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Critique / Models of Good Data Visualization

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This Week

  • Review common rules & mistakes.
    • @@ -25,7 +21,7 @@

Like any kind of visual art, creating good visualizations takes learning to see.

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Reminder: Color

  • Appropriate & consistent use of color for data type.
    • @@ -34,7 +30,7 @@
  • Accessibility: contrast, color vision deficiency, legibility
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Reminder: Data / Chart Integrity

  • Data variation, not design variation.
    • @@ -44,7 +40,7 @@
  • Alignment/Proximity: make comparisons easy & avoid misleading choices like changing axes between two otherwise identical graphs
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What to Look For

Expressiveness: Does every choice further the understanding of the data?

Effectiveness: Can audience decipher data and meaning quickly?

@@ -55,7 +51,7 @@
  • Appropriate use of grouping/sorting.
    • -
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    Critique

    • What is the purpose of critique?
      • @@ -63,7 +59,7 @@
    • Taking Critique
    -
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    Purpose of Critique

    You are the expert on your data source, and it can be challenging to look at things through the eyes of your audience.

    It is likely that you've stared at variations of the same chart for an hour or more, easily overlooking small flaws.

    @@ -75,40 +71,40 @@
  • Consider alternative solutions.
    • -
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    Giving Critique

    Be specific. If your first impulse is "I like it/I don't like it" go deeper and connect with why. Does it violate principles? Does a color choice stress your eye?

    Give your interpretation. What do you take away from the graphic as-is. Where is your eye drawn? Check your understanding against intention.

    Balance positives and negatives. Find things that are working and incorporate those into feedback as well. It's easier on recipient but also important so that they don't get removed in revision.

    Offer alternatives. What could be done differently?

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    Critique Language

    "I like..." - point out what works.
    "I wish..." - frame negative feedback as ways that it could improve for audience.
    "What if..." - new ideas and alternatives to consider.

    Be respectful. Be mindful of your tone and word choice.

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    What to Avoid (Giving Critique)

    Comments on creator. "You always...", "What were you thinking?", etc. Stay focused on the work.

    Bike-shedding. Suggesting arbitrary changes based on your preferences. "If I made this, I'd have chosen green."

    https://bikeshed.org

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    Receiving Critique

    Listen without defensiveness. Do not interrupt initial feedback to defend/clarify your intentions, doing so will lead to missing important first impressions.

    Separate yourself from the work. Remember the context you are doing this in, your peer is giving you their time to improve your work.

    Ask clarifying questions. If you do not understand why their interpretation is what it is, ask! Take the opportunity to learn more about what a new viewer will see.

    Look for patterns. Feedback may be unique or contradictory. Focus on trends in the feedback that point to common issues.

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    What to Avoid (When Receiving Critique)

    Convincing. If someone misunderstood your viz, others will too. There's nothing to be gained by convincing them they actually do understand it.

    Assuming all feedback is correct. If you disagree with a piece of feedback, it is OK to thank the person for it and not incorporate that into your changes if you feel strongly it was off base. There's generally no need to debate them on the spot however.

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    Suggested Peer Critique Process

    1. @@ -122,7 +118,7 @@
    -
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    1. Offer constructive criticisms and other suggestions for improvements and things to try. Author should mostly listen at this point, take notes, avoid the urge to respond to each piece of feedback as offered.

      @@ -139,32 +135,32 @@
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    Practice Critique

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    Readings & Sources

    • Critiquing data visualizations: A discussion on channeling empathy and productivity
      • @@ -179,7 +175,7 @@ author: Florence Nightingale - legend? - labeling on the chart itself?

      DuBois hourglass -Pronounced De-bois +He pronounced his name De-bois not a pie chart! meant for direct comparison between the two types @@ -192,4 +188,4 @@ why are some slices larger than others? does the eye in the middle mislead?

      1% of 1% via <https://sunlightfoundation.com/2013/06/26/1pct_of_the_1pct_polarization/>

      Grammys via Ruben, <https://edstem.org/us/courses/68015/discussion/5452397>

      DiCaprio via Amber

      Will's NC Seats via <https://projects.fivethirtyeight.com/partisan-gerrymandering-north-carolina/>

      KFC from Minh

      hungarian immigration from Dorka

      military from Tori

      part 2 of military expenditure

      NZ

      gender

      ny radial

      tx radial

      iraq

      inequality by Yi-Huai

      Fox from Andrew

      tribes in texas from Echo

      github two issues: scale is dependent on person's max commits more importantly: what we choose to visualize has an effect

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      Visualizing Uncertainty

      CAPP 30239

      -
      -
      -
      +
      +
      +

      What causes uncertainty?

      1. measurement error - An instrument used has some non-perfect degree of accuracy. In a survey, this could be a poorly-worded question.
        2. @@ -29,33 +25,33 @@

      The result is that we have a range or distribution, where we want a number to use with one of our channels (Hue, X, Y, etc.).

      -
      +

      Challenges of Uncertainty

      Often left out, in part due to being hard to understand, and even harder to visualize.

      Omission however misleads audiences, especially where a lot of significant figures are included.

      Global Population Uncertainty: ±160 million people (2%)

      -
      +

      Challenges of Uncertainty

      Uncertainty estimates are simplified, often out of necessity.

      30% chance of rain: "A 30% chance that at least 0.01" of rain will fall somewhere within a given area over a 12 hour period."

      Do I bring an umbrella?

      -
      +

      Challenges of Uncertainty

      Complexity of visualization can overwhelm audience, obscure other meaning.

      From a data-ink ratio perspective, it is understandable why if the error bars do not seem relevant to a narrative, that they would be omitted.

      -
      +

      Including Uncertainty

      If omitting uncertainty misleads, it violates our prime directive of graphical integrity.

      The job then, is to find ways that are audience appropriate & don't obfuscate the meaning.

      The difficulty will be in resolving this tension.

      -
      +

      Common Techniques

      • Uncertainty as Probability
        • @@ -63,28 +59,28 @@
      • Confidence Bands
      -
      +

      Uncertainty As Probability

      Random waffle chart: works for cases with discrete outcomes.

      -
      +

      Uncertainty As Probability

      In practice, we often care about more than boolean outcome.

      -
      -
      -
      +
      +
      +

      Uncertainty of Point Estimates

      These work when we're focused on uncertainty around a particular outcome.

      Sometimes we need to show uncertainty around discrete measurements, or projections.

      -
      +

      Error Bars

      -
      +

      Error Bands

      line = alt.Chart(source).mark_line().encode(
     x='Year',
@@ -99,7 +95,7 @@ band = alt.Chart(source).mark_errorband(extent='c
 band + line
      -
      +

      Issues with Error Bars & Confidence Bands

      1. There is no pre-defined meaning of these intervals.
        @@ -108,35 +104,35 @@ band + line
      2. Restricted to 1D/2D dots. If variable being expressed is mapped to color, area, etc. then alternative presentations needed.
      -
      -
      +
      +

      Variations on Error Bars & Intervals

      -
      -
      +
      +

      -
      +

      Regression Uncertainty

      -
      +

      Regression Uncertainty

      -
      +

      Other Approaches

      -
      +

      Showing Multiple Futures

      -
      +

      -
      -

      Hurricane Uncertainity

      +
      +

      Hurricane Uncertainty

      @@ -146,30 +142,31 @@ band + line
      -
      +

      On Maps

      -
      +

      "Sketchiness"

      -
      +

      Animating Uncertainty

      -
      +

      References & Acknowledgements

      What is this trying to show? source: https://www.ipcc.ch/report/ar6/wg1/figures/chapter-3/figure-3-4/

      These are showing essentially the same thing, one shows individual models and the other uses some aggregates with confidence intervals. These are from the same page of the IPCC report.

      We can convert this to discrete measurements: quantile dot plot.

      source: fivethirtyeight

      when appropriate, can also be used to show multiple intervals

      care should be taken that distribution is indeed normal if curves/etc. chosen

      source: https://tamucoa.b-cdn.net/app/uploads/2021/10/House2011TrackUncertaintyVisualization.pdf

      source: https://www.e-education.psu.edu/geog486/sites/www.e-education.psu.edu.geog486/files/Lesson_07/Images/ex_vs_ont.PNG

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      Narratives

      CAPP 30239

      -
      +

      Today

      • What types of data visualization narratives exist?
        • @@ -25,7 +21,7 @@
      • Narrative Examples
      -
      +

      Crafting a Narrative

      Narrative = Visualization + Context

      Benefits

      @@ -52,7 +48,7 @@
      -
      +

      Storytelling

      Instead of a loosely connected series of images, take reader on a journey with a beginning, middle, and end.

        @@ -61,12 +57,12 @@
      • End: Conclusion and/or Further Questions
      -
      +

      Risks

      Oversimplification. Constraints of format can lead to the need to simplify, finding the right balance requires skill, practice, and knowledge of audience.

      Bias. All narratives require interpretation, which can be a source of bias. We should not compromise on graphical integrity to make our point, or suggest conclusions that are not supported by the data.

      -
      +

      Medium is the Message

      "[...]medium is the message. This is merely to say that the
      personal and social consequences of any medium— that is, of any extension of ourselves— result from the new scale that is introduced into our affairs by each extension of ourselves, or by any new technology."

      @@ -81,7 +77,7 @@ personal and social consequences of any medium— that is, of any extension of o
    • Web Interactives
      • -
      +

      News Article Format

      Lede/Lead - Opening paragraph. Summarize key ideas & grab attention.
      "Nut Graf" - Nutshell paragraph. Immediately follows lead. Gives overview of why topic is important, and introduce key ideas to be explored in article.

      @@ -89,7 +85,7 @@ personal and social consequences of any medium— that is, of any extension of o

      Example: Chicago Missing Persons Pulitzer-winning long form piece that CAPP Alum Aïcha Camara contributed to.

      -
      +

      "Print" Article

      Even if initially viewed on a screen, use static design so can be copied/printed/shared easily.

      No vital information in tooltips, animation, etc.

      @@ -98,7 +94,7 @@ personal and social consequences of any medium— that is, of any extension of o
    • Visual-centric, with explanatory text.
      • -
      +

      Infographic

      Can be built around a single chart, or combine many into a story.
      Typically a single-page image with an arrangement of graphics, tables, and brief narrative.

      @@ -107,12 +103,13 @@ Typically a single-page image with an arrangement of graphics, tables, and brief

      Examples:

      -
      -
      -
      -
      +
      +
      +
      +

      Live Presentation

      Few words, large fonts.

      @@ -121,7 +118,7 @@ Typically a single-page image with an arrangement of graphics, tables, and brief

      Print handouts if visualizations have lots of detail.

      -
      +

      Animations & Videos

      Animation can be used to show change, uncertainty, or relationships.

      Video can blend presentation with commentary and narrative.

      @@ -132,11 +129,11 @@ Typically a single-page image with an arrangement of graphics, tables, and brief
    • NYT Thanksgiving Flight Animations (thanks Krisha!)
      • -
      +

      Museum Interactives

      Blue Paradox @ MSI - Thanks Magdalena!

      -
      +

      Interactive Stories

      Can take advantage of web as a medium. Seamlessly blend text, images, audio and video, and interactive elements.

      Potential Benefits

      @@ -148,7 +145,7 @@ Typically a single-page image with an arrangement of graphics, tables, and brief

      Caveat: Can overwhelm with options, lose sight of what's important.

      -
      +

      Reducing Cognitive Load

      Make data pleasant, avoid rigidity of visualization designed for print.

      Animations, tooltips, drop-downs, sliders, zoom & pan, highlights.
      @@ -162,7 +159,7 @@ Used artfully, these can provide ways for complex data to be presented in a more

    • https://especiales.eldiario.es/spain-lives-in-flats/ (thanks Michael!)
      • -
      +

      Personalization For Reader

      Engage reader better by showing them the data most relevant to them first.

      Excellent way to drill down in large data sets to make it meaningful to reader.

      @@ -172,7 +169,7 @@ Used artfully, these can provide ways for complex data to be presented in a more
    • https://www.nytimes.com/interactive/2018/08/30/climate/how-much-hotter-is-your-hometown.html
      • -
      +

      Making Information Playful

      Give people tools to explore or run simulations on their own in the browser.
      Encourages exploration & engagement.

      @@ -182,9 +179,10 @@ Encourages exploration & engagement.

    • https://www.nytimes.com/interactive/2024/upshot/buy-rent-calculator.html
    • https://www.economist.com/big-mac-index (thanks Paul!)
    • https://pudding.cool/2017/05/song-repetition/ (thanks Daniel!)
      • +
    • https://bost.ocks.org/mike/algorithms/
      • -
      +

      Prompting Self-Reflection

      Encourage people to reflect upon expectations & experiences relevant to framing of story.

      NYT "You Draw It" Series:

      @@ -193,7 +191,7 @@ Encourages exploration & engagement.

    • https://www.nytimes.com/interactive/2015/05/28/upshot/you-draw-it-how-family-income-affects-childrens-college-chances.html
      • -
      +

      More Examples...

      NYT Snow Fall
      Google Public Data Explorer

      @@ -201,7 +199,7 @@ Encourages exploration & engagement.

    • pudding.cool - a data visualization magazine, with lots more inspiration
      • -
      +

      What Skills Do You Need?

      • Writing
        • @@ -210,7 +208,7 @@ Encourages exploration & engagement.

      • Subject-matter expertise
      -
      +

      SVG vs PNG vs JPG

      • SVG - Scalable Vector Graphics format.
        • @@ -218,7 +216,7 @@ Encourages exploration & engagement.

      • JPG - Image format that uses compression, suitable for photographs but not fine details in visualizations.
      -
      +

      Document Formats

      • PDF - Portable Document Format - A vector-based format aimed at entire documents. PDF documents are aligned to paper sizes, great for printing, but less flexible on modern devices.
        • @@ -227,7 +225,7 @@ Encourages exploration & engagement.

      • Markdown - Simplified markup language that can be converted to HTML or PDF with various tools. Offers much less control over formatting than other options.
      -
      +

      In Practice

      1. @@ -245,7 +243,7 @@ Encourages exploration & engagement.

      2. For print or PDF: The intermediate format used by a print design tool will be exported to a PDF of the appropriate size & resolution.
      -
      +

      Tools: Typesetting Languages

      • LaTeX - PDF-focused, almost anything via plugins/pandoc. @@ -258,7 +256,7 @@ Encourages exploration & engagement.

      • Quarto - Can use mix of Markdown, Python, R, and Julia. Jupyter notebook compatible. Can generate PDFs or HTML from same source document.
      -
      +

      Tools: Vector Graphics Editors

      • Inkscape - Free & Open Source SVG editor. My recommendation for this class if you don't have prior experience with other options.
        • @@ -267,11 +265,12 @@ Encourages exploration & engagement.

      Honorable Mentions

        +
      • Graphite - Mix of GUI/programmatic SVG editing.
      • BoxySVG - Online/browser-based SVG editor.
      • Canva - Popular for infographics, collection of free clip art/etc.
      -
      +

      References & Acknowledgements

      • Thanks to Alex Kale.
        • @@ -284,4 +283,4 @@ Encourages exploration & engagement.

        does Google Data explorer overwhelm the user? circa 2010 lots of attempts to create "one viz to rule them all"

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Examples: - - +- --- ![bg fit](baseball1.png) @@ -204,6 +204,7 @@ Examples: - - (thanks Paul!) - (thanks Daniel!) +- --- @@ -294,10 +295,10 @@ to create "one viz to rule them all" ### Honorable Mentions +- **[Graphite](https://graphite.rs/learn/)** - Mix of GUI/programmatic SVG editing. - **BoxySVG** - Online/browser-based SVG editor. - **Canva** - Popular for infographics, collection of free clip art/etc. - --- ## References & Acknowledgements