Information Design & Data Visualization
No more LittleDataGraphics
Small data sets should be shown directly...LittleDataGraphics (pie charts, bar charts) translate and encode data into areas and colors. Viewers must then mentally translate codes back into numbers. These codes are unique to the local sets of data graphics, and do not repay learning. Instead, just directly show numbers as numbers. No more LittleDataGraphics. Data visualizations are at their best when there is so much data that the only way to see it...is to see it.
The Visual Information Seeking Mantra
There are many visual design guidelines but the basic principle might be summarized as the Visual Information Seeking Mantra:
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demand
Overview first, zoom and filter, then details-on-demandEach line represents one project in which I found myself rediscovering this principle and therefore wrote it down it as a reminder. It proved to be only a starting point in trying to characterize the multiple information visualization innovations occurring at university, government, and industry research labs.
Envisioning Information
A Book by Edward TufteBeautiful Evidence
A Book by Edward TufteThe Visual Display of Quantitative Information
A Book by Edward TufteThe Elements of Graphing Data
A Book by William S. ClevelandVision Science
A Book by Stephen E. PalmerVisual Explanations
A Book by Edward TufteVisualizing Algorithms
An Article by Mike BostockInformation Visualization: Perception for Design
A Book by Colin WareThe Eyes Have It
A Research Paper by Ben ShneidermanVisualizing Data
A Book by William S. ClevelandExploratory Data Analysis
A Book by John TukeyPlus Equals #4
An Article by Rob WeychertOne of the seeds for Plus Equals was planted a few years ago with Incomplete Open Cubes Revisited, my extension of a Sol LeWitt work. I learned a lot about isometric projection from that project, but my affection for the concept didn’t begin there. Whether I’m looking at a Chris Ware illustration or an exploded-view technical drawing of a complex machine, an isometric rendering always stirs something in me.
In search of visual texture
An Article by Rachel PruddenI’m now more inclined to attribute Looseleaf’s power to its visual texture than to some cognitive media-style abstraction. And the visual texture owes more to the beauty (yes, beauty!) of the original pdfs from the Vasulka Archive. Perhaps the demo is best understood not as a prototype generic tool, but as a specific curated experience in its own right, with form and content claiming equal importance in its overall success.
Even so, I think there are some general lessons that can be drawn from this demo:
- Content is not inert
- Visual texture lets content breathe
- Visual texture lets the eye wander without losing itself
Chartwell
A FontThis set of tools for easily creating graphs is conveniently disguised as a set of fonts. OpenType features are used to interpret and visualize the data. The data remains as editable text, allowing for painless updates.
Windy
A WebsiteBetter colormaps?
An Article by Mark LibermanA modest sample of alternative ways of coloring the same type of 2-D density plots of rates of F0 change and amplitude change.
ImageQuilts
A Tool by Edward Tuftepress.stripe.com
A WebsiteStripe partners with millions of the world’s most innovative businesses. These businesses are the result of many different inputs. Perhaps the most important ingredient is “ideas.”
Stripe Press highlights ideas that we think can be broadly useful. Some books contain entirely new material, some are collections of existing work reimagined, and others are republications of previous works that have remained relevant over time or have renewed relevance today.
Between the Words
An Artwork by Nicholas RougeuxMoby Dick.
Between the Words is an exploration of visual rhythm of punctuation in well-known literary works. All letters, numbers, spaces, and line breaks were removed from entire texts of classic stories...leaving only the punctuation in one continuous line of symbols in the order they appear in texts. The remaining punctuation was arranged in a spiral starting at the top center with markings for each chapter and classic illustrations at the center.
SeriesHeat
A Website by Jim VallandinghamSearch for a TV Series to see a heatmap of average IMDb ratings for each episode.
Time-based analytics
An Article by Ryan SingerAnalytics apps don't tell you much about usage behavior. You might be able to see how many users performed an event, or how many times they did it. But none of the analytics packages out there are good at showing you how often people do things. Are they using to-dos once a week? Every day? Only signing into the app once a month but happily paying for years?
Time matters. You can't understand usage without time.
Cameras and lenses
An Article by Bartosz CiechanowskiPictures have always been a meaningful part of the human experience. From the first cave drawings, to sketches and paintings, to modern photography, we’ve mastered the art of recording what we see.
Cameras and the lenses inside them may seem a little mystifying. In this blog post I’d like to explain not only how they work, but also how adjusting a few tunable parameters can produce fairly different results.
The Subtleties of Color
A Series by Robert SimmonThe use of color to display data is a solved problem, right? Just pick a palette from a drop-down menu (probably either a grayscale ramp or a rainbow), set start and end points, press “apply,” and you’re done. Although we all know it’s not that simple, that’s often how colors are chosen in the real world. As a result, many visualizations fail to represent the underlying data as well as they could.
tixy.land
A Websitesin(t * x) * cos(t * y)
Creative code golfing.
Zipdecode
A Website by Ben FryA Library Demand List
A Website by Robin SloanThis visualization takes the current New York Times Best Sellers list for combined print and e-book fiction and scales each title according to the demand for its e-book edition at a collection of U.S. public libraries, selected for their size and geographic diversity.
MIT Student Hub
An Article by Alex HogrefeThe project is located on the MIT Campus and will be a “Student Hub” containing restaurants, large event spaces, and smaller study spaces.
V-RESAS
An ApplicationAll in & with the flow
An Article by Buster BensonInfoCrystal
A Research PaperThis paper introduces a novel representation, called the InfoCrystal, that can be used as a visualization tool as well as a visual query language to help users search for information. The InfoCrystal visualizes all the possible relationships among N concepts.
Readings in Information Visualization: Using Vision to Think
A Book by Ben Shneiderman
Deadlines are bullshit
In software development deadlines are a necessary evil. It is important to understand when they are necessary, and it is important to understand why they are evil.
- External vs. internal deadlines
- Why are internal deadlines evil?
- Engineers who love their work
External vs. internal deadlines
When are deadlines necessary?
- Contractual obligations
- Technical liabilities (e.g., dependency EOL)
- Compliance, government, investors, and other external stakeholders
What do all of these deadlines have in common? They are all important. They are all deadlines that cannot be missed. They are all external.
When are deadlines evil?
- Your manager says you have a deadline
- Your software development methodology says you have deadlines
What do all of these deadlines have in common? None of them are important. They are arbitrary. They are all internal. They are all bullshit.
Why are internal deadlines evil?
- Estimation: When estimating engineering work a substantial time investment is required by an engineer in order to get an accurate estimate.
- Misaligned Incentives: There is an incentive to lie and give estimates much longer than the feature is truly expected to take.
- Low Morale: Deadlines are likely to be missed often. Repeated failure has a cost to the morale of the team.
- Micromanagement: Deadlines are wielded by middle managers as a whip to harass and annoy engineers working on features.
- High Stress: When engineers feel the pressure of other stakeholders holding deadlines over their heads it creates an environment of high stress.
- High Turnover: On teams with high turnover rates the best engineers have an easy time finding new work and leave quickly, the worst engineers have a difficult time finding work and remain. This selects for a lower quality team over time.
Engineers who love their work
The resolution is simple. Never have internal deadlines. Operate on a prioritized and ordered list of features. Estimate only when necessary to prioritize and do so in a t-shirt sizing way. Trust your engineers and they will begin to love their work. Engineers who love their work are happy and productive.