This is neat. A Google Arts & Culture Experiment, X Degrees of Separation shows a path of visual connections between two art pieces of your choosing. It’s like Six Degrees of Kevin Bacon but with art, computer vision, and machine learning.
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In an effort to get people more interested in and to learn about artificial intelligence, Google just launched A.I. Experiments to showcase the technology in fun ways.
With all the exciting A.I. stuff happening, there are lots of people eager to start tinkering with machine learning technology. A.I. Experiments is a showcase for simple experiments that let anyone play with this technology in hands-on ways, through pictures, drawings, language, music, and more.
You can also download the code for each project and have a go yourself.
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John Nelson has a knack for making maps that glow, where the base map serves as a dark backdrop and the data of interest sort of lights up. In a recent talk, he calls it Firefly Cartography and explains its use in presentation and in education.
A firefly map is to regular thematic maps the way that a lightsaber is to swords. Thematic layers that look like they are etched with white hot plasma tend to draw eyeballs and provide a sense of intensity that solid Boolean symbology just doesn’t offer. I think we are wired to notice and note things that glow. Whether it is marking time by the sun or moon, staring into embers, watching for nighttime travelers by the open flame they carry, or noting the churned phosphorescence of the sea, we historically have done well to note the things that glow.
I suspect firefly charts would be equally expressive.
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During the election, The New York Times showed a live gauge to show the current forecast for Clinton and Trump. It moved to show a 25th to 75th percentile band of uncertainty:
A lot of people didn’t get it, and it seemed to upset plenty of people too. Gregor Aisch, an NYT graphics editor, explains what they tried to accomplish with the gauges.
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Default color schemes are often horrible, but spending a lot of time putting together color schemes when you don’t have a lot of minutes is also horrible. I Want Hue by Mathieu Jacomy at the Sciences-Po Medialab lets you set a few options, and it spits out a palette for your visualization.
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Data Sketches, a collaboration between Nadieh Bremer and Shirley Wu, aims to visualize a monthly topic in two different ways.
Each month, we choose a topic and aim to have a visualization completed by the end of the month. Within the topic, we’re free to do whatever we want; a perfect opportunity to create, experiment, and have fun. The collaboration also gives us an encouraging sounding board, and a motivating pressure: a mutual reluctance of letting the other down.
They’re four months into the project with some fun results, and it’s a good example of how similar things can be shown in very different ways.
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This is an update to the guide I wrote in 2009, which as it turns out, is now mostly outdated. So, 2016. Here we go.
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Transitioning Map, Part 2: Refining the Format and Layout
How to make a more readable and more visually accurate map, before you dive into the big transitions.
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This is some fine map work by Lazaro Gamio and Dan Keating for the Washington Post. It shows total votes and margin of victory for each county with a peaks and valleys metaphor. Taller means more votes, and wider means greater margin. The Post rotated the United States map so that the east coast is on top and the west coast is on the bottom, which allows for scrollability and provides more space horizontally to show margins.
Nice.
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A group of researchers wondered if there was a trend or predictability for when a scientist’s most impactful work came about. It’s random.
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In case you didn’t get the memo, pattern fills are back and so hot right now. In the category of new-to-me, Textures.js by Riccardo Scalco is a JavaScript library that makes adding lines and dots in place of boring solid colors a trivial task.
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[arve url=”https://youtu.be/Fqo5xPDCts8″]
Neil Halloran, creator of the interactive World War II documentary focused on deaths, is working on another focused on the cost of nuclear war. With the election tomorrow, Halloran pushed out an “election cut” to highlight what’s at stake. Very scary.
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Noah Veltman put together a history of newspapers’ presidential endorsements since 1980 for about 100 publications. There’s a simple table showing Republican, Democrat, or other endorsement over the years, and you can download the data too.
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We saw the changing percentage of white people in the United States and how whites are not the majority in a lot of places. Who is the majority in these areas? Here’s a breakdown for the main three races that make up majorities.
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Below is the estimated percentage of white population in the United States from 1970 to 2010, based on data from the Census Bureau and made more accessible by NHGIS. I like the evaporative quality coming up in the southwest.
Mostly though, this is just me trying out a new toy, and the form fascinates me at the moment.
Members: I’m working on a set of tutorials for how to make these. There are a few steps involved, so I’m breaking it down to make it more digestible. Part 1 is here.
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Every election, there’s a slew of election maps that come in all shapes and sizes. The maps have evolved with the web, the amount of data available, and the level of reader interest, and it’s about finding a balance between the new and what works. To see the evolution, you can look to The New York Times portfolio over the decades. The Upshot has the rundown.
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It’s been three years since the Affordable Health Care Act. Margot Sanger-Katz and Quoctrung Bui for The Upshot look at how this changed the percentage of those who were uninsured in this country. In some places the percentages didn’t change much, but in many others, there are much fewer uninsured.
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For anyone who watched the presidential debates, I think it was fairly obvious what emotion each candidate projected at various moments. However, a group of graduate students from Columbia University applied computer vision and sentiment analysis to get a more quantitative gauge. Because, sure, why not. Sarah Slobin for Quartz explains the results.