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

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

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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 graphics

Nam Wook Kim Harvard Zhicheng Leo Liu Adobe Mira Dontcheva Adobe Hanspeter Pfister Harvard Wilmot Li Adobe Jovan Popović Adobe Eston Schweickart Cornell

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Source: Google Search Trends

2016

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Analysts & Researchers

Source: Google Search Trends

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Artists, Journalists, Bloggers, Designers

Source: Google Search Trends

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Visualization Design Tools

Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

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Visualization Design Tools

Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop

Less expressive (Automatic) More expressive (Manual)

Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

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Plot.ly Excel Spotfire Tableau ManyEyes Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop

Visualization Design Tools

Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

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Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Visualization Design Tools

Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop

Less expressive (Automatic) More expressive (Manual)

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Visualization Design Tools

Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

?

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Interviews with infographic designers.

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Interviews with infographic designers.

Participants

  • 2 professional designers


3 master student designers
 1 visualization researcher.


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Interviews with infographic designers.

Participants

  • 2 professional designers


3 master student designers
 1 visualization researcher.


  • 2 ~ 10 years of experience in

graphic & infographic design.


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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.

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Interviews with infographic designers.

Questions

  • What difficulties they face in

creating infographics?
 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.

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Interviews with infographic designers.

Questions

  • What difficulties they face in

creating infographics?


  • Overall design practice.

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.

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Interviews with infographic designers.

Questions

  • What difficulties they face in

creating infographics?


  • Overall design practice.
  • How they manually encode

data into graphics. 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.

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Interviews with infographic designers.

Questions

  • What difficulties they face in

creating infographics?


  • Overall design practice.
  • How they manually encode

data into graphics. Related work Bigelow et al (AVI, 2014). 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.

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Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop

  • 1. Lack of flexible design in 


visualization construction tools

Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

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Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

Difficult to add annotations & embellishments.

`

  • 1. Lack of flexible design in 


visualization construction tools

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Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

Difficult to design new visual marks & layouts.

` `

Difficult to add annotations & embellishments.

  • 1. Lack of flexible design in 


visualization construction tools

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Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop

Less expressive (Automatic) More expressive (Manual)

  • 2. Tedious manual encoding

required in graphic design tools

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Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

  • 2. Tedious manual encoding

required in graphic design tools

Custom Scale

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Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop

Less expressive (Automatic) More expressive (Manual)

  • 3. Absence of data binding for 


custom/imported charts in graphic design tools

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  • 3. Absence of data binding for 


custom/imported charts in graphic design tools

Illustrator CorelDRAW AffinityDesigner InDesign Inkscape Photoshop Plot.ly Excel Spotfire Tableau ManyEyes Lyra iVisDesigner SageBrush DataVisual Improvise

Less expressive (Automatic) More expressive (Manual)

Graph Tool in Illustrator. To customize this chart, ungrouping is required resulting in loss of data binding.

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Design Goals

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  • 1. Maintain flexibility in the design process.

e.g., not enforcing a predefined outcome

  • r specific order of operations.

Design Goals

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

Design Goals

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Design Goals

  • 2. Provide methods for accurate data-driven drawing.
  • 1. Maintain flexibility in the design process.
  • 3. Support persistent data binding for freeform graphics.
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Data Driven Guides:

Supporting expressive design for Information graphics

Length guide

a

Area guide

d = area d = length

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Data Table

A B C D

18 35 53 70

Graph

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Data Table

A B C D

18 35 53 70

Graph

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Data-Driven Guides Data Table

A B C D

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Flexible direct manipulation To manipulate to create a custom layout

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d2 d1 d3 d4

Maintain proportional lengths To preserve data integrity

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Snap to a guide To support accurate data-driven drawing

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

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Architectural Design Drawing Logo Design User Interface Design

Designer’ Friends: Rulers, Grids, Guides

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Length and area guides can also be used as a Position guide.

Length guide

a

Area guide

d = length d = area

Default guide color: Cyan

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  • J. Bertin.,1983.

Rankings of visual variables by J. Mackinlay.,1986.

Fundamental channels for encoding information .

Visual variables

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  • J. Bertin.,1983.

Rankings of visual variables by J. Mackinlay.,1986.

Fundamental channels for encoding information .

Frequently used in infographics, although

  • ften misused.

Visual variables

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Use Cases

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Use cases

  • 1. Drawing data-driven graphics
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Cyan: Data-Driven Guides

Cyan: Data-Driven Guides

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Cyan: Data-Driven Guides

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Create & manipulate

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Link & Repeat

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Radial layout & link inspection

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Composite structure & copy and paste

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Label generation

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Deforms

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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.

Deforms

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Linear blend skinning Old point in a shape.

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 Deformed

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Rest pose 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.

Spatial Transformations (changes in guides)

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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.

Skinning weight (Transformation amount) Rest pose Deformed

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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.

New points in a shape. Rest pose Deformed

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Deforms

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Moves Moves

Used as a 
 Position Guide

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Used as flexible rulers.

Cyan: Data-Driven Guides

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Cyan: Data-Driven Guides

Four area guides are used to encode a single shape.

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Use cases

  • 1. Drawing data-driven graphics
  • 2. Retargeting existing artworks
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Original By Nigel Holmes

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Original By Nigel Holmes

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Use cases

  • 1. Drawing data-driven graphics
  • 2. Retargeting existing artworks
  • 3. Proofreading existing infographics
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Proofreading existing infographics

By Nigel Holmes. By Tiffany Farrant-Gonzalez.

Cyan: Data-Driven Guides

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Proofreading existing infographics

By Nigel Holmes.

Original The length of marks do not match the length of guides (curved).

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Proofreading existing infographics

By Tiffany Farrant-Gonzalez.

Area guide Length guide (Radius) Original

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Participants

  • 13 master student designers


(architecture, urban planning, and infographic design etc)

Informal usability evaluation with designers.

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Participants

  • 13 master student designers


(architecture, urban planning, and infographic design etc)

  • 2 ~ 10 years of experience in

infographic design.

Informal usability evaluation with designers.

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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).




Informal usability evaluation with designers.

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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).




Informal usability evaluation with designers.

Procedure

  • A 60 min session with a 15 min

tutorial

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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).




Informal usability evaluation with designers.

Procedure

  • A 60 min session with a 15 min

tutorial

  • Pre-task and post-task surveys
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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).




Informal usability evaluation with designers.

Procedure

  • A 60 min session with a 15 min

tutorial

  • Pre-task and post-task surveys
  • 2 replication tasks
  • 1 creative task


 * We did not measure time

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Data: GDP of G5 Countries. Creative Tasks

  • Two selected infographics

created by participants.

Results

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Creative Tasks

  • Two selected infographics

created by participants.

Results

Post-task surveys (5-point Likert scale)

  • 1. Interactions with DDG were intuitive.


(μ=4.0, σ=0.71)
 Data: GDP of G5 Countries.

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Creative Tasks

  • Two selected infographics

created by participants.

Results

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.

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Creative Tasks

  • Two selected infographics

created by participants.

Results

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
 (μ=4.9, σ=0.38) Data: GDP of G5 Countries.

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“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.

Results: qualitative feedback

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“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.

Results: qualitative feedback

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“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.

Results: qualitative feedback

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

Limitations

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

Limitations

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  • 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.

Limitations

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Template

Future work

  • 1. Reusable creative infographic templates.
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Template

Future work

  • 1. Reusable creative infographic templates.
  • 2. Data-driven guides for other visual variables.

Color, angle, shape, slope etc.

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Template

Future work

  • 1. Reusable creative infographic templates.
  • 2. Data-driven guides for other visual variables.

Color, angle, shape, slope etc.

  • 3. Intelligent systems for automatically providing design feedback.

Area vs radius Incorrect size

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Data Driven Guides:

Supporting expressive design for Information graphics

www.namwkim.org/ddg