Seung Lee collected sleep data for his son’s first year. Then he knitted a blanket to visualize the data. The blanket is impressive. Collecting a baby’s sleep data for a year? More so.

Jason Forrest delves into the history of a single Isotype and a bit of the general background on the picture language:

Isotype is a highly refined picture language designed for educating people with as few words as possible. Created by Otto Neurath in 1925, the International System of Typographic Picture Education (ISOTyPE) evolved over the next two decades with the collaboration of Marie Neurath and Gerd Arntz. The trio developed their distinct approach to data visualization iteratively, and very collaboratively. Otto provided the overall direction, Marie “transformed” the data to present the story, and Gerd designed the pictogram units and highly-refined designs.

Based on data from Gridded Population of the World, geographer Garrett Dash Nelson calculated the square kilometers in major cities with the highest population density.

On CityLab:

In the interactive visualization, I’ve taken GPW data for a curated selection of American cities. Some have old, historic cores, and others are dominated by more recent development; some have constricting physical geographies and others lie on relatively flat, open plains; some were built for horse transportation and others for the automobile era.

Visualization is still a relatively young field, so people learn about and how to visualize data in a lot of different ways. For instance, there weren’t any visualization-specific courses when I was in school, so I picked up a lot ad hoc. Alli Torban, looking at responses to the 2018 Data Visualization Survey, shows how others learned. The top three: examples, collaboration with those more skilled, and books.

Airport runways orient certain directions that correlate with wind direction in the area. It helps planes land and take off more easily. So, when you map runways around the world, you also get wind patterns, which is what Figures did:

Winds circulate around the globe, forming patterns of gigantic proportions. These patterns become part of human culture and are reflected in our architecture. They are hidden designs, mapping the complexion of the earth, which we can uncover. By orienting on the direction of general winds, airports recreate wind patterns, forming a representation of a global wind map with steel and stone, thus making the invisible visible.

Wondering whether if a player’s shot improves over the course of his career, Peter Beshai shows shot performance for all players from the 2018-19 season:

To understand whether or not a player actually gets better over time, we need some kind of baseline to compare their current performance against. On Shotline, the baseline is set after a player completes their first season in the NBA and has shot at least 200 times. This may sometimes feel a bit arbitrary, and I guess it is, but it feels reasonable to compare a player’s first season’s performance to their current to understand whether they have improved or not. The graphs are set up to allow you to compare their current performance against any other point in time too if the baseline is not sufficiently interesting to you.

They provide an anchor in your charts, and you compare everything else against the anchor. Where you set the anchor changes your chart completely.

Rewind to 2006 when Hans Rosling’s talk using moving bubbles was at peak attention. Researchers studied whether animation in visualization was a good thing. Danyel Fisher revisits their research a decade later.

While they found that readers didn’t get much more accuracy from the movement versus other method, there was a big but:

But we also found that users really liked the animation view: Study participants described it as “fun”, “exciting”, and even “emotionally touching.” At the same time, though, some participants found it confusing: “the dots flew everywhere.”

This is a dilemma. Do we make users happy, or do we help them be effective? After the novelty effect wears off, will we all wake up with an animation hangover and just want our graphs to stay still so we can read them?

Working from the Quick, Draw! dataset, Moniker dares people to not draw a penis:

In 2018 Google open-sourced the Quickdraw data set. “The world’s largest doodling data set”. The set consists of 345 categories and over 15 million drawings. For obvious reasons the data set was missing a few specific categories that people enjoy drawing. This made us at Moniker think about the moral reality big tech companies are imposing on our global community and that most people willingly accept this. Therefore we decided to publish an appendix to the Google Quickdraw data set.

Draw what you want, and the application compares your sketch against a model, erasing any offenders.

Nicholas Rougeux, who has a knack and the patience to recreate vintage works in a modern context, reproduced Elizabeth Twining’s Illustrations of the Natural Orders of Plants:

If someone told me when I was young that I would spend three months of my time tracing nineteenth century botanical illustrations and enjoy it, I would have scoffed, but that’s what I did to reproduce Elizabeth Twining’s Illustrations of the Natural Orders of Plants and I loved every minute.

The best part is that you can select flowers in the text or on the illustrations to focus on a specific parts, which makes descriptions easier to interpret.

Read more on Rougeux’s process here.

A few years ago, The New York Times asked readers to guess a trend line before showing the actual data. It forced readers to test their own beliefs against reality. TheyDrawIt from the MU Collective is a tool that lets you make similar prediction charts:

These line graphs encourage readers to reflect on their own beliefs by predicting the data before seeing it. Only after they draw a prediction does the real data appear. The graph can then show the reader traces of other peoples’ predictions. This progression makes it easy for readers to visualize the difference between their predicted beliefs, their peer’s beliefs, and the actual data.

Give it a go.

Some people love the Tidyverse in R. Others are less fond of it. For me, the more tools the better.

When it comes to reading lists, we usually look for what’s popular, because if a lot of people read something, then there must be something good about it. Russell Goldenberg and Amber Thomas for The Pudding took it the other direction. Using checkout data from the Seattle Public Library, they looked for books that haven’t been checked out in decades.

Also: How cool is it that there’s an API to access library checkout data?

National Geographic went all out on their atlas of moons. Space. Orbits. Rotating and interactive objects in the sky. Ooo. You’ll want to bookmark this one for later, so you can spend time with it.

As you might expect, NASA collects a lot of data, and much of it is seasonal. Eleanor Lutz animated a few maps to show the detail:

To show a few examples, the NASA Earth Observations website includes data on seasonal fire incidence (1), vegetation (2), solar insolation, or the amount of sunlight (3), cloud fraction (4), ice sheet coverage (5), and processed satellite images (6). My own map (7) combines the ice sheet data and the Blue Marble satellite images (6). The NEO database also has many more interesting datasets not shown here, like rainfall, chlorophyll concentration, or Carbon Monoxide.

Check out the final result. And, if you want to make your own, Lutz published her code on GitHub.

The citizenship question for the upcoming Census is still stuck in limbo. One of the arguments against the question is that it could lead to a significant undercount in population, which can lead to less funding. For Reuters, Ally J. Levine and Ashlyn Still show how this might happen with a highlight on federal programs that rely on population estimates.

Roger Peng provides a lesson on the roots of R and how it got to where it is now:

Chambers was referring to the difficulty in naming and characterizing the S system. Is it a programming language? An environment? A statistical package? Eventually, it seems they settled on “quantitative programming environment”, or in other words, “it’s all the things.” Ironically, for a statistical environment, the first two versions did not contain much in the way of specific statistical capabilities. In addition to a more full-featured statistical modeling system, versions 3 and 4 of the language added the class/methods system for programming (outlined in Chambers’ Programming with Data).

I’m starting feel my age, as some of the “history” feels more like recent experience.

You can also watch Peng’s keynote in the video version.

It’s about purpose. It’s about who your work is for. It’s about what you’re trying to show.