andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2065 knowledge-graph by maker-knowledge-mining
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Introduction: Robert Gonzalez reports on some beautiful graphs from John Nelson. Here’s Nelson: The sexes start out homogenous, go super segregated in the teen years, segregate for business in the twenty-somethings, and re-couple for co-habitation years. Then the lights fade into faint pockets of pink. I [Nelson] am using simple tract-level population/gender counts from the US Census Bureau. Because their tract boundaries extend into the water and vacant area, I used NYC’s Bytes of the Big Apple zoning shapes to clip the census tracts to residentially zoned areas -giving me a more realistic (and more recognizable) definition of populated areas. The census breaks out their population counts by gender for five-year age spans ranging from teeny tiny infants through esteemed 85+ year-olds. And here’s Gonzalez: Between ages 0 and 14, the entire map is more or less an evenly mixed purple landscape; newborns, children and adolescents, after all, can’t really choose where the
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1 Robert Gonzalez reports on some beautiful graphs from John Nelson. [sent-1, score-0.087]
2 Here’s Nelson: The sexes start out homogenous, go super segregated in the teen years, segregate for business in the twenty-somethings, and re-couple for co-habitation years. [sent-2, score-0.146]
3 Then the lights fade into faint pockets of pink. [sent-3, score-0.208]
4 I [Nelson] am using simple tract-level population/gender counts from the US Census Bureau. [sent-4, score-0.09]
5 Because their tract boundaries extend into the water and vacant area, I used NYC’s Bytes of the Big Apple zoning shapes to clip the census tracts to residentially zoned areas -giving me a more realistic (and more recognizable) definition of populated areas. [sent-5, score-0.426]
6 The census breaks out their population counts by gender for five-year age spans ranging from teeny tiny infants through esteemed 85+ year-olds. [sent-6, score-0.366]
7 And here’s Gonzalez: Between ages 0 and 14, the entire map is more or less an evenly mixed purple landscape; newborns, children and adolescents, after all, can’t really choose where they live – let alone where they’re born. [sent-7, score-0.348]
8 But between the ages of 15 and 19, something interesting happens. [sent-8, score-0.105]
9 As Nelson writes on his blog: We are in the age-span where teens/young adults can choose where to live. [sent-9, score-0.097]
10 Immediately we see clusters of females and, to a lesser extent, clusters of males. [sent-11, score-0.242]
11 We also start to see the filling of Rikers Island with green dots as young men begin to populate the jail complex. [sent-17, score-0.113]
12 In their early twenties, for example, professional women tend to gather in Midtown Manhattan, while swaths of early-twenty-something masculinity emerge in places like the SUNY Maritime College, and Yeshiva University. [sent-22, score-0.107]
13 The forties and fifties are characterized by a re-segregation of genders, and a thinning population. [sent-26, score-0.212]
14 Women outnumber the remaining men at a rate of better than two to one. [sent-31, score-0.182]
15 Various retirement communities popular with women become apparent, almost as strongly as their geographic preferences in their teens and twenties. [sent-32, score-0.286]
16 These maps are a beautiful illustration of the Chris Rock effect . [sent-36, score-0.172]
17 But he says it so well that we get a shock of recognition, the joy of relearning what we already know, but hearing it in a new way that makes us think more deeply about all sorts of related topics. [sent-38, score-0.143]
18 Statisticians, following John Tukey and Bill Cleveland, emphasize the ability of graphical data displays to reveal things that we have never thought of before. [sent-40, score-0.085]
19 In contrast, graphics designers celebrate innovative designs and visual juxtapositions that reveal interesting aspects of data but without highlighting any particular comparisons. [sent-41, score-0.152]
20 It is way more truthful a means of presenting relative geographic dispersion and affiliation than, say, choropleth mapping, which will be the carto-whipping-post of 2013. [sent-48, score-0.318]
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Introduction: Robert Gonzalez reports on some beautiful graphs from John Nelson. Here’s Nelson: The sexes start out homogenous, go super segregated in the teen years, segregate for business in the twenty-somethings, and re-couple for co-habitation years. Then the lights fade into faint pockets of pink. I [Nelson] am using simple tract-level population/gender counts from the US Census Bureau. Because their tract boundaries extend into the water and vacant area, I used NYC’s Bytes of the Big Apple zoning shapes to clip the census tracts to residentially zoned areas -giving me a more realistic (and more recognizable) definition of populated areas. The census breaks out their population counts by gender for five-year age spans ranging from teeny tiny infants through esteemed 85+ year-olds. And here’s Gonzalez: Between ages 0 and 14, the entire map is more or less an evenly mixed purple landscape; newborns, children and adolescents, after all, can’t really choose where the
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Introduction: I dodged a bullet the other day, blogorifically speaking. This is a (moderately) long story but there’s a payoff at the end for those of you who are interested in forecasting or understanding voting and public opinion at the state level. Act 1 It started when Jeff Lax made this comment on his recent blog entry: Nebraska Is All That Counts for a Party-Bucking Nelson Dem Senator On Blowback From His Opposition To Kagan: ‘Are They From Nebraska? Then I Don’t Care’ Fine, but 62% of Nebraskans with an opinion favor confirmation… 91% of Democrats, 39% of Republicans, and 61% of Independents. So I guess he only cares about Republican Nebraskans… I conferred with Jeff and then wrote the following entry for fivethirtyeight.com. There was a backlog of posts at 538 at the time, so I set it on delay to appear the following morning. Here’s my post (which I ended up deleting before it ever appeared): Party-Bucking Nelson May Be Nebraska-Bucking as Well Under the head
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