

7 7/59

8 8/59

9 9/59

10 10/59

11 11/59

12 12/59 Vis Lies The ability to influence is one of the most powerful and yet scary aspect of visualization. Subtle influence can lead to biases, which are incredibly difficult to shake off. Visualization provides the first view of the data, where the viewer is most susceptible to biases

13 13/59 Using Visualization to Influence? Why rainbow color maps are bad Image courtesy of

14 14/59 Color == Hue? Image courtesy of

15 Color == Intensity (Luminance)? 15/59

16 16/59 Visual Metaphors Image courtesy Caroline Ziemkiewicz

17 Visual Metaphors 17/59

18 Appropriateness? 18/59

19 19/59 Structure and Form Image courtesy of Barbara Tversky

20 20/59 Appropriateness? Which data dimension should be mapped to what visual variable?

21 Bar vs. Pie... 21/59

22 22/59 Card, Mackinlay (1997) Symbol Meaning D Data Type ::= N (Nominal), O (Ordinal), Q (Quantitative). Q X (Intrinsically spatial), Q lon (Geographical) NxN (Set mapped to itself - graphs) F : Function for recoding data ::= f (unspecified) > (filter) s (sorting) mds (multidimensional scaling) (interactive input to a function) D : Recoded Data Type (see D) CP : Control Processing tx (text) M : Mark types ::= P (Point), L (Line), S (Surface), A (Area), V (Volume) R : Retinal (mark) properties ::= C (Color), S (Size), (Connection), [] (Enclosure) XYZT : Position in space time ::= N, O, Q, * (nonsemantic use of space-time) V : View transformation ::=hb(hyperbolic mapping) W : Widget ::= sl(slider) rb(radio buttons)

23 23/59 Example 1: Ozone Mapping Card and Mackinlay, The Structure of the Information Visualization Design Space

24 24/59 Example 1: Ozone Mapping The rows of the table describe the variables with the case variable ( Samples ) at the top and the value variables below. The nominal (N) set of Samples is mapped to point marks (P in column M), which have their retinal property of color (C in column R) mapped to the Ozone variable. The ozone mapping includes a function (f) that converts the quantitative (Q) ozone measurements to an ordinal (O) set that can be easily mapped to a set of colors. The quantitative (Q) variables of Longitude, Latitude, and Height are mapped to the positions X, Y, and Z, which determine the position of the point marks. The Date variable is mapped to time (T), which creates an animated visualization. Table 1 makes it clear that Figure 1 is a 3D animated visualization involving colored points.

25 Example 2: GIS 25/59

26 26/59 Example 2: GIS Table 2 describes the map part of Figure 2. The Offices variable is mapped to line marks (L). The Profit variable is mapped to the size of these lines (Sz in the R column). Profits are also mapped to the Z-axis and via a function (f) to a nominal set indicating the sign of the profits. This nominal set is mapped to the color of the lines (C in the R column). Table 2 clearly reveals that multiple graphical techniques are used to describe the Profit variable in order to enhance the perception of this important data variable

27 Other Examples 27/59

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29 Treemap Example 29/59

30 30/59 Exercise: Treemap Let s think about your assignment 1: Squarified Treemap. How many (minimum) dimensions of data does it need? Accept 1 dimension or 2 dimensions Write out the mapping of Squarified Treemap Starting with the basic (no hierarchy) Add hierarchy

31 31/59 Treemap Example The problem is that the same variable is mapped onto two different position presentations, each half of the time Q -> X (half time) Q -> Y (half time) giving an inconsistent mapping and prohibiting the user from forming an easy image. What the user should be able to take from the image is essentially Retinal: Size coding, but the same Size can have many different visual manifestations, each with a different aspect ratio. Thus the space-filling property of the visualization comes at a perceptual cost, which is clearly shown in Table 9.

32 Questions? 32/59

33 Animation and Animated Transition 33/59

34 34/59 Set Theory (from Lecture 2) 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?

35 35/59 Remco s Claim All visualizations depicting the same data that follow bijective visual-data mapping are in fact isomorphic to each other In other words:

36 36/59 Remco s Claim For all visualizations depicting the same data that follow bijective visual-data mapping, there exists a non-trivial animated transition between them. Non-trivial is defined as not reaching a blank state where any depicted data item reaches a value of 0. For example, consider a case where bar -> line, but all bars are set to 0 first

37 37/59 Remco s Claim Note that this does not mean that there is only one way to transition from one visualization to another. For example, for two bivariate visualizations of dimensions (x, y), there are at least two ways to do the transition: Animate X, followed by animate Y Animate Y, followed by animate X (Note: don t try to do two at the same time. More on this later)

38 38/59 Remco s Claim For example, for two bivariate visualizations of dimensions (x, y), there are at least two ways to do the transition: Animate X, followed by animate Y Animate Y, followed by animate X Following this logic, consider the case of: Bar Line How many ways are there to do this transition? (Hint: define the mappings first)

39 39/59 Exercise Considering that all the visualizations depict the same data and relationships, how can one go from one visualization to another??

40 Tree -> Tree (with Nodes) 40/59

41 Tree (with Nodes) -> Icicle 41/59

42 Icicle -> Circle Icicle(?) 42/59

43 43/59 Circle Icicle -> Packed Circle? Invert Space (negative space becomes positive space) OR Shift each row up from child into parent (recursively)

44 44/59 Circle Icicle -> Packed Circle Invert Space (negative space becomes positive space) OR Shift each row up from child into parent (recursively)

45 Other Paths? 45/59

46 46/59 Remco s Claim #2 For every visualization that uses the Cartesian coordinate system, there is a corresponding visualization in the Polar coordinate system (and vice versa) Question is with the mapping (x -> r, y -> theta), or treemap to circle packing? This was one really easy way to get publications in the visualization community in the early days

47 47/59 Marks Layout Layout2: (because size is vague)

48 48/59 Notice that There is a clear difference between Visual layout (visual metaphor) Visual marks Each of these can have a coordinate system WARNING! While some times it s easy to swap a visual mark with another, these two considerations are not always independent For example, think wedge as a visual mark. Works well with pie chart, but does not work with a rectangular layout

49 49/59 Animation vs. Animated Transitioning Animated Transition is a subset of animation techniques in general: Animated transitioning focuses on the specific goal of leading the user from one view to another in a cohesive way Whereas animation, as it is often mapped to the time component of temporal data, introduces new information.

50 50/59 Powers of Animation Animation (movement) directly connects to our visual system <blink> In fact, a moving object entering your perceptual space immediate draws your attention. You cannot avoid this or suppress this instinct. </blink>

51 51/59 Animation Long story short, use animation sparingly. Some exceptions to this rule: Animated Transitioning: To better denote a cause and effect relationship. E.g., in zooming. Or to help the user transition between two states in a visualization. Storytelling: Focus the user on a specific aspect of the visualization. Artistic reasons (part of storytelling?) to particularly highlight certain emotions (sense of chaos, sense of flatness, etc.)

52 52/59 Why is Animation So Bad? Part 1 Overused. When every object in the visualization is moving, it is impossible for any one person to keep track of all of them. E.g. think Gapminder video This means that two viewers of the same animation walks away with different findings (if you don t have Hans narrating to you)

53 53/59 Why is Animation So Bad? Part 2 Limited cognitive abilities The human s short-term memory starts to degrade within a second or so. Animation of important information overloads this cognitive resource in no time. This is easy to test if I ask you to recall a particular frame in the Gapminder video, you probably can t do it.

54 54/59 Why is Animation So Bad? Part 2 The humans attention span is limited. Asking someone to focus (keep track) of a lot of moving objects over a long period of time is extremely taxing!

55 55/59 Why is Animation So Bad? Part 3 The limited attention leads to: Change Blindness This Task is much harder if we were to animate these two frames

56 56/59 Why is Animation So Bad? Part 3 The limited attention leads to: Change Blindness u/1/igqmdok_zfy

57 57/59 Alternative to Animation There are different ways to flatten the animation. In temporal data visualization, one easy way is to treat time as a quantitative value. For example, map time to the y-axis of a line chart Treat animation as a sequence of images and use small-multiples Research has shown that user s are faster and more accurate when using small-multiples to analyze the same data used in the Hans Rosling video (than using animation).

58 Thank You 58/59

59 Q & A 59/59