Data Driven Guides: Supporting expressive design for Information graphics

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1 Data Driven Guides: Supporting expressive design for Information graphics Nam Wook Kim Eston Schweickart Zhicheng Leo Liu Harvard Cornell Adobe Mira Dontcheva Wilmot Li Jovan Popović Hanspeter Pfister Adobe Adobe Adobe Harvard

2 Source: Google Search Trends 2016

3 Source: Google Search Trends Analysts & Researchers

4 Source: Google Search Trends Artists, Journalists, Bloggers, Designers

5 Visualization Design Tools Tableau Lyra Illustrator Excel ivisdesigner CorelDRAW Plot.ly AffinityDesigner Spotfire DataVisual Photoshop ManyEyes SageBrush Improvise Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

6 Visualization Design Tools Tableau Lyra Illustrator Excel ivisdesigner CorelDRAW Plot.ly AffinityDesigner Spotfire DataVisual Photoshop ManyEyes SageBrush Improvise Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

7 Visualization Design Tools Tableau Lyra Illustrator Excel ivisdesigner CorelDRAW Plot.ly AffinityDesigner Spotfire DataVisual Photoshop ManyEyes SageBrush Improvise Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

8 Visualization Design Tools Tableau Lyra Illustrator Excel ivisdesigner CorelDRAW Plot.ly AffinityDesigner Spotfire DataVisual Photoshop ManyEyes SageBrush Improvise Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

9 Visualization Design Tools Tableau Lyra Illustrator Excel ivisdesigner CorelDRAW Plot.ly Spotfire DataVisual? AffinityDesigner Photoshop ManyEyes SageBrush Inkscape Improvise InDesign Less expressive More expressive (Automatic) (Manual)

10 Interviews with infographic designers.

11 Interviews with infographic designers. Participants 2 professional designers 3 master student designers 1 visualization researcher.

12 Interviews with infographic designers. Participants 2 professional designers 3 master student designers 1 visualization researcher. 2 ~ 10 years of experience in graphic & infographic design.

13 Interviews with infographic designers. Participants 2 professional designers 3 master student designers 1 visualization researcher. 2 ~ 10 years of experience in graphic & infographic design. Mostly use vector editing tools such as Adobe Illustrator.

14 Interviews with infographic designers. Participants 2 professional designers 3 master student designers Questions What difficulties they face in creating infographics? 1 visualization researcher. 2 ~ 10 years of experience in graphic & infographic design. Mostly use vector editing tools such as Adobe Illustrator.

15 Interviews with infographic designers. Participants 2 professional designers 3 master student designers Questions What difficulties they face in creating infographics? 1 visualization researcher. Overall design practice. 2 ~ 10 years of experience in graphic & infographic design. Mostly use vector editing tools such as Adobe Illustrator.

16 Interviews with infographic designers. Participants 2 professional designers 3 master student designers Questions What difficulties they face in creating infographics? 1 visualization researcher. Overall design practice. 2 ~ 10 years of experience in graphic & infographic design. How they manually encode data into graphics. Mostly use vector editing tools such as Adobe Illustrator.

17 Interviews with infographic designers. Participants 2 professional designers 3 master student designers Questions What difficulties they face in creating infographics? 1 visualization researcher. Overall design practice. 2 ~ 10 years of experience in graphic & infographic design. How they manually encode data into graphics. Mostly use vector editing tools such as Adobe Illustrator. Related work Bigelow et al (AVI, 2014).

18 1. Lack of flexible design in visualization construction tools Tableau Excel Lyra ivisdesigner Illustrator CorelDRAW Plot.ly Spotfire ManyEyes DataVisual SageBrush Improvise AffinityDesigner Photoshop Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

19 1. Lack of flexible design in visualization construction tools Tableau Lyra Difficult to add annotations & Illustrator Excel ivisdesigner embellishments. CorelDRAW ` Plot.ly AffinityDesigner Spotfire ManyEyes DataVisual SageBrush Improvise Photoshop Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

20 1. Lack of flexible design in visualization construction tools Tableau Lyra Difficult to add annotations & Illustrator Excel ivisdesigner embellishments. CorelDRAW ` Plot.ly ` AffinityDesigner Spotfire ManyEyes DataVisual SageBrush Improvise Photoshop Inkscape InDesign Less expressive (Automatic) Difficult to design More expressive (Manual) new visual marks & layouts.

21 2. Tedious manual encoding required in graphic design tools Tableau Excel Plot.ly Spotfire ManyEyes Lyra ivisdesigner DataVisual SageBrush Improvise Illustrator CorelDRAW AffinityDesigner Photoshop Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

22 2. Tedious manual encoding required in graphic design tools Tableau Excel Plot.ly Spotfire ManyEyes Lyra ivisdesigner DataVisual SageBrush Improvise Custom Scale Illustrator CorelDRAW AffinityDesigner Photoshop Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

23 3. Absence of data binding for custom/imported charts in graphic design tools Tableau Excel Plot.ly Spotfire ManyEyes Lyra ivisdesigner DataVisual SageBrush Improvise Illustrator CorelDRAW AffinityDesigner Photoshop Inkscape InDesign Less expressive (Automatic) More expressive (Manual)

24 3. Absence of data binding for custom/imported charts in graphic design tools Tableau Excel Plot.ly Spotfire ManyEyes Lyra ivisdesigner DataVisual SageBrush Illustrator CorelDRAW AffinityDesigner Photoshop Inkscape Improvise To customize this chart, InDesign Less expressive ungrouping is required More expressive (Automatic) Graph Tool in Illustrator. resulting in loss of data binding. (Manual)

25 Design Goals

26 Design Goals 1. Maintain flexibility in the design process. e.g., not enforcing a predefined outcome or specific order of operations.

27 Design Goals 1. Maintain flexibility in the design process. 2. Provide methods for accurate data-driven drawing.

28 Design Goals 1. Maintain flexibility in the design process. 2. Provide methods for accurate data-driven drawing. 3. Support persistent data binding for freeform graphics.

29 d = length a d = area Length guide Area guide Data Driven Guides: Supporting expressive design for Information graphics

30 70 A B C D Data Table 0 Graph

31 70 A B C D Data Table 0 Graph

32 A B C D Data Table Data-Driven Guides

33 Flexible direct manipulation To manipulate to create a custom layout

34 d2 d3 d1 d4 Maintain proportional lengths To preserve data integrity

35 Snap to a guide To support accurate data-driven drawing

36 Data-binding using guides as the backbone of associated shapes. (2D deformation for vector graphic)

37 Logo Design User Interface Design Designer Friends: Rulers, Grids, Guides Architectural Design Drawing

38 Default guide color: Cyan d = length a d = area Length guide Area guide Length and area guides can also be used as a Position guide.

39 Visual variables Fundamental channels for encoding information. J. Bertin.,1983. Rankings of visual variables by J. Mackinlay.,1986.

40 Visual variables Fundamental channels for encoding information. Frequently used in infographics, although often misused. J. Bertin.,1983. Rankings of visual variables by J. Mackinlay.,1986.

41 Use Cases

42 Use cases 1. Drawing data-driven graphics

43 Cyan: Data-Driven Cyan: Guides Data-Driven Guides

44 Cyan: Data-Driven Guides

45 Create & manipulate

46 Link & Repeat

47 Radial layout & link inspection

48 Composite structure & copy and paste

49 Label generation

50

51 Deforms

52 Deforms Linear blend skinning Related Work 1. Bounded biharmonic weights for real-time deformation. Jacobson, Alec, et al. ACM Trans. Graph., Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

53 Rest pose Old point in a shape. Deformed Linear blend skinning Related Work 1. Bounded biharmonic weights for real-time deformation. Jacobson, Alec, et al. ACM Trans. Graph., Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

54 Rest pose Spatial Transformations (changes in guides) Deformed Linear blend skinning Related Work 1. Bounded biharmonic weights for real-time deformation. Jacobson, Alec, et al. ACM Trans. Graph., Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

55 Rest pose Skinning weight (Transformation amount) Deformed Linear blend skinning Related Work 1. Bounded biharmonic weights for real-time deformation. Jacobson, Alec, et al. ACM Trans. Graph., Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

56 Rest pose New points in a shape. Deformed Linear blend skinning Related Work 1. Bounded biharmonic weights for real-time deformation. Jacobson, Alec, et al. ACM Trans. Graph., Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

57 Deforms

58 Moves Moves Used as a Position Guide

59 Used as flexible rulers. Cyan: Data-Driven Guides

60 Cyan: Data-Driven Guides Four area guides are used to encode a single shape.

61 Use cases 1. Drawing data-driven graphics 2. Retargeting existing artworks

62 Original By Nigel Holmes

63 Original By Nigel Holmes

64 Use cases 1. Drawing data-driven graphics 2. Retargeting existing artworks 3. Proofreading existing infographics

65 Proofreading existing infographics Cyan: Data-Driven Guides By Nigel Holmes. By Tiffany Farrant-Gonzalez.

66 Proofreading existing infographics The length of marks do not match the length of guides (curved). Original By Nigel Holmes.

67 Proofreading existing infographics Length guide (Radius) Area guide Original By Tiffany Farrant-Gonzalez.

68 Informal usability evaluation with designers. Participants 13 master student designers (architecture, urban planning, and infographic design etc)

69 Informal usability evaluation with designers. Participants 13 master student designers (architecture, urban planning, and infographic design etc) 2 ~ 10 years of experience in infographic design.

70 Informal usability evaluation with designers. Participants 13 master student designers (architecture, urban planning, and infographic design etc) 2 ~ 10 years of experience in infographic design. Frequently used tools include vector & image editors (programming: only 2 people).

71 Informal usability evaluation with designers. Participants 13 master student designers (architecture, urban planning, and Procedure A 60 min session with a 15 min tutorial infographic design etc) 2 ~ 10 years of experience in infographic design. Frequently used tools include vector & image editors (programming: only 2 people).

72 Informal usability evaluation with designers. Participants 13 master student designers (architecture, urban planning, and Procedure A 60 min session with a 15 min tutorial infographic design etc) Pre-task and post-task surveys 2 ~ 10 years of experience in infographic design. Frequently used tools include vector & image editors (programming: only 2 people).

73 Informal usability evaluation with designers. Participants 13 master student designers (architecture, urban planning, and Procedure A 60 min session with a 15 min tutorial infographic design etc) Pre-task and post-task surveys 2 ~ 10 years of experience in infographic design. 2 replication tasks Frequently used tools include 1 creative task vector & image editors (programming: only 2 people). * We did not measure time

74 Results Creative Tasks Two selected infographics created by participants. Data: GDP of G5 Countries.

75 Results Creative Tasks Two selected infographics created by participants. Post-task surveys (5-point Likert scale) 1. Interactions with DDG were intuitive. (μ=4.0, σ=0.71) Data: GDP of G5 Countries.

76 Results Creative Tasks Two selected infographics created by participants. Post-task surveys (5-point Likert scale) 1. Interactions with DDG were intuitive. (μ=4.0, σ=0.71) 2. DDG is useful for positioning and measuring custom shapes based on data compared to rulers or grids. (μ=4.7, σ=0.63) Data: GDP of G5 Countries.

77 Results Creative Tasks Two selected infographics created by participants. Post-task surveys (5-point Likert scale) 1. Interactions with DDG were intuitive. (μ=4.0, σ=0.71) 2. DDG is useful for positioning and measuring custom shapes based on data compared to rulers or grids. (μ=4.7, σ=0.63) 3. DDG is useful for designing creative and expressive infographics Data: GDP of G5 Countries. (μ=4.9, σ=0.38)

78 Results: qualitative feedback Currently, I need a calculator to make data graphic, which is pretty arduous. This tool makes it much easier to try things out and experiment with the graphics. - P10.

79 Results: qualitative feedback Currently, I need a calculator to make data graphic, which is pretty arduous. This tool makes it much easier to try things out and experiment with the graphics. - P10. I think that this would be a wonderful aid in creating graphics for architectural representations. - P3.

80 Results: qualitative feedback Currently, I need a calculator to make data graphic, which is pretty arduous. This tool makes it much easier to try things out and experiment with the graphics. - P10. I think that this would be a wonderful aid in creating graphics for architectural representations. - P3. I usually do very analytical infographics, using traditional forms like bars or circles. Because of that, I m not quite sure if data guides might be very useful - P5.

81 Limitations 1. Data-driven guides currently works with a tabular dataset.

82 Limitations 1. Data-driven guides currently works with a tabular dataset. 2. Difficult to generate guide elements such as axes and legends.

83 Limitations 1. Data-driven guides currently works with a tabular dataset. 2. Difficult to generate guide elements such as axes and legends. 3. Other visual variables have to be manually encoded such color or angle.

84 Future work 1. Reusable creative infographic templates. Template

85 Future work 1. Reusable creative infographic templates. Template 2. Data-driven guides for other visual variables. Color, angle, shape, slope etc.

86 Future work 1. Reusable creative infographic templates. Template 2. Data-driven guides for other visual variables. Color, angle, shape, slope etc. 3. Intelligent systems for automatically providing design feedback. Incorrect size Area vs radius

87 Data Driven Guides: Supporting expressive design for Information graphics

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