People’s interpretation of a chart can change if you use differents words to describe it, even if the data stays the same.

Data Visualization in Society, an open access book, is a collection of works that looks closer at the role data visualization plays beyond the technical aspects of the discipline:

The expansion of data visualization in society therefore requires a new kind of literacy if it is to enable citizens to act in informed and critical ways. It also requires the assessment of data visualization’s role in democracy, and the reassessment of democratic theory in light of developments in data visualization. This means asking a range of questions about the relationship between data visualization and democracy. It also means considering the factors in visualization consumption and production processes that affect engagement, which might include factors which extend beyond textual and technical matters, such as class, gender, race, age, location, political outlook, and education of audience members. Some of the contributions in this collection address these issues.

My reading list just got longer.

The Datavis Book Club run by Datawrapper is reading and discussing the first four chapters, in case you want get in on the fun.

This is interesting:

What does 425,000 Covid deaths sound like? I was inspired by this article by @LazaroGamio and @LaurenLeatherby for the NY Times, where they visualized how long to reach another 25k deaths. The piece had a rhythm that made me think of music, so I tried turning the data into sound pic.twitter.com/2YhmgqDZGQ

— carni_dc (@CarniDC) February 1, 2021

NYT’s The Upshot published their precinct-level map of 2020 election results. Zoom in to your geographic area and bask in or scratch your head over the detailed variation.

This seems be a recurring view now, with their “extremely detailed map” making an appearance after the 2016 and 2018 election. They also had their “most detailed maps” in 2014.

However, this year, The Upshot made their precinct-level data available on GitHub, so you can look closer if you like.

With this straightforward unit chart, wcd.fyi shows which generation each Senate member belonged to, from 1947 through 2021. Each rectangle represents a senator, and each column represents a cohort.

As time moves on, the generations inevitably shift. In 2021, we have the first Millennial senator in Jon Ossoff from Georgia.

I’m also looking forward to Jer Thorp’s Living in Data, which comes out later this year but is available for pre-order now:

In this provocative book, Thorp brings his work as a data artist to bear on an exploration of our current and future relationship with data, transcending facts and figures to find new, more visceral ways to engage with data. Threading a data story through hippo attacks, glaciers, and school gymnasiums; around colossal rice piles and over active mine fields, Living in Data keeps humanity front and center. Thorp reminds us that the future of data is still wide open; that there are stories to be told about how data can be used, and by whom. Accompanied by informative and poetic illustrations, Living in Data not only redefines what data is, but re-imagines how it might be truly public, who gets to speak its language, and how, using its power, new institutions and spaces might be created to serve individuals and communities. Timely and inspiring, this book gives us a path forward: one where it’s up to all of us to imagine a more just and participatory data democracy.

When I started FlowingData, Statistics and data almost always seemed highly technical and accessible to only a few. A few years later, understanding data was like a novelty that more people wanted to play with but still didn’t quite know the implications of what they were looking at. These days, spurred on by last year especially, interpreting data is an essential skill.

Over the next few years, Thorp’s perspective on how we live with these new streams, individually and as a society, will only grow more important.

While we’re on the topic of Statistics books for the general public, David Spieglhalter’s The Art of Statistics: How to Learn from Data is also on my reading list.

In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems — it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and — perhaps even more importantly — we learn how to responsibly interpret the answers we receive.

I was waiting for the book to come to North America, and apparently it did in 2019. I’m so behind in my reading, but I declare 2021 as the year I take my attention span back.

Tim Harford has a new book coming out tomorrow called The Data Detective: Ten Easy Rules to Make Sense of Statistics.

Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics—we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism of numbers and are often “the only way of grasping much of what is going on around us.” If we can toss aside our fears and learn to approach them clearly—understanding how our own preconceptions lead us astray—statistics can point to ways we can live better and work smarter.

Added to the list.

If you’re outside North America, look for How To Make The World Add Up. They’re the same book.

For Reuters, Feilding Cage, Chris Canipe and Prasanta Dutta made an interactive that lets you adjust dose rate and state in a simulation to get an estimate for when we might reach herd immunity.

As with any simulation, there are assumptions and simplifications. In this case, the dose rate stays uniform and total population is used, even though there are no vaccines available to children yet. But it’s something.

My main takeaway is that we’re gonna have to be patient (still).

Just speaking to the chart, I like the sketch-ish dashed lines and gradient to show herd immunity ranges. They communicate that things are still uncertain.

Vivian Wu made a snowflake generator. Adjust parameters such as growth, kaleidoscoping, and density, and you dear friend, can make yourself a unique snowflake of your very own.

I think I’ll just zone out and let the animation play out for a few minutes every day. Breathe in. Breathe out.

Here’s the good stuff for January.

The United States passed 425,000 coronavirus deaths this week. For The New York Times, Lazaro Gamio and Lauren Leatherby used dot density over time to show how we got to this point.

Each dark pixel represents a death, and each tick mark represents a day. So the strip starts light with sparsely placed dots, and then it gets darker and darker. Get to present day, and there’s hardly any white space.

Twitter released a small JavaScript library to make density plots — for when you have a lot of overlapping points and could use some granular binning. Feed a method an array of thousands of x-y coordinates, and the library takes care of the rest.

With vaccines, we might be tempted to jump back into “normal” life before it’s really safe. The New York Times reports on why waiting until March instead of February might be the way to. This is based on estimates from Columbia University researchers, and you can read the preprint here (pdf) by Jeffrey Shaman et al.

We’ve come this far already…

Maybe you remember the SimCity-like views through satellite imagery from a few of years ago. Robert Simmon from Planet Labs returns to the topic discussing practical use cases and advantages over a top-down view:

Satellite imagery surrounds us — from Google Maps and daily weather forecasts to the graphics illustrating news stories — but almost all of it is from a map-like, top-down perspective. This view allows satellite data to be analyzed over time and compared with other sources of data. Unfortunately, it’s also a distorted perspective. Lacking many of the cues we use to interpret the world around us, top-down satellite imagery (often called nadir imagery in remote sensing jargon) appears unnaturally flat. It’s a view that is disconnected from our everyday experience.

Thomas Mock explains how to extract and parse data tables in image files via ImageMagick and R:

There are many times where someone shares data as an image, whether intentionally due to software constraints (ie Twitter) or as a result of not understanding the implications (image inside a PDF or in a Word Doc). xkcd.com jokingly refers to this as .norm or as the Normal File Format. While it’s far from ideal or a real file format, it’s all too common to see data as images in the “wild”. I’ll be using some examples from Twitter images and extracting the raw data from these. There are multiple levels of difficulty, namely that screenshots on Twitter are not uniform, often of relatively low quality (ie DPI), and contain additional “decoration” like colors or grid-lines. We’ll do our best to make it work!

You can never have too many tools to grab data from various, inconvenient file formats.

[arve url=”https://www.youtube.com/watch?v=gFFj22kjlZk&feature=emb_title” loop=”no” muted=”no” /]

Jon Schwabish has a new book coming out: Better Data Visualizations. To kick things off, he’s running a video series on the many different chart types. There will be 50 videos released daily, each with an invited practitioner to briefly talk about what the chart is and how it’s used. They’re already 10 videos into it.

Should be informative.

I make most of my static charts in R, but I always bring them into Illustrator for polish, layout, and readability. Here’s my process.

The New York Times labeled all of the people sitting behind Joe Biden during the inauguration. It’s a straightforward but slick interactive that lets you pan and zoom the photograph. Click on a name for more details or use the list of names in a sidebar.