This week, we talk annotation and how it can make your charts more readable and easier to understand.

The New York Times is in a quizzy mood lately. Must be all the hot weather. Sahil Chinoy shows how certain demographics tend towards Democrat or Republican, with a hook that that lets you put in your own information. A decision tree updates as you go.

Reminds of the Amanda Cox decision tree classic from 2008.

Add a book to the humorous-charts-documenting-the-everyday genre. Am I Overthinking This? by Michelle Rial charts the everyday. I like how Rial uses everyday objects to show everyday data informally. [Amazon link]

Dorota M. Skowron et al. made the first 3-D map of the galaxy. Sean Greene and Andrea Roberson reporting for the Los Angeles Times:

Astronomers have understood since the 1950s that the galaxy is curved, but until now, they’ve based that idea on models and indirect measurements.

Over a six-year period, the researchers took more than 100 images of the galaxy from their observatory in the Chilean Andes. In particular, they monitored 2,400 stars called classical Cepheids, a category of “young” supergiants less than 400 million years old.

For Carto, Matt Forrest explains why you shouldn’t use ZIP codes for spatial analysis:

The problem is that zip codes are not a good representation of real human behavior, and when used in data analysis, often mask real, underlying insights, and may ultimately lead to bad outcomes. To understand why this is, we first need to understand a little more about the zip code itself.

In a nutshell, ZIP Codes don’t represent geographic areas, so it doesn’t make sense to treat them like they are.

Speaking of earthquakes, Will Chase looked back at a 2012 earthquake in Sumatra that triggered not only a bunch of small ones in the vicinity, but other large ones around the world:

In the ten days following the Sumatra mainshock, 44 earthquakes M5.5+ were recorded. The statistics were clear: this nine-fold increase was highly significant, and most researchers agreed the likely explanation was remote triggering by the M8.6 Sumatra earthquake. This massive triggering of large earthquakes was unprecedented, and many wondered if the strike-slip mechanism at such a large magnitude was a unique combination that led to this outsized response.

For The New York Times, Derek Watkins used animated maps to show how a large earthquake can lead to thousands of small ones.

Living in California, I’ve experienced a handful of these, but it had been a while before feeling one a few weeks ago. I may or may not have immediately started looking at earthquake insurance.

For everyone who does data visualization for a living right now, there was someone who came before.

Over the past four years, Planet deployed 293 satellites in low orbit to take a snapshot of Earth every day. This animation by Nadieh Bremer shows how the snapshot gets pieced together. Most of me is like, yeah awesome. But then there’s that remaining bit of me that is a little bit nervous.

This animated interactive explains how a research group is using light to measure the speed of objects millions of miles away. Light that is farther away will appear to be a different color than a light that is closer. So by measuring the rate of color shifts, they can estimate velocity.

In a compare-your-preconceptions-against-reality quiz, The Upshot asks, “Are you rich?” Enter your nearest metro area, income, and what you consider to be rich. See where you actually land.

Gerry uses congressional district boundaries as letters. Hahahahaha. Oh wait.

Every month I collect useful visualization tools and resources. Here’s the good stuff for July.

Using blobbies with varying traits such as size, speed, and food gathering ability, Primer simulates natural selection in the explainer video below. Blobby.

[arve url=”https://www.youtube.com/watch?v=0ZGbIKd0XrM” /]

Lauren Baldo illustrated how he applies color theory in his paintings and illustrations. You don’t have to travel far to see how this transfers to visualization.

When staying at national parks, some people choose a tent. Some bring an RV. Others might stay in a lodge or sleep under the stars. Of course, it depends on where they stay and the weather during any given time of year. Using data from the National Park Service, Jordan Vincent charted all these things with a multi-faceted approach.

Each band represents patterns for an accommodation type over a year, band width represents number of nights stayed per month, and radius represents volume. Average temperature sits in the background.

Oftentimes, putting so many variables together in one view hides patterns, but this abstract view feels intuitive, even if less concrete. [Thanks, Dario]