Posts tagged with data viz

Research focuses on real wages—wages that are adjusted for inflation. Getting data on wages is tricky. But accounting for inflation is even harder. (For example, workers often paid rent informally, meaning that there are few records around).

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And so it is unsurprising that researchers differ in their estimations of real wages. Some, such as Peter Lindert and Jeffrey Williamson, suggest that full-time earnings for British common labourers, adjusted for inflation, more than doubled in the seventy years after 1780. But Charles Feinstein argued that over the same period, British real wages only increased by around 30%. It’s a bit of a … mess.

Most people agree that after about 1840, real wages did better. Nicholas Crafts and Terence Mills shows that from 1840 to 1910, real wages more than doubled. Their findings are mirrored by other researchers ….

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in almost all British cities, mortality conditions in the 1860s were no better—and were often worse—than in the 1850s. In Liverpool in the 1860s, the life expectancy fell to an astonishing 25 years. It was not until the two subsequent decades that rises in life expectancy were found

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roots of x²⁶•y + x•z + y¹³•z + x•y¹³ + z²⁶     =   0

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(Source: imaginary.org)










Just some sweet, sweet pictures of science:

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datahacker:

San Francisco contour lines. got inspired by someone else’s version, thought it would be fun to try it out. contour data from datasf. (d3, three.js, python)

(Source: polytopes)










Two interesting ideas here:

  • "trading time"
  • price impact of a trade proportional to exp( √size )

Code follows:

Read More

(Source: finmath.stanford.edu)










Michael Conover: Information Visualization for Large-Scale Data Workflows

  • data geometry
  • memes
  • visual analysis of program structure
  • visual analysis of propaganda
  • image
  • compare last week’s analysis and share with colleagues
  • geom_bin2d rather than geom_point(alpha=...) in ggplot2
  • ggpairs
  • automated grading: in addition to unit testing, 1) parse syntax trees of submissions, 2) define edit distance between them, 3) induces a network structure, 4) identify clusters, 5) give feedback to a representative member of the cluster and cc: everyone else




playing along with Elias Wegert in R:

X <- matrix(1:100,100,100)                  #grid
X <- X * complex(imaginary=.05) + t(X)/20    #twist & shout
X <- X - complex(real=2.5,imaginary=2.5)     #recentre
plot(X, col=hcl(h=55*Arg(sin(X)), c=Mod(sin(X))*40 ) ,        pch=46, cex=6)

Found it was useful to define these few functions:

arg <- function(z) (Arg(z)+pi)/2/pi*360     #for HCL colour input
ring <- function(C) C[.8 < Mod(C) &   Mod(C) < 1.2]        #focus on the unit circle
lev <- function(x) ceiling(log(x)) - log(x)
m <- function(z) lev(Mod(z))
plat <- function(domain, FUN) plot( domain, col= hcl( h=arg(FUN(domain)), l=70+m(domain)), pch=46, cex=1.5, main=substitute(FUN) )           #say it directly

NB, hcl's hue[0,360] so phase or arg needs to be matched to that.










A billion chronically hungry people in the world via The Economist
As you can see from the right-hand scale, during the 1990&#8217;s and 2000&#8217;s the &#8220;bottom billion&#8221; poorest people have been starving or close to it.
Even though the right-hand scale is more important, the lines get graphical emphasis.
Therefore the two pictures, though nearly equivalent in absolute terms, tell very different stories:about a spiking crisis and increasing failure to deal with poverty during rich-world recession
about marginal improvements that continue despite a rich-world financial debacle.

Both stories were told by the Food and Agriculture Organisation, of the United Nations.
Of course statistical bodies revise estimates all the time.
But still this juxtaposition warns us to question the facticity of numbers appearing in charts.
All data come from somewhere. Just because the numbers appear on a chart doesn&#8217;t make them correct.

A billion chronically hungry people in the world via The Economist

  • As you can see from the right-hand scale, during the 1990’s and 2000’s the “bottom billion” poorest people have been starving or close to it.
  • Even though the right-hand scale is more important, the lines get graphical emphasis.
  • Therefore the two pictures, though nearly equivalent in absolute terms, tell very different stories:
    1. about a spiking crisis and increasing failure to deal with poverty during rich-world recession
    2. about marginal improvements that continue despite a rich-world financial debacle.
  • Both stories were told by the Food and Agriculture Organisationof the United Nations.
  • Of course statistical bodies revise estimates all the time.
  • But still this juxtaposition warns us to question the facticity of numbers appearing in charts.
  • All data come from somewhere. Just because the numbers appear on a chart doesn’t make them correct.

hi-res




3D map of the large-scale distribution of dark matter, reconstructed from measurements of weak gravitational lensing with the Hubble Space Telescope.
via davidaedwards

3D map of the large-scale distribution of dark matter, reconstructed from measurements of weak gravitational lensing with the Hubble Space Telescope.

via davidaedwards


hi-res




Car Concept Visual Memex via sfdp by Tomasz Malisiewicz

hi-res