SlideShare a Scribd company logo
Elizabeth  Murnane	
Information Visualization
—  What  is  information  visualization?	
—  What  are  visualizations  good  for?	
—  Design  guidelines  &  techniques	
—  HCI              Visualization              User  Goals	
—  Responsibilities,  considerations,  alternate  solutions	
Today’s Plan
—  What  is  information  visualization?	
—  What  are  visualizations  good  for?	
—  Design  guidelines  &  techniques	
—  HCI              Visualization              User  Goals	
—  Responsibilities,  considerations,  alternate  solutions	
Today’s Plan
—  What  is  information  visualization?	
—  What  are  visualizations  good  for?	
—  Design  guidelines  &  techniques	
—  HCI              Visualization              User  Goals	
—  Responsibilities,  considerations,  alternate  solutions	
Today’s Plan
—  What  is  information  visualization?	
—  What  are  visualizations  good  for?	
—  Design  guidelines  &  techniques	
—  HCI              Visualization              User  Goals	
—  Responsibilities,  considerations,  alternate  solutions	
Today’s Plan
—  What  is  information  visualization?	
—  What  are  visualizations  good  for?	
—  Design  guidelines  &  techniques	
—  HCI              Visualization              User  Goals	
—  Responsibilities,  considerations,  alternate  solutions	
Today’s Plan
—  What  is  information  visualization?	
—  What  are  visualizations  good  for?	
—  Design  guidelines  &  techniques	
—  HCI              Visualization              User  Goals	
—  Responsibilities,  considerations,  alternate  solutions	
—  Have  fun!	
◦  Playing  with,  brainstorming  about,  and  evaluating  
visualizations	
Today’s Plan
—  Visualization:  “The  use  of  computer-­‐‑supported,  
interactive,  visual  representations  of  data  to  amplify  
cognition”  (Card  et  al.,  1998)	
—  Graphical  depiction  of  understandable  information	
◦  Transformation  of  data  to  information	
—  Mental  models	
—  Creates  an  “artificial  memory  that  best  supports  our  
natural  means  of  perception”  (Bertin)	
Key Concepts
—  Reason  about,  communicate,  document,  and  
preserve  knowledge  (Tufte)	
—  Quickly  understand  and  assimilate  information	
—  Gain  and  share  insights	
◦  Discovery  and  decision-­‐‑making	
◦  Explanation  and  dissemination	
—  Purpose  is  not  the  visualizations  themselves	
Why do we use visualizations?
—  Analyzing  information	
◦  Discover  paZerns  and  explore  trends	
◦  Determine  underlying  factors  and  notice  relationships	
◦  Reason,  plan,  problem-­‐‑solve	
—  Communicating  information	
◦  Present,  explain,  illustrate	
◦  Point  out  key  aspects  &  minimize  less  relevant  details	
◦  Education	
Viz Types: Viewing vs. Creating
An interactive meta-viz of Viz
Ralph  Lengler  &  Martin  J.  Eppler,  Towards  A  Periodic  
Table  of  Visualization  Methods  for  Management,  2007.	
Interactive  version  at:  www.visual-­‐‑literacy.org
—  Handle  the  expanding  volume  and  diversity  of  data	
—  Summarize,  organize,  and  incorporate  multiple  
layers  of  information  into  single  illustration	
—  An  aesthetic  and  appealing  format  makes  
comprehension  process  more  enjoyable	
Power of Visualization
Some classic examples
Napoleon’s March – Minard,1861
—  Illustrates  multiple  facets  of  the  data  (i.e.,  
geography,  time,  temperature,  army  size,  direction  
of  movement)	
—    Also  serves  as  a  record  of  the  data	
Napoleon’s March – Minard,1861
Cholera Epidemic
—  Norman:  “The  power  of  the  unaided  mind  is  highly  
overrated”	
—  Visualizations  aid  thinking	
◦  Increase  human  perceptual  processing  and  aZention	
◦  Expand  our  working  memory	
◦  Reduce  the  search  for  information	
◦  Enhance  our  ability  to  recognize  paZerns	
◦  Help  us  notice  irregularities  and  anomalies	
Amplifying Human Cognition
—  Multiply  66  x  43  in  your  head	
Multiplication
—  Multiply  66  x  43  in  your  head	
—  Multiply  66  x  43  on  paper	
Multiplication
—  Multiply  66  x  43  in  your  head	
—  Multiply  66  x  43  on  paper	
—  People  perform  5  times  faster  with  the  visual  aid	
Multiplication
—  Norman:  “The  power  of  the  unaided  mind  is  highly  
overrated”	
—  Visualizations  aid  thinking	
◦  Increase  human  perceptual  processing  and  aZention	
◦  Expand  our  working  memory	
◦  Reduce  the  search  for  information	
◦  Enhance  our  ability  to  recognize  paZerns	
◦  Help  us  notice  irregularities  and  anomalies	
Amplifying Human Cognition
What’s interesting here?
What’s interesting here?
Revealing Correlation
Revealing Outliers
v HCI+Viz:  Orient  visualizations  around  users  and  tasks,  
not  visualizations  themselves	
v Schneiderman  /  Carr	
—  Overview	
—  Zoom	
—  Filter	
—  Details-­‐‑on-­‐‑demand	
—  Relate	
—  History	
—  Extract	
Fulfilling User Tasks
v  Metaphors	
v  Tufte’s  Rules	
v  Gestalt  theories  of  form  and  configuration	
v  CRAP:  Contrast,  Repetition,  Alignment,  Proximity	
	
—  Utilize  multi-­‐‑functioning  graphical  elements	
◦  intuitive  cues  that  convey  information	
◦  meaning  through  shape,  size,  location,  color,  orientation,  motion	
—  Use  small  multiples	
◦  repetition,  similarity,  invite  comparison	
—  Show  process  and  causality	
—  Separate  and  layer	
◦  stratify,  order,  relate	
—  Use  color  effectively	
◦  highlight,  distinguish,  show  selection	
—  Avoid  extraneous  “junk”  components  that  add  cluZer  and  confusion	
◦  information  overload,  disruptive  with  no  purpose,  “above  all,  do  no  harm”	
Some Principles for Viz Design
Infographic Advertising from Honda
—  Informative  /  Aesthetic	
—  Dynamic  /  Static	
—  Interactivity	
—  Appropriateness  given  data,  domain,  application	
—  Alternative  sensory  inputs	
—  Social  visualization  &  transparency,  ambiguity,  
behavior	
Considerations and Choices
Baby Name Voyager
Facebook Friend Wheel
—  Schneiderman:  "ʺStatistics  alone  are  dangerous  and  they  
hide  a  lot”	
◦  Viz  can  help  reveal  problems  otherwise  hard  to  detect	
—  Heer:  Important  we  also  uncover,  assess,  and  verify  a    
visualization’s  credibility	
◦  Provide  interactivity  and  feedback	
—  Tufte:  “Graphical  integrity”	
—  Lie  Factor  &  exaggeration	
—  Careful  of  size,  area,  volume,  perspective,  baseline,  context	
—  Distortion  ever  useful?	
	
Truth in Visualization
Misleading Graphics
Stock  Market  Crash?!
Show full scale
Show context
—  Values  and  goals	
—  Good,  bad,  interesting,  effective,  informative,  overly  
complicated,  visually  appealing?	
—  Appropriate  graphical  representation  for  the  data?	
—  Who  are  the  users?	
—  Accessibility	
—  Methods  of  evaluation	
—  Testing  designs  with  people	
Evaluating Visualizations
Treemap
US Presidential Speeches Tag Cloud
isbarackobamathepresident.com
Oakland Crimespotting
hZp://oakland.crimespoZing.org/
—  hZp://visual.ly/	
◦  Info  graphics  &  data  viz  centered  community	
◦  Search  and  explore  visualizations  for  information  and  inspiration,  set  up  a  portfolio  
of  your  own  work  to  share,  and  follow  and  connect  with  other  designers	
◦  Offers  blog  with  posts  about  trends,  tools,  tips,  opportunities,  and  stories	
—  hZp://www.visualizing.org/	
◦  View  a  gallery  of  visualizations  or  upload  and  showcase  your  own	
◦  Enter  challenges  to  create  visualizations  from  a  given  dataset.  New  challenges  open  
up  all  the  time:  hZp://www.visualizing.org/contests/visualize-­‐‑us-­‐‑election	
—  hZp://www.google.com/publicdata/directory	
◦  Google'ʹs  visualization  engine  that  offers  an  online  tool  to  interactively  explore  and  
visualize  data.  	
◦  Use  public  datasets  from  around  the  world  or  upload  your  own  data	
—  hZp://www.informationisbeautiful.net/	
—  hZp://www.coolinfographics.com/	
—  hZp://infosthetics.com/	
Additional Resources

More Related Content

What's hot

Team building insights from artificial intelligence
Team building insights from artificial intelligenceTeam building insights from artificial intelligence
Team building insights from artificial intelligence
Robert Roan
 

What's hot (13)

August Designstorm: Alternative Reporting Formats
August Designstorm: Alternative Reporting FormatsAugust Designstorm: Alternative Reporting Formats
August Designstorm: Alternative Reporting Formats
 
Introduction to Knowledge Management
Introduction to Knowledge ManagementIntroduction to Knowledge Management
Introduction to Knowledge Management
 
Information Architecture Workshop
Information Architecture WorkshopInformation Architecture Workshop
Information Architecture Workshop
 
Km In The Public Sector Satst
Km In The Public Sector   SatstKm In The Public Sector   Satst
Km In The Public Sector Satst
 
Humans and Intelligent Machines - The Cognitive Fabric Agenda
Humans and Intelligent Machines - The Cognitive Fabric AgendaHumans and Intelligent Machines - The Cognitive Fabric Agenda
Humans and Intelligent Machines - The Cognitive Fabric Agenda
 
Thought Leadership And Wisdom London
Thought Leadership And Wisdom LondonThought Leadership And Wisdom London
Thought Leadership And Wisdom London
 
Information Outlook Journey Mapping Article - "Connecting People To Other Peo...
Information Outlook Journey Mapping Article - "Connecting People To Other Peo...Information Outlook Journey Mapping Article - "Connecting People To Other Peo...
Information Outlook Journey Mapping Article - "Connecting People To Other Peo...
 
Storytelling & Knowledge Management
Storytelling & Knowledge ManagementStorytelling & Knowledge Management
Storytelling & Knowledge Management
 
Team building insights from artificial intelligence
Team building insights from artificial intelligenceTeam building insights from artificial intelligence
Team building insights from artificial intelligence
 
Eliciting Tacit Knowledge for Learning
Eliciting Tacit Knowledge for LearningEliciting Tacit Knowledge for Learning
Eliciting Tacit Knowledge for Learning
 
IT Leadership: The People Domain
IT Leadership: The People DomainIT Leadership: The People Domain
IT Leadership: The People Domain
 
Strategic Management
Strategic ManagementStrategic Management
Strategic Management
 
Building Strategy Using Data-Derived Insights: Major Gifts
Building Strategy Using Data-Derived Insights: Major GiftsBuilding Strategy Using Data-Derived Insights: Major Gifts
Building Strategy Using Data-Derived Insights: Major Gifts
 

Similar to Info Viz by Liz

1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...
1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...
1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...
KrzysztofLada
 
Allstate Foundation
Allstate FoundationAllstate Foundation
Allstate Foundation
Beth Kanter
 
Data Visualisation Sara Miller McCune founded SAGE
Data Visualisation Sara Miller McCune founded SAGEData Visualisation Sara Miller McCune founded SAGE
Data Visualisation Sara Miller McCune founded SAGE
OllieShoresna
 

Similar to Info Viz by Liz (20)

Data visualization introduction
Data visualization introductionData visualization introduction
Data visualization introduction
 
Ideation through research
Ideation through researchIdeation through research
Ideation through research
 
Data visualization research project
Data visualization research projectData visualization research project
Data visualization research project
 
Deep Customer Insights, Laurea, October 2015
Deep Customer Insights, Laurea, October 2015 Deep Customer Insights, Laurea, October 2015
Deep Customer Insights, Laurea, October 2015
 
Data Visualisation Literacy - Learning to See
Data Visualisation Literacy - Learning to SeeData Visualisation Literacy - Learning to See
Data Visualisation Literacy - Learning to See
 
The Best from the UX Summit in Chicago
The Best from the UX Summit in ChicagoThe Best from the UX Summit in Chicago
The Best from the UX Summit in Chicago
 
Social Media Measurement by Daniel Backhaus at Infuz
Social Media Measurement by Daniel Backhaus at InfuzSocial Media Measurement by Daniel Backhaus at Infuz
Social Media Measurement by Daniel Backhaus at Infuz
 
SMART Seminar Series: Learning Journeys – Making learning visible in developi...
SMART Seminar Series: Learning Journeys – Making learning visible in developi...SMART Seminar Series: Learning Journeys – Making learning visible in developi...
SMART Seminar Series: Learning Journeys – Making learning visible in developi...
 
Finding, Evaluating, Understanding, and Using Information: Information Litera...
Finding, Evaluating, Understanding, and Using Information: Information Litera...Finding, Evaluating, Understanding, and Using Information: Information Litera...
Finding, Evaluating, Understanding, and Using Information: Information Litera...
 
Introduction to Data Visualisation
Introduction to Data VisualisationIntroduction to Data Visualisation
Introduction to Data Visualisation
 
Inclusive design workshop
Inclusive design workshopInclusive design workshop
Inclusive design workshop
 
Systemic Learning Analytics Symposium, October 10th 2013
Systemic Learning Analytics Symposium, October 10th 2013Systemic Learning Analytics Symposium, October 10th 2013
Systemic Learning Analytics Symposium, October 10th 2013
 
Meandering in-civic-intelligence.reduced
Meandering in-civic-intelligence.reducedMeandering in-civic-intelligence.reduced
Meandering in-civic-intelligence.reduced
 
1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...
1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...
1 dokumen.tips_empathize-ideate-design-thinking-define-prototype-little-time-...
 
Infographics for the social sector webinar
Infographics for the social sector webinarInfographics for the social sector webinar
Infographics for the social sector webinar
 
Data Literacy at IFRC 2017
Data Literacy at IFRC 2017Data Literacy at IFRC 2017
Data Literacy at IFRC 2017
 
Allstate Foundation
Allstate FoundationAllstate Foundation
Allstate Foundation
 
Data is love data viz best practices
Data is love   data viz best practicesData is love   data viz best practices
Data is love data viz best practices
 
Identifying Your Audience
Identifying Your AudienceIdentifying Your Audience
Identifying Your Audience
 
Data Visualisation Sara Miller McCune founded SAGE
Data Visualisation Sara Miller McCune founded SAGEData Visualisation Sara Miller McCune founded SAGE
Data Visualisation Sara Miller McCune founded SAGE
 

Recently uploaded

Search and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical FuturesSearch and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical Futures
Bhaskar Mitra
 

Recently uploaded (20)

Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
 
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMsTo Graph or Not to Graph Knowledge Graph Architectures and LLMs
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
 
Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*Neuro-symbolic is not enough, we need neuro-*semantic*
Neuro-symbolic is not enough, we need neuro-*semantic*
 
The Future of Platform Engineering
The Future of Platform EngineeringThe Future of Platform Engineering
The Future of Platform Engineering
 
Search and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical FuturesSearch and Society: Reimagining Information Access for Radical Futures
Search and Society: Reimagining Information Access for Radical Futures
 
From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...
From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...
From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...
 
IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024IoT Analytics Company Presentation May 2024
IoT Analytics Company Presentation May 2024
 
ODC, Data Fabric and Architecture User Group
ODC, Data Fabric and Architecture User GroupODC, Data Fabric and Architecture User Group
ODC, Data Fabric and Architecture User Group
 
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptxUnpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
Unpacking Value Delivery - Agile Oxford Meetup - May 2024.pptx
 
Speed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in MinutesSpeed Wins: From Kafka to APIs in Minutes
Speed Wins: From Kafka to APIs in Minutes
 
UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3UiPath Test Automation using UiPath Test Suite series, part 3
UiPath Test Automation using UiPath Test Suite series, part 3
 
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
 
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
From Daily Decisions to Bottom Line: Connecting Product Work to Revenue by VP...
 
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
AI for Every Business: Unlocking Your Product's Universal Potential by VP of ...
 
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
 
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualitySoftware Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
 
Exploring UiPath Orchestrator API: updates and limits in 2024 🚀
Exploring UiPath Orchestrator API: updates and limits in 2024 🚀Exploring UiPath Orchestrator API: updates and limits in 2024 🚀
Exploring UiPath Orchestrator API: updates and limits in 2024 🚀
 
Quantum Computing: Current Landscape and the Future Role of APIs
Quantum Computing: Current Landscape and the Future Role of APIsQuantum Computing: Current Landscape and the Future Role of APIs
Quantum Computing: Current Landscape and the Future Role of APIs
 
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdfFIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
 
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
 

Info Viz by Liz

  • 2. —  What  is  information  visualization? —  What  are  visualizations  good  for? —  Design  guidelines  &  techniques —  HCI              Visualization              User  Goals —  Responsibilities,  considerations,  alternate  solutions Today’s Plan
  • 3. —  What  is  information  visualization? —  What  are  visualizations  good  for? —  Design  guidelines  &  techniques —  HCI              Visualization              User  Goals —  Responsibilities,  considerations,  alternate  solutions Today’s Plan
  • 4. —  What  is  information  visualization? —  What  are  visualizations  good  for? —  Design  guidelines  &  techniques —  HCI              Visualization              User  Goals —  Responsibilities,  considerations,  alternate  solutions Today’s Plan
  • 5. —  What  is  information  visualization? —  What  are  visualizations  good  for? —  Design  guidelines  &  techniques —  HCI              Visualization              User  Goals —  Responsibilities,  considerations,  alternate  solutions Today’s Plan
  • 6. —  What  is  information  visualization? —  What  are  visualizations  good  for? —  Design  guidelines  &  techniques —  HCI              Visualization              User  Goals —  Responsibilities,  considerations,  alternate  solutions Today’s Plan
  • 7. —  What  is  information  visualization? —  What  are  visualizations  good  for? —  Design  guidelines  &  techniques —  HCI              Visualization              User  Goals —  Responsibilities,  considerations,  alternate  solutions —  Have  fun! ◦  Playing  with,  brainstorming  about,  and  evaluating   visualizations Today’s Plan
  • 8. —  Visualization:  “The  use  of  computer-­‐‑supported,   interactive,  visual  representations  of  data  to  amplify   cognition”  (Card  et  al.,  1998) —  Graphical  depiction  of  understandable  information ◦  Transformation  of  data  to  information —  Mental  models —  Creates  an  “artificial  memory  that  best  supports  our   natural  means  of  perception”  (Bertin) Key Concepts
  • 9. —  Reason  about,  communicate,  document,  and   preserve  knowledge  (Tufte) —  Quickly  understand  and  assimilate  information —  Gain  and  share  insights ◦  Discovery  and  decision-­‐‑making ◦  Explanation  and  dissemination —  Purpose  is  not  the  visualizations  themselves Why do we use visualizations?
  • 10. —  Analyzing  information ◦  Discover  paZerns  and  explore  trends ◦  Determine  underlying  factors  and  notice  relationships ◦  Reason,  plan,  problem-­‐‑solve —  Communicating  information ◦  Present,  explain,  illustrate ◦  Point  out  key  aspects  &  minimize  less  relevant  details ◦  Education Viz Types: Viewing vs. Creating
  • 11. An interactive meta-viz of Viz Ralph  Lengler  &  Martin  J.  Eppler,  Towards  A  Periodic   Table  of  Visualization  Methods  for  Management,  2007. Interactive  version  at:  www.visual-­‐‑literacy.org
  • 12. —  Handle  the  expanding  volume  and  diversity  of  data —  Summarize,  organize,  and  incorporate  multiple   layers  of  information  into  single  illustration —  An  aesthetic  and  appealing  format  makes   comprehension  process  more  enjoyable Power of Visualization
  • 14. Napoleon’s March – Minard,1861
  • 15. —  Illustrates  multiple  facets  of  the  data  (i.e.,   geography,  time,  temperature,  army  size,  direction   of  movement) —   Also  serves  as  a  record  of  the  data Napoleon’s March – Minard,1861
  • 17. —  Norman:  “The  power  of  the  unaided  mind  is  highly   overrated” —  Visualizations  aid  thinking ◦  Increase  human  perceptual  processing  and  aZention ◦  Expand  our  working  memory ◦  Reduce  the  search  for  information ◦  Enhance  our  ability  to  recognize  paZerns ◦  Help  us  notice  irregularities  and  anomalies Amplifying Human Cognition
  • 18. —  Multiply  66  x  43  in  your  head Multiplication
  • 19. —  Multiply  66  x  43  in  your  head —  Multiply  66  x  43  on  paper Multiplication
  • 20. —  Multiply  66  x  43  in  your  head —  Multiply  66  x  43  on  paper —  People  perform  5  times  faster  with  the  visual  aid Multiplication
  • 21. —  Norman:  “The  power  of  the  unaided  mind  is  highly   overrated” —  Visualizations  aid  thinking ◦  Increase  human  perceptual  processing  and  aZention ◦  Expand  our  working  memory ◦  Reduce  the  search  for  information ◦  Enhance  our  ability  to  recognize  paZerns ◦  Help  us  notice  irregularities  and  anomalies Amplifying Human Cognition
  • 26. v HCI+Viz:  Orient  visualizations  around  users  and  tasks,   not  visualizations  themselves v Schneiderman  /  Carr —  Overview —  Zoom —  Filter —  Details-­‐‑on-­‐‑demand —  Relate —  History —  Extract Fulfilling User Tasks
  • 27. v  Metaphors v  Tufte’s  Rules v  Gestalt  theories  of  form  and  configuration v  CRAP:  Contrast,  Repetition,  Alignment,  Proximity —  Utilize  multi-­‐‑functioning  graphical  elements ◦  intuitive  cues  that  convey  information ◦  meaning  through  shape,  size,  location,  color,  orientation,  motion —  Use  small  multiples ◦  repetition,  similarity,  invite  comparison —  Show  process  and  causality —  Separate  and  layer ◦  stratify,  order,  relate —  Use  color  effectively ◦  highlight,  distinguish,  show  selection —  Avoid  extraneous  “junk”  components  that  add  cluZer  and  confusion ◦  information  overload,  disruptive  with  no  purpose,  “above  all,  do  no  harm” Some Principles for Viz Design
  • 28.
  • 30. —  Informative  /  Aesthetic —  Dynamic  /  Static —  Interactivity —  Appropriateness  given  data,  domain,  application —  Alternative  sensory  inputs —  Social  visualization  &  transparency,  ambiguity,   behavior Considerations and Choices
  • 31.
  • 34. —  Schneiderman:  "ʺStatistics  alone  are  dangerous  and  they   hide  a  lot” ◦  Viz  can  help  reveal  problems  otherwise  hard  to  detect —  Heer:  Important  we  also  uncover,  assess,  and  verify  a     visualization’s  credibility ◦  Provide  interactivity  and  feedback —  Tufte:  “Graphical  integrity” —  Lie  Factor  &  exaggeration —  Careful  of  size,  area,  volume,  perspective,  baseline,  context —  Distortion  ever  useful? Truth in Visualization
  • 38. —  Values  and  goals —  Good,  bad,  interesting,  effective,  informative,  overly   complicated,  visually  appealing? —  Appropriate  graphical  representation  for  the  data? —  Who  are  the  users? —  Accessibility —  Methods  of  evaluation —  Testing  designs  with  people Evaluating Visualizations
  • 40.
  • 42.
  • 45. —  hZp://visual.ly/ ◦  Info  graphics  &  data  viz  centered  community ◦  Search  and  explore  visualizations  for  information  and  inspiration,  set  up  a  portfolio   of  your  own  work  to  share,  and  follow  and  connect  with  other  designers ◦  Offers  blog  with  posts  about  trends,  tools,  tips,  opportunities,  and  stories —  hZp://www.visualizing.org/ ◦  View  a  gallery  of  visualizations  or  upload  and  showcase  your  own ◦  Enter  challenges  to  create  visualizations  from  a  given  dataset.  New  challenges  open   up  all  the  time:  hZp://www.visualizing.org/contests/visualize-­‐‑us-­‐‑election —  hZp://www.google.com/publicdata/directory ◦  Google'ʹs  visualization  engine  that  offers  an  online  tool  to  interactively  explore  and   visualize  data.   ◦  Use  public  datasets  from  around  the  world  or  upload  your  own  data —  hZp://www.informationisbeautiful.net/ —  hZp://www.coolinfographics.com/ —  hZp://infosthetics.com/ Additional Resources