Data Driven Guides: Supporting expressive design for Information - PowerPoint PPT Presentation

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

Source: Google Search Trends 2016

Source: Google Search Trends Analysts & Researchers

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

Visualization Design Tools Tableau Lyra Illustrator iVisDesigner CorelDRAW Excel A ffi nityDesigner Plot.ly DataVisual Photoshop Spotfire SageBrush Inkscape ManyEyes Improvise InDesign Less expressive More expressive (Automatic) (Manual)

Visualization Design Tools Tableau Lyra Illustrator iVisDesigner CorelDRAW Excel ? A ffi nityDesigner Plot.ly DataVisual Photoshop Spotfire SageBrush Inkscape ManyEyes Improvise InDesign Less expressive More expressive (Automatic) (Manual)

Interviews with infographic designers.

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

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

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.

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

Interviews with infographic designers. Participants Questions • 2 professional designers   • What di ffi culties they face in 3 master student designers   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.

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

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

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

1. Lack of flexible design in   visualization construction tools Di ffi cult to add annotations & Tableau Lyra Illustrator embellishments. iVisDesigner Excel CorelDRAW ` Plot.ly A ffi nityDesigner DataVisual Spotfire Photoshop SageBrush ManyEyes Inkscape Improvise InDesign Less expressive More expressive (Automatic) (Manual)

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

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

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

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

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

Design Goals

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

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

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.

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

70 B C A D 53 35 18 0 Data Table Graph

B C A D Data Table Data-Driven Guides

Flexible direct manipulation To manipulate to create a custom layout

d2 d1 d3 d4 Maintain proportional lengths To preserve data integrity

Snap to a guide To support accurate data-driven drawing

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

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

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 .

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

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.

Use Cases

Use cases 1. Drawing data-driven graphics

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

Cyan: Data-Driven Guides

Create & manipulate

Link & Repeat

Radial layout & link inspection

Composite structure & copy and paste

Label generation

Deforms

Deforms Linear blend skinning Related Work   1. Bounded biharmonic weights for real-time deformation. Jacobson, Alec, et al. ACM Trans. Graph., 2011 2. Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

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., 2011 2. Skinning cubic Bézier splines and Catmull-Clark subdivision surfaces. Liu, Songrun, et al. ACM Trans. Graph., 2014.

Data Driven Guides: Supporting expressive design for Information - PowerPoint PPT Presentation

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

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Data Driven Guides: Supporting expressive design for Information - PowerPoint PPT Presentation

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

1 Data-dr Data-driven philosophy n philosophy Data-dr Data-driven: push n: push 7 8

CDS product guides We have a number of different product guides available, please click on the

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Data Driven Marketing the DNA of customer oriented companies 00101001 yes no Data Driven

Data-driven Clustering via Parameterized Lloyds Families Travis Dick Joint work with

CS 528 Mobile and Ubicomp Lecture 3a: Data-Driven Layouts &amp; Android Components Emmanuel Agu

Learner guides Articulate the learning goals Discussion question Learner guides Activities

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Where are we? Data! Data? We are not short of data Technologies! Technologies? We are not

2016 Constituent Driven Process Not to, not for, but WITH Planning Model ~ Top Down Board

Working Together on Data Science Data-Driven Discovery Initiative @ Moore Foundation Chris

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TO JOIN BY TELEPHONE: TO JOIN BY TELEPHONE: Phone: (5 Phone: (510) 2 ) 210-8882 0-8882 | Access

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