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"Tell the chef, the beer is on me."
Disinformation is kind of a problem these days, yeah? Fatih Erikli uses a simulation that works like a disaster spread model applied to social networks to give an idea of how disinformation spreads.
I tried to visualize how a disinformation becomes a post-truth by the people who subscribed in a network. We can think this network as a social media such as Facebook or Twitter. The nodes (points) in the map represent individuals and the edges (lines) shows the relationships between them in the community. The disinformation will be forwarded to their audience by the unconscious internet (community) members.
Set the “consciousness” parameter and select a node to run.
Reuben Fischer-Baum for The Washington Post looks at professional football expectations given their draft picks versus performance.
By comparing how much value teams should get given their set of picks with how much value they actually get, we can calculate which franchises make the most of their draft selections. Approximate Value (AV), a stat created by Pro Football Reference that measures how well a player performed overall in a season, is useful here. Based on this metric, we find that the Browns draftees have underperformed in the NFL given their draft position, especially when compared to the draftees of a team like, say, the Seahawks.
My main takeaway is that teams seem to know what they’re gonna get. Overall at least. Save a few teams who outdid expectations and a few who failed pretty badly, everyone else sticks towards the baseline. But it’s also really random year-to-year, which is essentially what makes sports interesting.
See also the player-level comparison for professional basketball from last year.
And, just a random observation, it felt weird reading this sports piece with “Democracy Dies in Darkness” at the header of The Washington Post site. But maybe that’s just me.
The choice for Most Valuable Player in the NBA is only minimally about the numbers, but it’s fun to look anyways. FiveThirtyEight makes the case for Stephen Curry. I particularly like the chart that shows how other players on a team fare when an MVP candidate doesn’t play.
Not only do virtually all of his teammates (10 of 11 players with at least 30 shots, representing over 1,700 shots taken without him3) shoot worse without Curry on the court to draw attention, they shoot dramatically worse. Overall, Curry’s teammates shoot 7.3 percentage points worse with Curry off the court, with his average teammate4 shooting 8.3 points worse. Among our MVP candidates, LeBron has the next-highest impact on average teammate shooting (3.9 points), followed by Westbrook (2.5 points). When it comes to opening up a team’s offense, Curry has no equal.
From Little Planet Factory, a Solar System in a bottle made to scale:
A small bottle attempting to maintain the correct scale between the 8 planets of the solar system at a scale of 1:5,000,000,000. Much as in reality the entire bottle is almost entirely dominated by the volume (and mass) of the four gas giants while the four solid planets settle almost dust like in comparison at the bottom of it.
Cute. [via @alykat]
Government data isn’t always the easiest to use with computers. Maybe it’s in PDF format. Maybe you have to go through a roundabout interface. Maybe you have to manually request files through an email address that may or may not work. However, this file that OpenElections received might take the cake.
It’s a spreadsheet, but the numbers are clipart.
City of Detroit produced a lookup tables for its absentee precincts in 2016. It's in Excel. But wait for it: the values are CLIP ART. pic.twitter.com/pzsPbjvc6j
— OpenElections (@openelex) April 17, 2017
Did someone enter clipart manually? Why is it clipart instead of numbers in Excel? Who made this file? So many questions, so little data.
In case you didn’t hear, California had a bit of a drought problem for the past few years. We complained about not enough rain constantly, and we finally got a lot of it this year. Now we complain that there’s too much rain (because you know, we have to restore balance). On the upside, the state looks a lot greener and less barren these days. David Yanofsky for Quartz has got your satellite imagery right here.
NASA recently released composite images of the Earth at night based on 2016 data, which was a follow-up to similar images for 2012. John Nelson compared the two, specifically looking for new lights that came on (blue) and lights that went off (pink). The former, suggesting growth and the latter, suggesting decline.
I know, it’s only April 2017, but some senators and representatives have some extra planning to as they figure out how to persuade midterm voters to re-elect them when the voters went a different direction for the presidential election. Kevin Schaul and Kevin Uhrmacher for The Washington Post use a scatterplot and scrollytelling to explain.
You had me at craft beer. Russell Goldenberg for The Pudding looks for the capital based on three factors — number of breweries, quality of breweries, and location — under the premise that the whole process of picking the best is really subjective.
Don’t miss the second the chart, which is a scatterplot that shifts favorite cities based on your preferences.
NASA just released a composite map of the world at night using satellite imagery from 2016. This is the first nighttime map since 2012, but the team behind the work hopes for bigger things with a more real-time system.
For instance, daily nighttime imagery could be used to help monitor unregulated or unreported fishing. It could also contribute to efforts to track sea ice movements and concentrations. Researchers in Puerto Rico intend to use the dataset to reduce light pollution and help protect tropical forests and coastal areas that support fragile ecosystems. And a team at the United Nations has already used night lights data to monitor the effects of war on electric power and the movement of displaced populations in war-torn Syria.
Be sure to check out the high-res versions to see all the little pockets of light around the world.
We live in a time when personal data leaves digital traces of what we do, what we like, and who we care about. Quinn Norton makes a concerted effort to not leave behind such traces using layers of security and encryption, which ironically makes for an old-fashioned love story.
My love affair has taught me that the age of data makes time solid in a way that it didn’t used to be. I have a calendar and email archive that nails down the when/where/who of everything I’ve done. I know when my kid was here; the last time I saw a friend in New York; exactly what my last email exchange with my mother was. Not so with my lover. Time is a softer thing for us. Sometimes it seems like he’s always been there, sometimes it seems like we’re a brand new thing. Every other relationship in my life is more nailed down than this one.
Soft time. I like it.
The Climate Change Coloring Book by Brian Foo makes data tactile and interactive. “The goal is to encourage learning, exploration, and reflection on issues related to climate change through act of coloring.” It’s in the early days of a Kickstarter campaign, but I suspect it’ll be funded in no time. Pledged.
When you first get a CSV file, sometimes it’s useful to poke at it a bit to see what’s there. Sometimes you need to restructure the data or sort it in some non-straightforward way. Tad is a lightweight desktop application that helps with this early stage of data gathering, “designed to fit in to the workflow of data engineers and data scientists.” It’s free and open source.
I played around with it a little bit, and it’s still a little rough around the edges, but it seems like a promising start, especially for larger datasets. For small datasets, you’re probably better off just firing up R, Excel, or whatever software you use already.
Facial hair styles change with the years. One year it might be more fashionable to be clean shaven and another year the trend might veer towards big beards. Reddit user Mystic_Toaster sifted through old university yearbooks to manually tabulate the trends from 1898 to 2008.
Obviously there’s some self-selection going on here. It’s only a sample of yearbooks from four universities, and yearbook photos don’t always match everyday styles, but it’s a fun peek into long-term changes.
See also the older 1976 study.
Oh, and here’s the spreadsheet for the chart above in case you want to make your own charts.
The most recent Data Stories episode with Elijah Meeks is worth a listen if you visualize data at work, want to visualize data for work, wish your work would value your visualization more, or all of the above.
deck.gl is an open source framework developed by Uber to visualize large datasets (mainly geospatial ones, naturally). It started as an internal tool but was released to the public in November last year. Uber just released the next iteration of the package, which handles a bunch more use cases. Bookmarked it.
On April Fool’s Day, Reddit launched a blank canvas that users could add a colored pixel every few minutes. It ran for 72 hours, and the evolution of the space as a whole was awesome.
What if you look more closely at the individual images, edits, and battles for territory? Even more interesting. sudoscript looks closer, breaking participants into three groups — the creators, protectors, and destroyers — who fight for the ideal Place. In the process, among the Dickbutt variations, penis jokes, and Pokémon characters, it’s a story of humanity. [via Moritz]
Mapping geographic data in R can be tricky, because there are so many ways to complete separate tasks. It’s a jumble of options and you have to figure out how to put the pieces together. This course walks you through the steps so that the process isn’t so jumbley.
FlowingData members can access the course right now. Learn how to load geographic data, draw boundaries, map locations, and make data-driven maps that are publication-ready. Then refine the process and learn how you can apply it to your own data.
"Tell the chef, the beer is on me."
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