Time zones are odd sometimes.

“Normal America.” I’m not sure what that means anymore, but at some point it had a lot to do with demographics. Naturally, the “normal” that you look at or want bleeds into policy-making and the like. Jed Kolko for FiveThirtyEight looks into the states most similar to the country overall — the one from 1950 and from today.

But the places that look today most like 1950 America are not large metros but rather smaller metros and rural areas. Looking across all of America, including the rural areas, the regions that today look most demographically similar to 1950 America are the portion of eastern Ohio around the towns of Cambridge and Coshocton and the Cumberland Valley district in southeastern Kentucky.

The states most similar demographically to today’s America: Illinois, New York, New Jersey, Connecticut, and Virginia.

From Josh Begley, this quickfire flip book shows every New York Times front page since 1852. Watch the shift from all words, to a handful of small pictures, to larger pictures, to color, and then more color pictures.

[arve url=”https://vimeo.com/204951759″/]

It reminds me of the flip book for the Hawaiian Star and the comparison of pages for popular science magazines, which show a similar evolution.

To celebrate the ten-year anniversary of the National Art Center in Tokyo, Emmanuelle Moureaux made the Forest of Numbers.

The installation “Forest of Numbers” visualized the decade of the future from 2017 to 2026, created a sense of stillness across the large exhibition space. More than 60,000 pieces of suspended numeral figures from 0 to 9 were regularly aligned in three dimensional grids. A section was removed, created a path that cut through the installation, invited visitors to wonder inside the colorful forest filled with numbers.

Some say annotation is the most important layer for charts meant for public consumption. It directs readers where to look and what’s important. But the process is not always straightforward. ChartAccent is an application slash research project that aims to make annotation easier. Plug in some data, make a chart, and do some clicking and dragging. Done.

For Excel users getting started with R, pain oftentimes finds its way into the learning process. Gordon Shotwell feels your pain and provides a primer to shifting to a different approach to your data.

At the beginning, when you are trying to accomplish simple things like balancing a budget or entering some data by hand, R is definitely harder to learn than Excel. However, as the task gets more complex, it becomes easier to accomplish in R than Excel, because the core structures of Excel are designed for relatively simple use cases and are not the best for more complex problems. This isn’t to say that you can’t solve a lot of complex problems with Excel, it’s just that the tool won’t make it easy for you.

Worth that little bit of extra effort in the beginning IMHO.

When you search for datasets on The White House site, you get nothing. So yeah. That’s where we’re at.

In 1900, W. E. B. Du Bois and his students drew a series of charts for The Exhibit of American Negroes. They’re not all winners, but these were hand-drawn in 1900, so there’s some leeway there. There are also a handful of graphics that use graphic devices that we sometimes mistake for modern methods, like cartograms to compare values and a bent bar graph to allow smaller values some space on a zoomed-in axis.

The stacked bar graph above, which shows marital status by age, struck me especially because I made a similar chart for the current population. I am like, so 1900. [via @michalmigurski]

[arve url=”https://vimeo.com/201178499″/]

Facebook logs data about you and how you use their application. I know this. You know this. From there, Facebook makes inferences and serves you ads that might be relevant. Data Selfie is a Chrome extension that attempts to log similar data about you so that you can see what Facebook sees.

The data stays local on your computer, and you can export it or delete it. You also get a dashboard view of the data with what you liked, viewed, and inferences based on a combination of machine learning algorithms.

Grab the extension here.

As part of his dissertation, Geoff Boeing generated these maps that show one square mile of road network in select cities.

To compare urban form in different kinds of places, these visualizations have depicted some downtowns, some business parks, and some suburban residential neighborhoods. These patterns also vary greatly within cities: Portland’s suburban east side looks very different than its downtown, and Sacramento’s grid-like downtown looks very different than its residential suburbs. These visualizations, rather, show us how different urbanization patterns and paradigms compare at the same scale.

Roll your own using Boeing’s OpenStreetMap-based Python package, OSMnx. Just one line of code.

[arve url=”https://www.youtube.com/watch?v=jD8tjhVO1Tc”/]

My son used to watch Daniel Tiger’s Neighborhood (a modern take on Mister Rogers’ Neighborhood) a lot, and one song’s chorus goes like, “In some ways we are different, but in so many ways we are the same.” This commercial from TV2 in Denmark is the grown-up, categorical version of that message.

Hans Rosling passed away this morning. The man. The legend.

[arve url=”https://www.youtube.com/watch?v=YpKbO6O3O3M”/]

Most goods imported from Mexico are untaxed under the North American Free Trade Agreement. The Administration wants to tax those billions of dollars of goods coming in. David Yanofsky for Quartz plotted the imported products.
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Enrico Bertini, who has taught information visualization at New York University for the past few years, put up his class materials for open use. There are lecture slides, exercises, and a course diary of his own teaching experiences. Should be useful if you want to teach or learn on your own.

Back in my day, I didn’t have formal visualization courses. I checked out paper books from the library, pieced together tidbits of Flash tutorials meant for games, and walked in the snow for five miles to and from school. Consider yourself lucky.

Christian Laesser takes an abstract look at how different languages represent Vincent van Gogh through various Wikipedia pages.

The visualization explores how different languages present Van Gogh’s work and life by images. Inspired by Geolinguistic Contrasts in Wikipedia. The viz tries to show different narative strategies by showing the image type, origin date and authorship. You can reveal the connections between languages by hovering the images.

I’m not quite convinced this helps with understanding, but I appreciate the experimentation.

As I’m sure you know, the current administration banned immigrants from seven countries recently. The New York Times looks at immigrants from these countries who already settled in the United States — their education, salaries, and where they live.

Inaugural addresses come in different flavors, with different messages and purpose. Periscopic passed video of the ten most recent speeches through the Microsoft Emotion API to estimate emotion from each speaker’s facial expressions. Then they used a feather metaphor to visualize the results.

Shown here in the form of collected emotion arcs, each “feather” represents an inaugural address. Each barb of the feather is a moment during the speech where the president displayed an emotion — positive emotions are drawn above the quill, negative emotions below. The length of each barb represents the intensity of the emotion. The curve of the feather itself indicates the overall positivity or negativity of the speech.

As you might expect, the feather for Donald Trump weighs predominantly downward in red and orange.

There are a lot of R packages, which is why before I implement any chart type myself, I look to see if someone already did it. Recently, the official R package repository surpassed the 10,000 mark.

Why so many packages? R has a very active developer community, who contribute new packages to CRAN on a daily basis. As a result, R is unparalleled in its capabilities for statistical computing, data science, and data visualization: almost anything you might care to do with data has likely already been implemented for you and released as an R package.

That’s quite the feat for a language only statisticians knew about not that long ago.