andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1810 knowledge-graph by maker-knowledge-mining
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Introduction: Abby points us to a spare but cool visualization . I don’t like the curvy connect-the-dots line, but my main suggested improvement would be a closer link to the map . Showing median income on census tracts along subway lines is cool, but ultimately it’s a clever gimmick that pulls me in and makes me curious about what the map looks like. (And, thanks to google, the map was easy to find.)
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same-blog 1 1.0 1810 andrew gelman stats-2013-04-17-Subway series
Introduction: Abby points us to a spare but cool visualization . I don’t like the curvy connect-the-dots line, but my main suggested improvement would be a closer link to the map . Showing median income on census tracts along subway lines is cool, but ultimately it’s a clever gimmick that pulls me in and makes me curious about what the map looks like. (And, thanks to google, the map was easy to find.)
2 0.28168425 1006 andrew gelman stats-2011-11-12-Val’s Number Scroll: Helping kids visualize math
Introduction: This looks cool.
3 0.22591521 2288 andrew gelman stats-2014-04-10-Small multiples of lineplots > maps (ok, not always, but yes in this case)
Introduction: Kaiser Fung shares this graph from Ritchie King: Kaiser writes: What they did right: - Did not put the data on a map - Ordered the countries by the most recent data point rather than alphabetically - Scale labels are found only on outer edge of the chart area, rather than one set per panel - Only used three labels for the 11 years on the plot - Did not overdo the vertical scale either The nicest feature was the XL scale applied only to South Korea. This destroys the small-multiples principle but draws attention to the top left corner, where the designer wants our eyes to go. I would have used smaller fonts throughout. I agree with all of Kaiser’s comments. I could even add a few more, like using light gray for the backgrounds and a bright blue for the lines, spacing the graphs well, using full country names rather than three-letter abbreviations. There are so many standard mistakes that go into default data displays that it is refreshing to see a simple graph done
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Introduction: See here . Cool–it looks like they’re doing interesting stuff, and it’s great to see this sort of support for applied research.
5 0.15465826 1653 andrew gelman stats-2013-01-04-Census dotmap
Introduction: Andrew Vande Moere points to this impressive interactive map from Brandon Martin-Anderson showing the locations of all the residents of the United States and Canada. It says, “The map has 341,817,095 dots – one for each person.” Not quite . . . I was hoping to zoom into my building (approximately 10 people live on our floor, I say approximately because two of the apartments are split between two floors and I’m not sure how they would assign the residents), but unfortunately our entire block is just a solid mass of black. Also, they put a few dots in the park and in the river by accident (presumably because the borders of the census blocks were specified only approximately). But, hey, no algorithm is perfect. It’s hard to know what to do about this. The idea of mapping every person is cool, but you’ll always run into trouble displaying densely populated areas. Smaller dots might work, but then that might depend on the screen being used for display.
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Introduction: Abby points us to a spare but cool visualization . I don’t like the curvy connect-the-dots line, but my main suggested improvement would be a closer link to the map . Showing median income on census tracts along subway lines is cool, but ultimately it’s a clever gimmick that pulls me in and makes me curious about what the map looks like. (And, thanks to google, the map was easy to find.)
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Introduction: This looks cool.
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Introduction: From Ira Stoll , a link to this cool data site , courtesy of the Manhattan Institute, with all sorts of state budget information including the salaries of all city and state employees.
4 0.66943556 2153 andrew gelman stats-2013-12-29-“Statistics Done Wrong”
Introduction: Govind Manian points me to this online textbook by Alex Reinhart. It’s hard for me to evaluate because I am so close to the material. But on first glance it looks pretty reasonable to me.
Introduction: Aleks points me to this article showing some pretty maps by Eric Fisher showing where people of different ethnicity live within several metro areas within the U.S. The idea is simple but effective; in the words of Cliff Kuang: Fisher used a straight forward method borrowed from Rankin: Using U.S. Census data from 2000, he created a map where one dot equals 25 people. The dots are then color-coded based on race: White is pink; Black is blue; Hispanic is orange, and Asian is green. The results for various cities are fascinating: Just like every city is different, every city is integrated (or segregated) in different ways. New York is shown below. No, San Francisco is not “very, very white” But I worry that these maps are difficult for non-experts to read. For example, Kuang writes the following:: San Francisco proper is very, very white. This is an understandable mistake coming from someone who, I assume, has never lived in the Bay Area. But what’s amazing i
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Introduction: Abby points us to a spare but cool visualization . I don’t like the curvy connect-the-dots line, but my main suggested improvement would be a closer link to the map . Showing median income on census tracts along subway lines is cool, but ultimately it’s a clever gimmick that pulls me in and makes me curious about what the map looks like. (And, thanks to google, the map was easy to find.)
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Introduction: Xian sends along this link that might be of interest to some of you.
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Introduction: In his new book, “What is Your Race? The Census and Our Flawed Efforts to Classify Americans,” former Census Bureau director Ken Prewitt recommends taking the race question off the decennial census: He recommends gradual changes, integrating the race and national origin questions while improving both. In particular, he would replace the main “race” question by a “race or origin” question, with the instruction to “Mark one or more” of the following boxes: “White,” “Black, African Am., or Negro,” “Hispanic, Latino, or Spanish origin,” “American Indian or Alaska Native,” “Asian”, “Native Hawaiian or Other Pacific Islander,” and “Some other race or origin.” Then the next question is to write in “specific race, origin, or enrolled or principal tribe.” Prewitt writes: His suggestion is to go with these questions in 2020 and 2030, then in 2040 “drop the race question and use only the national origin question.” He’s also relying on the American Community Survey to gather a lo
4 0.73053861 2146 andrew gelman stats-2013-12-24-NYT version of birthday graph
Introduction: They didn’t have room for all four graphs of the time-series decomposition so they just displayed the date-of-year graph: They rotated so the graph fit better on the page. The rotation worked for me, but I was a bit bummed that that they put the title and heading of the graph (“The birthrate tends to drop on holidays . . .”) on the left in the Mar-Apr slot, leaving no room to label Leap Day and April Fool’s. I suggested to the graphics people that they put the label at the very top and just shrink the rest of the graph by 5 or 10% so as to not take up any more total space. Then there’d be plenty of space to label Leap Day and April Fool’s. But they didn’t do it, maybe they felt that it wouldn’t look good to have the label right at the top, I dunno.
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