Today:
~20-30 min of lecture/programming
~1.5-2 hr of finishing-up-viz work
~0.5-1 hr of viz party
the end of the class will be the opportunity for you all to show off your cool viz! don't worry if its not done yet - one of the skills you can learn here is how to articulate what you'd like to do in the future if you had time!
Today
Communication with an Audience (Viz for self, peers, others)
3D Movies
Where to go from here
Final project - putting on your finishing touches
Three Classes of Visualizations
Viz for Self
Viz for Experts
Viz for the Public
Viz for Self
There are no rules!
Labels don't matter. Colors don't have to look nice. Interactivity doesn't have to be fast. Here we are just trying to get something on screen that represents the data. Often this is challenge enough.
As the designer, you know what choices you have made, so "elegant design" isn't a huge deal - although don't go too crazy or you could make things more confusing for yourself.
Viz for Self
Here are some examples from my work
There are plots of data from a large scale simulation of the universe. Here, I'm curious about how the average number of elements heavier than helium in our simulation changes over time.
Viz for Self
name of: L25n512TNG_shenplot_STARS_ylimn0p5_bins_largeEu_1e2_nx050_ny050_sn099.png
You can even tell from the name of the file (which runs off the page here) that I'm playing with a bunch of things. For example I'm plotting here stars, but there is no way you'd really get that from the actual plot itself.
These are plots that show an example of how one might "explore" the data - look for relationships.
Viz for Self
Later this became (viz for peers)
Paper if you're really curious
later, that plot became this plot right here which can be thought of as viz-for peers
While its probably not something that you all understand (unless there are some astronomers in our midst) - you can see I'm more careful here about fonts, presentatin, color schemes and actually labeling things
Viz for Self
Let's talk about exploration.
What are characteristics of data that would influence how you visualize it?
What information do you have that would be visually interesting?
What information do you not have that you need to understand the importance of the data?
Example: A banking database where each record is a bank transaction and the fields include date, deposit or withdrawal amount, customer id, and the interest rate of the account.
Viz for Self
Let's talk about exploration.
What are characteristics of data that would influence how you visualize it?
What information do you have that would be visually interesting?
What information do you not have that you need to understand the importance of the data?
Example: A spreadsheet of experimental crop growth measurements where each record is a measurement, and the fields include date, plant species, plant id number, number of leaves, plant height, number of internodes, and average leaf length.
internodes = the number of "generations" of a plant, aka how many branches come off the main stem.
Viz for Self
Let's talk about exploration.
What are characteristics of data that would influence how you visualize it?
What information do you have that would be visually interesting?
What information do you not have that you need to understand the importance of the data?
Example: A computational simulation of a galaxy where each record is a timestep in the evolution of the 3D grid, and the fields include time, X position, Y position, Z position, gas density, gas temperature, gas metallicity, and number of stars.
Viz for Self
What do you want to get out of visualization for yourself?
Do you want to find meaning?
Do you want to understand how to guide further visualizations?
Is the story you want to tell already known to you?
What shortcuts can you take?
shortcut ideas ... subsampling, delete unused fields, use a simpler tool, manually select outliers by hand instead of doing it procedurally
forget labels that others have to figure out
don't worry about color schemee
Viz for Experts
To design a visualization for experts, you need to analyze how they process information.
What do they know?
What conventions will they assume?
Are they able to fill in the blanks of information?
Viz for Experts
Here's a series of visualizations made by or for domain experts that AJ just had lying around on his laptop.
What are some things you notice they have in common?
Viz for Experts
Here's a series of visualizations made by or for domain experts that I just had lying around on my laptop.
What are some things you notice they have in common?
Viz for Experts
Here's a series of visualizations made by or for domain experts that I just had lying around on my laptop.
What are some things you notice they have in common?
Viz for Experts
Here's a series of visualizations made by or for domain experts that I just had lying around on my laptop.
What are some things you notice they have in common?
Viz for Experts
Here's a series of visualizations made by or for domain experts that I just had lying around on my laptop.
What are some things you notice they have in common?
Viz for Experts
Experts often want to interrogate the data themselves.
How can they do that?
Linked Dashboards
Side-by-side comparison plots
Text annotation with specific values listed
Color bar annotation
Viz for Experts
Experts are looking to isolate variables to make scientific conclusions.
How can we make visualizations more analytical?
Reduce the dimensionality of the image (slices)
Viewpoint from "outside the box"
Extremely high contrast color choices (or highlight different features)
Viz for the Public
This is what you're most accustomed to, because usually YOU are the public.
Who here is an expert on the historical accuracy of movies? Do you think this visualization was created for those experts? Or was it created for you? How can you tell?
Jargon
Jaret before and after from Alan Alda Center on Vimeo .
so here is an example of a scientist and, in a typical scientist fashion, using a lot of jargon
but then after taking some improv he's able to communicate more effectively to the public
Jargon
Definition: Special words or expressions that are used by a particular profession or group and are difficult for others to understand.
When working with experts on their data, they will often try to use words that are specific to their field. As a data communicator, it's your job to decipher that jargon and make it more approachable to the public.
Jargon
xkcd.com/1133
This XKCD webcomic makes fun of how much jargon scientists use. He tries to explain NASA's Saturn V rocket using only the 1000 most common words in the English language.
He actually wrote a whole book about science this way called "Thing Explainer."
Jargon
Simple Writer
And here's a neat tool he created to help you write your own!
Before:
"The Advanced Visualization Lab creates cinematic treatments of supercomputer data for immersive displays."
After:
"The very good picture making team creates movies of huge computer information for screens you can be inside of."
Storytelling
Let's just get it out there: humans don't respond to data. They respond to stories.
Which of these do you care about more?
Global average atmospheric carbon dioxide is 405 ppm.
Polar bears are dying because the ice they use to hunt is melting.
In visualization, we really want to tell a story, not just a list of facts.
Storytelling
Why is that?
Emotional response to storytelling is an evolutionary trait humans developed to form stronger social bonds and learn from each other's experience.
Storytelling
Freytag's Pyramid
A story needs setup, conflict, and resolution. One thing many novice storytellers forget is to have a resolution or ending. You need to wrap it up in a neat package!
aside - "denouement" is one of my favorite words
Storytelling
Characters and Conflict
People as subjects: Two political candidates are battling for office
People as researchers: Dr. Smith is trying to cure cancer
Objects as characters: The photons are trying to push their way to the sun's surface
each story is going to have different sorts of characters and different kinds of conflicts
here are a few examples - you can even make scientific data into a story! Don't you really want to root for the photons to make it out of the sun?
Know your Audience
VIDEO
so, we won't watch this whole thing, but it is worth checking out how the jargon/language changes when he's talking to different groups of folks
Know your Audience
What do you know about them?
Age
Nationality
Occupation
Affiliation with a Special Interest / Organization
Or is it literally EVERYBODY (web publishing)
You can hone your narrative for certain audiences. If it's the broadest audience, like for journalism shared online, you need to keep in mind that there will be children, grandparents, experts, politicians, people with money, etc all in the audience.
Visualization for the Public
Images can be more powerful than words, text, and numbers. Images can tell stories.
What can you say about this event given the picture? How is this different from an average day on the National Mall in DC?
What is the story of this photograph?
Visualization for the Public
Context is vital for the public.
Embed multiple datasets
Place something familiar to relate to the unfamiliar
Smooth transitions so you can see where you've come from and where you're going to
This is different from a visualization for Experts where you usually want to isolate the dataset.
With experts, you can asssume some background of understanding. You might start a presentation or paper with a background summary, but based on the field you don't have to explain as much. For example, if I'm giving a astronomy talk, I can assume folks know the difference between newtonian gravity and general relativistc treatments and roughly when each apply (GR for going fast, or near big things).
Visualization for the Public
Context is vital for the public.
The AVL created this sequence for a movie, which originally moved much slower on a large screen, and had a narrator explaining the sequence. Someone recaptured it and made it into a gif, sharing it on reddit with the caption "A simulation of the Earth's moon being formed."
What context might have been lost?
How well does the imagery speak for itself?
Visualization for the Public
Identifying the narrative
Do you first find data, or first find a story?
What is the difference between the data narrative and a narrative for the public?
In visualization for the public, typically we will start with a dataset, and then build a narrative around it, and seek out more data to build context.
Data narrative = what the data collector is researching
Public narrative = what makes an interesting story and provides context
This is different than viz for experts where you are typically talking about the dataset you have been working with specifically.
Visualization for the Public
Aesthetics Matter
Interactivity should not have any lag or latency
Relative values are often more important than specific values
Limit the amount of text/reading
Limit the amount of information being packed in
Visualization for the Public
A web-based toolkit for storytelling with data visualizations!
Reduce manual coding for interactive articles
Integration with D3.js
Also check out: https://jnaiman.github.io/
A JavaScript library for web-based data visualization
Declarative, not imperative (this is better for web coding)
Dynamic and interactive
Smooth transitions
Info/sci viz: Vaex
opensource components, check out docs page for large point datasets
Sci viz: Glue
linked data views for scientific data (astro emphasis)
includes starting 3D exploration tools
Sci viz: yt
for scientific data, heavy emphasis on astro
Comes with many example datasets you can grab on the sample data hub
The Things I Want You To Take Away
Coding is not magic, and neither is astrophysics
Numerical solutions are only so precise, but we can measure their inaccuracy
There are a variety of methods and codes to use for numerical science
There are a variety of systems, tools, and ecosystems available for visualizing data
How not to lie with data viz (and how to detect when someone is lying to you!)
How to share scientific results online with data viz tools
Viz Party
Prepare your "elevator speech" - in a few sentences:
What are you modeling (what system? What initial conditions?)
What are the interesting things you noticed about your system (it has lots of planets, it is very stable/unstable, it has planets of many sizes, ...)
Why you made the viz choices you did (textures, sizes, etc)
What you'd like to do if you have more time
Reminder: Teaching philosophy
Non-competative, group learning environment
There are no "lone wolfs" in science - Path to Newton
Inquiry based approach to learning - basically, we are actually going to do some computational astrophysics!
Today:
~20-30 min of lecture/programming
~1.5-2 hr of finishing-up-viz work
~0.5-1 hr of viz party
the end of the class will be the opportunity for you all to show off your cool viz! don't worry if its not done yet - one of the skills you can learn here is how to articulate what you'd like to do in the future if you had time!