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Sea viz meetup 8 3-16 Content Matters. Context matters.
- 1. Content Matters. Context Matters. Sandlin Seguin, PhD | Senior eLearning Developer Aug 3, 2016 | sseguin@tableau.com | @SandlinSeguin
- 2. “In 2014, 52% of chemistry graduates had full-time work 6 months after graduation.” HESA Destination of Leavers survey, www.hesa.ac.uk
- 4. Why is data storytelling like learning?
- 5. Knowing Your Data: a Confession and an Appeal Principles of Adult Learning Three Key Ideas for Communicating with Data Pre-attentive Attributes Illusions that can Fluster Perception Agenda
- 7. An Appeal: Know Your Data
- 9. Principles of Adult Learning Adults need to be engaged in their own learning. Adults are more interested in learning where there is a direct impact on their life or work. Experience shapes how adults learn. Adults prefer problem-centered learning over content-oriented learning. Knowles, M. (1984). The Adult Learner: A Neglected Species (3rd Ed.). Houston, TX: Gulf Publishing. https://elearningindustry.com/the-adult-learning-theory-andragogy-of-malcolm-knowles
- 12. Principles of Adult Learning Adults need to be engaged in their own learning. Adults are more interested in learning where there is a direct impact on their life or work. Experience shapes how adults learn. Adults prefer problem-centered learning over content-oriented learning. Knowles, M. (1984). The Adult Learner: A Neglected Species (3rd Ed.). Houston, TX: Gulf Publishing.
- 13. Observation Hypothesis Test and Analyze Conclusions Use the Scientific Method As a Model for Stories
- 15. Tools from Adult Learning Use Words and Graphics Contiguity Coherence
- 19. Contiguity: Leverage Labels Clark and Mayer, E-Learning and the science of Instruction. John Wiley & Sons, Inc. 2011
- 21. Coherence: Keep it Focused Clark and Mayer, E-Learning and the science of Instruction. John Wiley & Sons, Inc. 2011
- 23. Use Words and Graphics Contiguity Coherence
- 35. Context Matters
- 36. Nickerson and Adams, Cognitive Psychology, v11 n3 p287-307 Jul 1979
- 37. Grab pick of Aarons Pre-attentive attributes
- 40. Checkerboard illusion By derivative work: Sakurambo (talk)Grey_square_optical_illusion.PNG - Grey_square_optical_illusion.PNG, Copyrighted free use, https://commons.wikimedia.org/w/index.php?curid=4443183
- 41. Checkerboard illusion By Original by Edward H. Adelson - File created by Adrian Pingstone, based on the original created by Edward H. Adelson, Copyrighted free use, https://commons.wikimedia.org/w/index.php?curid=45737683
- 42. It’s really hard to compare colors on a gradient
- 43. Stepped color can be better
- 44. Poggendorff Illusion By Fibonacci. - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=2073873
- 45. Poggendorff Illusion By Fibonacci. - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=2073873
- 46. The greatest shifts are between 500 and 580 nm
- 47. The greatest shifts are between 500 and 580 nm
- 48. • Desert fort Change Blindness
- 49. • Desert fort Change Blindness
- 50. Change Blindness
- 51. • Remember to communicate for adults. Problem-centered stories. • Use words, graphics, contiguity, and coherence. • Take advantage of pre-attentive attributes, but don’t abuse them. • Beware of relative comparisons of color and size. • Don’t disrupt the view. Conclusions
- 52. Thank you!
Editor's Notes
- When I was pulling this talk together, I found I had a lot of things to say about a lot of seemingly unrelated things. Which brings us to my title- Content matters. Context matters. I really want to talk about storytelling- the big things and the little things.
- Quote Let’s start with story telling. Facts might be interesting, but chances are, you want to know more. You want the story.
- Title Only You want the data in a context that helps it to make sense. We can see that 52% is an increase over previous years, and that only 6.7% were fully unemployed in 2014. In the world of data visualization, data story telling is a hot buzzword. In business, people seem to be in the bad habit of thinking that means cramming more data into presentations or something. Scientists have cultivated a really healthy attitude towards data, in that you generally get data with some purpose in mind, and you generally show data because it means something. https://public.tableau.com/views/HighereducationstatisticsDestinationofleavers/Destinationofleavers?:embed=y&:display_count=yes&:showTabs=y
- In this talk, I’m going to talk about storytelling and learning like they are two sides of the same coin. This is totally my bias- I think that storytelling is about knowledge transfer, and so we can leverage a lot of the good research on learning to improve uptake. But let me clarify why I think that- the modern version of storytelling is about framing and focusing information in a palatable way for consumption. Learning and knowledge transfer has more of an audience or student focus, but it’s the same struggle- how do I format information in a way that can be absorbed correctly? What scaffolding do I need to have in place before the learner can get to the aha moment? That’s how I think about it, and I’m hoping by the end of the talk, you’ll at least understand why.
- Table of Contents Is this all related? This all falls under the broad subject area of data visualization, and I’m talking about it tonight, so it’s related now.
- So let me just level a bit here. I’m a scientist, a biologist actually, and I’ve spend a lot of time getting data, analyzing data, talking about and presenting my conclusions… When I was a grad student, it used to drive me crazy how much time my boss would spend editing drafts of my figures- like this one, the first figure from my first, first-author paper. The back and forth we were doing on this figures felt like window dressing at best. He’d have me edit axis titles, bold text, unbold text, fatten trend lines, whatever… just to make the figure look more professional. So let’s assess what I put out there. Anyone want to hazard a guess about what’s going on here And I thought that was pretty self evident from the figures, so we didn’t bother with crass things like “labels not being cryptic” or “data points you can actually distinguish by shape.” I realize now that the bar we were shooting for was pretty low. We wanted the figure to look polished, not to actually be inherently understandable. And it wasn’t until I’d been working at Tableau for a while, taking our Visual analytics course (http://www.tableau.com/learn/classroom/visual-analytics), in fact, that I realized that just being clear and correct is not always the best way to show data. You need to be clear, correct and engaging, so that someone will spend more than 10 tortured seconds looking and your numbers. So I finally gave into the idea that it’s worth spending the time to make your data not just presentable, but beautiful and interesting. Not only that it’s worth it, but that it’s not dishonest to present data in an attractive, engaging packet. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3731742/
- That being said, I absolutely never had any doubts about the interpretation of the data that I published because I’d usually repeated each assay multiple times with similar results and had statistics to back it up. And that’s huge. As data visualization becomes easier, it’s dangerously easy for people to whip out pretty pictures. Everything I want to tell you today is predicated on the idea that you know your data really well, and you can therefore make informed decisions about what is the best way to present that nuanced story. Very rarely does real world data shape up as pretty as the examples we see, so we have to learn how to deal with ambiguity- and not just with ambivalence. So please, before you sketch out your dashboard, before your frame you data story, make sure you know with some confidence what stories there are to tell about your data.
- Why? Because it’s too easy to use your powers of visualization for evil. Stay honest about presenting the real interpretation of your data, even if it’s messy, even if it makes you want to get more data, even if you don’t fully understand what it means. You are supposed to be helping the reader understand the data, not hiding it’s true meaning. To get there, you have to know your data really well.
- If you think about good communication as a form of knowledge transfer, and I do, thinking about learning can help shape the way you think about sharing. Depending on what resources you look at, there are 4 or 5 basic principles, all of which will sound dead obvious when I say them to you, but which can and should be used to help shape the way you communicate with other adults. First, adults needs to be engaged in their own learning. It’s one of the reasons why just staying awake in a lecture is not the same as learning or remembering. Adults are more interested in learning when there is a direct impact on their life or work. What’s in it for me? It’s pretty easy to pay attention to talks when I am listening for something I can use. Sometimes this means you can’t control what their personal take-away is. Experience shapes how adults learn. This is huge. Understanding what experience your audience has can be really helpful for scaffolding new knowledge, that is connecting new ideas to old ideas in a functional way. It also might give you some clues about what hurdles you have to clear to help make your point. Finally, adults prefer problem-centered learning over content-oriented learning. I don’t just want to know some facts, I want to understand a system, I want to solve a problem, I want to get to a solution. There is a sort of satisfaction that you get from solving a problem, and a by sharing that solution with someone, they get a lesser experience of the same satisfaction. So I’m going to try to mention these principles as I go through the rest of the talk, but I wanted to make sure that I said them, outloud, to other people who think about communicating complicated ideas, because I find reminding myself of these things is so incredibly helpful.
- So let’s see how to use some of those principles in one of my favorite, simple data stories. This is John Snow’s cholera map from the 1854 Broad st outbreak. So keep in mind that this is before the germ theory of disease has fully been accepted- people think you get Cholera from the miasma- or bad air, and not from contagious germs like bacteria of viruses that can live on surfaces or in water. John Snow is a doctor who cares about the health of the public, and he doesn’t buy into this idea of bad air. So he goes to this neighborhood and starts knocking on doors, looking to get an account of cholera patients. Each one of these little black marks is a cholera patient.
- And Snow realizes that they are centering around this one well. So he takes the handle off the well, and within days the outbreak stops. Looks like cholera was coming through in the water right?
- Title & Copy We can frame the story like a problem- lots of people were getting sick, and no one knows why. I took a guess that many of you are familiar with the germ theory of disease, or the spread of germs. I’m also guessing that some of you have used a visualization to really understand a problem. So your experience shapes the way that you experience that story. I know you’ll be more interested in a story that feels relevant to your work and life, so I pick a story where the guy who make visualizations is the winner! And because I’ve gone out of my way to make this relevant and salient to you, you might actually remember it. It’s a bit more likely you’ll actually remember the story of the cholera in the well, but hopefully that reinforced how easy this adult learning stuff can be.
- I think the scientific method can be an easy model for problem-centered learning through stories, especially when you have data to show. As John Snow’s story proves, sometimes the story just needs one good visual to bench the arc of the story. You don’t need more data to make it data storytelling. This is a very simple story arc that is problem centered. It resonates well with people, and also can provide enough guidance that you might remember to include the important points of the story. Simple method for organizing problem-centered stories.
- You put this slide here to stop for questions
- All three of these principles come out of research on adult learning
- The point of this slide is that you need to combine words and graphics. Research has shown that you can significantly improve retention of information over pictures alone by adding explanatory words or text- the words can be either audio or written, but the point is the words should help the viewer make sense of what they are seeing, and ideally reiterate the main points. Here is a great example from Ramon Martinez at Health Analysis. I tweaked the layout a bit to accommodate the slide, but Ramon always does brilliant dashboards. One of the things I love about his work is his careful labeling- the visuals are cool, but it’s the careful labeling that makes the whole thing work.
- On top of that, he makes great tool tips. Again, as adults, people might not be laser focused when they encounter your viz, and you can use words to overcome confusion and just help people get to the conclusions faster.
- I love tooltips, the idea that if you can reveal more detailed information by interacting. Here is another example, that I threw together to show a tooltips. We are looking at Sales and Profits for by category and country. There is a lot of data in here, and you might not know what you are supposed to get out of it. So I like to make the tooltips read like a sentence- that’s what we usually mean by human readable tool tips. The point is to reinforce what is being shown visually to help focus on the conclusions you mean to show.
- The second is the principle of contiguity- which means sharing a border. In the context of creating visuals though, it really means putting the label or text on the thing itself. I’ve pulled this view directly from the book where I first saw it because it’s such a perfect example of contiguity- these labels are right on top of the bars! As you can see, people retain more, or more likely just understand better the first time around, when the label is right with the visual. Full citation: Clark and Mayer, E-Learning and the science of Instruction. John Wiley & Sons, Inc. 2011 Adapted from Mayer, 2001. Multimedia Learning. New York: Cambridge University Press. Mayer 2005. Principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity, and temporal contiguity. In Mayer (Ed) The Cambridge handbook of multimedia learning. New York: Cambridge Press.
- So let me give you an examples. I ran across this image on Robert’s blog, Eager Eyes. And I love the layout! This figure shows us people’s ability to correctly estimate the size of the area represented by each chart. And rather than try to come up with a description of each that would fit under the error bar, we just see the chart that participants did. It’s right next to the data of interest, so the viewer isn’t doing any additional processing.
- The third principle is coherence, that is keep your story focused on the data of interest. This view shows that when excessive text is added, knowledge retention declines. This is called extraneous processing, when the audience has to filter through additional layers of confusion to get to the real issue. Finding a balance between these three principles can be challenging, but if you understand your data well, and you have a clear vision for what you want the audience to absorb you can help them focus there attention there. Full citation: Adapted from Mayer, 2001. Multimedia Learning. New York: Cambridge University Press. Mayer 2005. Principles for reducing extraneous processing in multimedia learning: Coherence, signaling, redundancy, spatial contiguity, and temporal contiguity. In Mayer (Ed) The Cambridge handbook of multimedia learning. New York: Cambridge Press.
- So here an example of a dashboard I whipped up. I was careful to follow that classic principles of layout- my time series are line charts, my categorical data is in a bar chart, my location data is on a map. The category colors are consistent throughout. But if I was going to ask you tomorrow what the takeaway of this chart was, what you would say? IS there one? Is it about sales? It is about category performance? Profit? Discounts? Just because we HAVE all this data, and it’s INTERESTing, doesn’t mean we need to show it all at once. Now let’s say I tell you this is about profits in furniture- I’ll even say the stagnant profits in furniture. Tomorrow, you might not really be able to remember that either, because there are some equally plausible stories to tell, on this dashboard, and all of them are competing for space in your memory. Extraneous processing. How to fix that? Tell one story at a time. Make sure what you are showing is directly related to the story you want to tell.
- Let me pause here, are there questions on any of that? Now let’s shift into the second half of the talk, which is more about packaging, starting with pre-attentive attributes.
- So to start, let’s so a little exercise. help me find the 9s. How many nines are on the screen?
- NOW count the nines. How many nines do you see? Raise your hand when you know. (There are 11 nines) It’s amazing how much easier it is to find and see the nines as soon as they’re picked out in red.
- Let’s get back to why color matters. Here is a less abstract example, possibly the sort of thing that floats around your office from time to time. Can you tell me Which product subcategory is the most unprofitable?
- Again: which product subcategory is the most unprofitable? Just by adding color, we can increase the understanding here. Within milliseconds, we know that the important numbers are here, and we and limited the numbers we are making comparisons between. But we are still looking at the numbers.
- Now … which product subcategory is the most unprofitable? You can instantaneously see that Tables for the Home Office customer segment are the least profitable. By combining both color and length, we are able to focus on the important values, and make a quick comparison. We are loosing the absolute value, but if the point of the chart is to point out areas of concern then this totally gets you there fast, without having to spend a long time trying to make sense of it.
- So, as we saw in that example, there are certain visual cues that jump out at us and help us to rapid visual analysis – we call these cues “pre-attentive visual attributes” Pre-attentive attributes are information we can process visually almost immediately, before sending the information to the attention processing parts of our brain. Length is one of these attributes. It’s easy, in this example, to see what’s different and where there are patterns we might want to pay attention to.
- Width is another example.
- Color is another example. As I’m sure you experienced in these examples, pre-attentive processing is unconsciously done. We almost can’t help but assign meaning to our environment.
- As I’m sure you’ve guessed, using at least one pre-attentive visual attribute is the best way to present data, because we can see these patters – see what matters - and do some analysis without thinking too hard. Preattentive means that perception preceeds focused attention. The visual system can prioritize this information for the brain. https://public.tableau.com/views/PreattentiveAttributes/PreattentiveAttributesinTableau?:embed=y&:display_count=yes
- Again, use these tools for good and not evil. You can over exploit these traits to make meaning where there isn’t any.
- Now we are going to shift a bit towards talking about perception. Perception is how we make sense of the information we are getting- visually or otherwise. There is a whole lot of science happening behind perception- how it works, why it happens, what it influences. What does that really mean in terms of understanding data visualizations? Take the example of the penny. Without digging into your pockets to actually look at one, think about what a penny looks like. Do you have that image in mind? (flip slide)
- Ok, since you were just thinking about a penny, which one is the real penny? This is part of a classic psychology experiment that was used to describe selective memory. For our purposes, the reason we struggle to identify the penny is that most of us think about the penny as the little copper colored one. Maybe we think about Lincoln. But basically, unless you are a coin collector, you’ve never bothered to or needed to remember what else is on the face of a penny This experiment was about selective memory, but I think it can drive home an important point about which attributes we use to call out data- namely, use the ones that people can identify and distinguish. But I was just talking about perception. I said Perception is making sense of the information we are getting. In this case, you don’t need a detailed image of the penny in our head to know which one will be 1 cent. The point I’m trying to prepare you for here is that what you perceive isn’t always the whole story- we know this because no one but the coin collectors guessed the real penny. And perhaps more importantly when you design visuals, keep in mind that not everything you put out there will be perceived as you intended.
- Didn’t I just tell you about these remarkable pre-attentive attributes that the brain is happily, reflexively distinguishing? Yes, but… Perception matters. Perception may be how we prioritize these traits, or whether we see them at all.
- Size matters, especially in context. Which of the two orange circles is larger? Yeah? What if I tell you they are the same size? In the Ebbinghaus illusion, the context of the larger of smaller circles greatly impacts your ability to make comparisons across the image.
- Where does this matter? What if I try to be super clever and add a third variable to size in my scatter plot. Take a look at these three points- which is biggest? Ok, they are all the same size, but how confident were you in that- Imagine how much harder this task would be if the sizes were a continuous range, and not discrete numbers? It’s really hard to make these kinds of comparisons when the context is changing.
- Here is an easy one- which is darker, A or B? Color is really hard to distinguish out of context. Shadows in particular cause us to adapt our perception. Even though I KNOW A and B are the same color, I just can’t see it in this image.
- It takes this modification for me to actually see it, and even then there is a part of my brain that struggles.
- It’s really hard to compare gradations of colors, especially when they aren’t next to each other.
- Stepped color, diverging color pallets or other encodings that make it more obvious what each color is*, regardless of context, can prevent this kind of error. This might feel like you are reducing the total amount of data in your view, but let’s be honest, the human eye doesn’t have the same resolution as your computer monitor. And often stepped color can help you make the generalized point you want to make- more people smoke in the South east, less people smoke in Minnesota. *labels, shapes, other encoding.
- Here is one more bonus illusion, because I have made so much noise about labeling so far. I just want to remind you that there is a right way, and a wrong way. So the Poggendorf illusion show one long continuous line, and one short line. Which line connects to the back like, the blue or the red?
- The basic issue is apparently that the acute angles are hard to perceive accurately, so we mistake the which line is continuing.
- Let’s look at a worse case scenario example. So earlier I told you use contiguity to put the label right on the data of interest. So here I am showing some chemical spectra that show a chemical reaction. At the beginning, of the reaction, the spectra is the top one, and by the end, the spectra have changed to this bottom one, so I add this label, that the greatest shifts are between 500 and 580 nm. BUT there is also a pretty big change in how that signal looks here, but I let my clever annotation overlap it, and now it’s not clear which line is which.
- The solution I recommend is using color, and also being careful not to let anything block the lines themselves, so there aren’t questions about acute angles, or disrupted lines.
- The one last pitfall I want to show you is about change blindness. All those brilliant visual pathways that parse data on its way to the brain? They have no storage capacity, which means that it’s hard to make comparisons of visuals if there is any break in the visual stream, or if there is any distraction in the visual. http://nivea.psycho.univ-paris5.fr/CBMovies/DesertFortFlickerMovie.gif More examples here http://nivea.psycho.univ-paris5.fr/#CB
- The one last pitfall I want to show you is about change blindness. All those brilliant visual pathways that parse data on its way to the brain? They have no storage capacity, which means that it’s hard to make comparisons of visuals if there is any break in the visual stream, or if there is any distraction in the visual. I’m making this sound super complicated- instead, let’s watch this video http://nivea.psycho.univ-paris5.fr/CBMovies/DesertFortFlickerMovie.gif More examples here http://nivea.psycho.univ-paris5.fr/#CB
- There are lots of cases where we assume that we are making a seamless comparison between two views, but with that little flash in between, we totally aren’t. What makes change blindness especially hard is that usually once you see the change, it’s easy to notice. So if you know there is going to be slight dip in the shipping values if you filter out last month, it looks super obvious to you, but the user who is looking at your view for the first time doesn’t see anything. And they might not be motivated to sit still for 5 min trying to make that comparison. What should you do about that? Let there be obvious points like reference lines for comparisons, keep all your data and legends that need to be compared within one view, and try to avoid that flash when the filter reloads.
- You asked about the visual analytics course I took where I first was introduced to these ideas? It’s this one here: http://www.tableau.com/learn/classroom/visual-analytics