NEWM - N328 Visualizing Information Khairi Reda | redak@iu.edu - - PowerPoint PPT Presentation

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NEWM - N328 Visualizing Information Khairi Reda | redak@iu.edu - - PowerPoint PPT Presentation

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NEWM - N328 Visualizing Information Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI Welcome! This course is about Data Visualiza1on Defining Visualization The transforma5on of data into visual representa1ons to aid

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NEWM - N328 Visualizing Information

Khairi Reda | redak@iu.edu School of Informa5cs & Compu5ng, IUPUI

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Welcome!

This course is about Data Visualiza1on

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Defining Visualization

  • The transforma5on of data into visual

representa1ons to aid people in the analysis, explora5on, and communica5on of that data

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  • data
  • visual representa5ons
  • people
  • computers
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  • data
  • visual representa5ons
  • people
  • computers
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Healthcare

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Financial markets

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Scien1fic instruments: Hubble Space Telescope

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Social Media

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How much data are we generating?

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In 2010: 1,200 exabytes 1 exabyte = 1,000,000,000,000,000,000 bytes 1 exabyte = 1018 bytes 10x increase every 5 years

Based on slide by Jeff Heer

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The problem

Based on a slide by John Stasko / Chris North

Data

Web, Books, Papers, Scientific data, News, Product reviews, …

How?

Sight, Hearing, Touch, Smell, Taste, Telepathy?

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Why use vision to analyze information?

  • 1. Vision is the highest bandwidth channel into our brain
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Sensory bandwidth

  • T. Norretranders, The User Illusion: Cutting

Consciousness Down to Size, 1999

How much we are ac5vely aware of

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  • 1. Vision is the highest bandwidth channel into our brain
  • 2. Cogni5on is limited. Visual percep5on beats cogni5on

Why use vision to analyze information?

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345OIJDFG98C90U5ET09VBKK23490XIVBCIBJ0345T09U 2G84GDF09U34590IDFK90345I-09345K90FU90DF90JDF 34T09X90DFJG90J34T09J34509J3459DFG08JKLSTJP435 DFDFG45OJERPOTJ45OPIJFDGLKM34T5XJSCTYY7K456 POJ345OIJLGJKOPE390UVFHUDGH9345H9R4N97HWTIO MADSIOPEJDFGPJ4309UT509345PODFGX093490823JFD PWDEIJ3408UDFMV984385Y0834N92384YU8DFB0H3T4N 345J09JDFG09J345X98U5Y09JGFB089H34509UJ45TM0IG P5JDGIOEGWJPIO345U345OPIJDTOPI3458345JPODFG09 45POJ34X09345J08EFJ825HJDFSJIPADOPQWIXERWNVF

Based on a slide by John Stasko

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345OIJDFG98C90U5ET09VBKK23490XIVBCIBJ0345T09U 2G84GDF09U34590IDFK90345I-09345K90FU90DF90JDF 34T09X90DFJG90J34T09J34509J3459DFG08JKLSTJP435 DFDFG45OJERPOTJ45OPIJFDGLKM34T5XJSCTYY7K456 POJ345OIJLGJKOPE390UVFHUDGH9345H9R4N97HWTIO MADSIOPEJDFGPJ4309UT509345PODFGX093490823JFD PWDEIJ3408UDFMV984385Y0834N92384YU8DFB0H3T4N 345J09JDFG09J345X98U5Y09JGFB089H34509UJ45TM0IG P5JDGIOEGWJPIO345U345OPIJDTOPI3458345JPODFG09 45POJ34X09345J08EFJ825HJDFSJIPADOPQWIXERWNVF

Based on a slide by John Stasko

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Given these 50 numbers what number appears most oQen?

Slide by Miriah Meyer, University of Utah http://www.cs.utah.edu/~miriah/teaching/cs6630/

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Given these 50 numbers what number appears most oQen?

Slide by Miriah Meyer, University of Utah http://www.cs.utah.edu/~miriah/teaching/cs6630/

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How has the unemployment rate in the US changed since 2006?

Seasonally adjusted unemployment rate in the US Bureau of Labor Statistics

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How is the unemployment rate in the US changing since 2006?

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Why use vision to analyze information?

  • 1. Vision is the highest bandwidth channel into our brain
  • 2. Cogni5on is limited. Percep5on beats cogni5on
  • 3. Working memory is limited; external representa5ons

expand our memory

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26 57 x

26

57x 2 130 + 1482

4

18

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The world is its

  • wn memory

http://www.femside.com/wp-content/uploads/2013/12/desk-notes-work-office-computer.jpg

“It’s things that make us smart” -Don Norman

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Why use vision to analyze information?

  • 1. Vision is the highest bandwidth channel into our brain
  • 2. Cogni5on is limited. Percep5on beats cogni5on
  • 3. Working memory is limited; external representa5ons

expand our memory

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Why use vision to analyze information?

  • 1. Vision is the highest bandwidth channel into our brain
  • 2. Cogni5on is limited. Percep5on beats cogni5on
  • 3. Working memory is limited; external representa5ons

expand our memory

  • 4. Visuals are an integral part of our culture
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  • “I see what you’re saying”
  • “Seeing is believing”
  • “I now see the big picture”
  • “A picture is worth a

thousand words”

Based on a slide by John Stasko

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  • data
  • visual representa1ons
  • people
  • computers
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  • data
  • visual representa5ons
  • people
  • computers
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Why involve people in the analysis of data?

  • Computers are very good at compu5ng an

answer to a specific ques1on

  • They are less useful good when we do not

know what we are looking for in advance

  • How does the employment market

react to changes in interest rate?

  • What is the effect of gene muta5on on

cancer risk?

  • Computers are bad at “hunches”
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  • data
  • visual representa5ons
  • people
  • computers
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  • data
  • visual representa5ons
  • people
  • computers
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  • Efficiency: Process large quan55es of informa5on

quickly

  • Interact with the data; zoom, filter, switch views, details
  • n demand
  • Quality: precise representa5on of the data
  • Re-use of code for different datasets

Why involve computers in the analysis of data?

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  • data
  • visual representa5ons
  • people
  • computers
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A little bit of history…

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Bailee Clift

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  • E. W. Gilbert. Simplified version of Snow’s map: http://www.ph.ucla.edu/epi/snow/cartographica39%284%291_14_2004.pdf
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Charles Menard. Napoleon’s 1812 campaign to Moscow

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Communication Analysis

  • When we don’t know

what to look for

  • When we don’t have a

priori question

  • When we know the facts

and want to show them to other people

  • When we want to tell a

story with data

How to use visualization

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What we will learn in this course

  • Understand when, why, and how to use visualiza5on for the

analysis of data

  • We will learn about a variety of visualiza5ons for a range of

data types

  • You will gain experience with modern visualiza5on tools to

create your own visualiza5ons

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Visual Percep1on and Cogni1on

Collin Ware

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Semiology of Graphics

  • J. Bertin

Visual Marks & Channels

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Visual representa1ons

InnoVis group at U Calgary Mike Bostock

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Maps and Geo-visualiza1ons

Mike Bostock

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Mike Bostock

Networks

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Yiwen Sun

UC Berkeley Visualization Lab

Interac1on techniques

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Required reading

Visualiza1on Analysis and Design Tamara Munzner Interac1ve Data Visualiza1on for the Web Scoc Murray

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http://khreda.com/teaching/2020/N328

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Percentage

Assignment 1 Intro to Tableu 15% Assignment 2 Exploratory Visual Analysis 20% Visualization Critique Rolling basis — starting from week 4 25% Final Project (group) 30% Participation in paper discussion and project critiques 10%

Grading

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Visualization Critique

  • Every week (star5ng from week 4), 2-3 students will be responsible for

selec5ng a visualiza5on and pos5ng a design cri5que on Canvas

  • You are responsible to post one design cri5que during the semester:
  • Find a visualiza5on from news web sources, textbooks, or scien5fic

literature

  • Explain the data being shown
  • Describe the visual encoding used in the visualiza5on: put into words what

the visualiza5on is trying to show and how.

  • Cri5que the visualiza5on: what works, and what doesn’t? Is the

visualiza5on clear, or is it misleading?

  • Do you like it? Why? What would you do to improve it?
  • Present the visualiza5on and cri5que to the class
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https://tinyurl.com/j2larrd

  • What is the visualiza5on about?
  • What informa5on is

represented in the visualiza5on? And how?

  • What are the interac5ons used?
  • What ques5ons can we answer

with the visualiza5on?

  • Do you like the visualiza5on?
  • Are there any improvements

that can be made to the design?

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Assignment 1: Intro to Tableau

Out today, due in two weeks

  • Use Tableau to visualize a simple dataset
  • Ask a meaningful ques5on about the data and

answer the ques5on by crea5ng a good chart

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coming up

Visual Integrity: How to not lie with visualiza5on

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Visual Integrity: How to not lie with visualization

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The representa5on of numbers, as physically measured on the surface of the graphic itself, should be directly propor5onal to the quan55es represented

Visual Integrity: How to not lie with visualization

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New York Times 8/9/1978, via Edward Tufte and Andy Johnson (https://www.evl.uic.edu/aej/424/)

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Lie Factor =

Size of the effect in pixels/inches Size of the effect in the data

(5.3-0.6) / 0.6 (27.5-18) / 18 = = 7.8 0.5 = 15.6

Effect size in graphics Effect size in data lie factor

New York Times 8/9/1978, via Edward Tufte and Andy Johnson (https://www.evl.uic.edu/aej/424/)

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0" 5" 10" 15" 20" 25" 30" 1978" 1979" 1980" 1981" 1982" 1983" 1984" 1985"

Miles"per"gallon"

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http://www.math.yorku.ca/SCS/Gallery/lie-factor.html Via Andy Johnson

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0" 10000" 20000" 30000" 40000" 50000" 60000" 70000"

1939" 41" 43" 45" 47" 49" 51" 53" 55" 57" 59" 61" 63" 65" 67" 69" 71" 73" 75" Male"professional" Physician"salary"

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0" 10000" 20000" 30000" 40000" 50000" 60000" 70000"

1939" 41" 43" 45" 47" 49" 51" 53" 55" 57" 59" 61" 63" 65" 67" 69" 71" 73" 75" Male"professional" Physician"salary"

Exponen5al growth Linear growth

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SLIDE 66 0" 10000" 20000" 30000" 40000" 50000" 60000" 70000"

1939" 41" 43" 45" 47" 49" 51" 53" 55" 57" 59" 61" 63" 65" 67" 69" 71" 73" 75" Male"professional" Physician"salary"

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Edward Tufte

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Chart junk

Visual elements in charts and graphs that are not necessary to comprehend the informa5on represented on the graph, or that district the viewer from this informa5on

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Via Andy Johnson (https://www.evl.uic.edu/aej/424/)

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Via Andy Johnson (https://www.evl.uic.edu/aej/424/)

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Based on reporting by the Guardian (https://www.theguardian.com/technology/blog/2008/jan/21/liesdamnliesandstevejobs) Via Miriah Meyer

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US smart phone marketshare

Slide by Miriah Meyer

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US smart phone marketshare

Slide by Miriah Meyer

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Space Shuttle Challenger Disaster

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By E. Tufte

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Nigel Holmes

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Mona Chalabi

“Informing without alienating”

Can chartjunk be useful?

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Chart junk: Bateman’s et al. study

Accuracy Recall

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Visual embellishment

Pros

Cons

  • Memorability
  • Engagement
  • Persuasion
  • Effec5ve communica5on of well-scoped messages
  • “Loading” visualiza5ons with messages
  • Poten5al for bias and stereotyping
  • Reduces accuracy of “reading” the data
  • Can taint trustworthiness of graphics

Based on slide by Miriah Meyer

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Via Stephen Few https://www.perceptualedge.com/articles/visual_business_intelligence/the_chartjunk_debate.pdf

Please, don’t do this

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Intro to Tableau

sample dataset:

tinyurl.com/storedata1