Tutorials

• T1: Critique & Redesign
• T2: Challenge & Sketching
• T3: Tools and data exploration
• T4: Storyboarding
• T5: Evaluation

T1: Critique & Redesign

Goals:

• Discover common flaws and mis-interpretations of visualizations
• Create visualization guidelines
• Design improved visualizations

Material: Charts

Tasks:

1. Analysis (10min)
   • Individually
   • Look at all the charts provided
   • list as many flaws as you can
2. Discussion (10 min)
   • With your neighbor,
   • discuss the flaws you found
   • compile a list of common flaws/pitfalls you found.
   • Give each pitfall
     • a name (e.g., ‘Axes truncated’)
     • and an implication of how this might affect people’s (mis)interpretation (‘People will mis-interpret differences between values on the y-axis. People might misinterpret actual sizes. Etc.’)
   • Discuss faults in class

break

1. Re-design (30min)
   • with your neighbor
   • pick 2 visualizations from the charts provided
   • for each visualization, sketch 2 possible redesigns
   • Argue with the guidelines (see above) and state where guidlines can be violated.
2. Guidelines (20min)
   • with your neighbor,
   • formulate visualization guidelines to overcome these flaws/pitfals. Use careful formulations: “Do X; “Avoid Y”; “If X, then Y”; “Keep in mind that X”, etc.
3. Show visulizations on wall (20min)

T2: Challenge + Sketching

DataVis2020_4-Sketching

1. Data Challenge: Draft a data challenge for the data you might think working with in assignment #2. A data challenge describes the context for your visualization project. Take a sheet and fold it twice so you get 4 areas. In each area, write notes about the following:
   1. Data:
      • What is my data about?
      • Where does it come from?
      • How is it characterized?
   2. Messages / Insigths / Facts
      • What am I looking for in the data?
      • What is the data showing?
      • What do I want to tell to the reader?
   3. Audience:
      • Who is my audience?
      • Why should they matter?
      • What do they know about topic?
      • What do they know about visualization?
   4. Context
      • Where does the audience engage with the visualization?
      • How do they engage? * Work out the challenge alone, then discuss with your neighbor(s) * Are there people with similar data and interest in class?
2. Sketching intro: This is a quick intro by your tutor to quickly express an idea through sketching
   1. Time a few seconds to draw what your tutor is telling you (~4 pieces)
   2. Draw your home country (10sec)
   3. Draw population density inside (20sec)
3. Sketching data: Sketch your own data or some small data sample provided by your tutor
   • Draw 4 quick sketches (2min each)
   • Discuss with your neighbors
   • Elaborate on 1 visualization
   • Discuss in class

T3: Tools and data exploration

• Get into groups of 3 students (will be facilitated). Decide on a rough topic and search for data.
• Decide which topic you want to work on (create a challenge sheet like in tutorial 2?)
• Look for data in the internet: what data do you need?
• Explore a data set of your choice, using any of the tools discussed in class (e.g., Rawgraph, Tableau, D3)

Discuss with your neighbor or group members

• which visualizations did you use?
• which insights did you find?
• how effective are the visualization you chose? What would need to be improved?
• How effective are the tool(s) you’re using? Which features and workflows are working well? Which tools and features are hard to use? Which tools would you recommend?

T4: Storyboarding

The tutorial will start with a brief introduction into data comics and storyboarding. Your task is to create a storyboard and narrative around your visualzation projct. In this tutorial, you will work in groups.

Tutorial Slides:

• DataVis2020_8-Storyboarding

Problem analysis

• What is your take-home message? (–> “Insight”)
• What do you want people to do? (–> “Action”)
• Who is your audience?
• Why do they care? (–> “Curiosity”)
• What does your audience know about the topic?
• What does your audience know about visualization and data analysis?

Narration

• Write down the main points in a small story This structure will help:
• Beginning
  • what is topic?
  • What is my data?
  • why does that matter?
• Middle
  • Which facts do you need to communicate?
  • Which visualizations do I need?
  • How can I explain each visualization?
• End
  • summary of important findings
  • take-home message
  • Call to action, if requires

Design

• Create either:
  • a storyboard,
  • the layout for an infographic,
  • design + interaction for an interactive application
• walk the entire group through your findings.

T5: Evaluation

1. Visualization Critique (15min)

• Bring your visualizations from Assignment 1
• Exchange your visualization and give constructive and critical feedback. Use the heuristics from the Evaluation lecture as well as all the other lectures. Give ideas for improvement
• 10min critiquing + 5min discussion

2. Checklist and evaluation of group project (30min)

• Create a checklist of at least 10 items that can be used as a heuristic to evaluate a visualization
• Exchange that checklist with another group
• Evaluate your visualization according to the checklist that you have received
• For each item on the checklist, note down:
  • how much you fulfill the advice / guideline / item (1-5 Likert scale)
  • what you could you do to improve
• If time permits, exchange checklists again

3. Plan evaluation of group work (50min)

• for your group project and assignment 2, create an evaluation plan & questionnaire to evaluate your visualization
• Create:
  • Write a piece of context about your work so that your study participants get the context
  • Define tasks to ask the users (5)
  • Create a questionnaire that includes:
    • asking for background (if appropriate)
    • asking for feedback on visualization and tasks. How you ask for feedback and what questions you ask is entirely up to you. Make sure that you have thought about:
      • Which information do you need to improve your project? (e.g., can people understand X?)
      • What data do you need to get this information? (e.g., can people solve task Y, associated to feature X?)
      • What question to ask to get these data (e.g., ask a question Z that requires people to perform Y).