John Snow’s cholera map of London recreated
What would John Snow’s famous cholera map look like on a modern map of London, using modern mapping tools? The map changed what we know about germs and disease - and created a new way of looking at the world. With the help of mapping tool CartoDB and using the Stamen style maps, this is how it looks with larger circles representing more deaths.
The previous post mentioned the BuRd theme and ColorBrewer. Here are some possible uses of both in a series of plots with cross-sectional country-level data. The code uses pooled WDI estimates for fertility and real GDP per capita as measured by the World Bank, and then adds UN region names to the data.
// package dependencies
ssc install wbopendata
ssc install kountry
// WDI data
wbopendata, indicator(SP.DYN.TFRT.IN; NY.GDP.PCAP.PP.CD) year(2008:2010) long clear
collapse ///
(mean) fr = sp_dyn_tfrt_in ///
(mean) gdpc = ny_gdp_pcap_pp_cd ///
, by(countrycode)
// geo indicators
kountry countrycode, from(iso3c) geo(un)
encode GEO, gen(region)
Geographical regions make it easy to plot the data over small multiples. I also often find it useful to look at a mosaic plot to diagnose how seriously missing data puts representativeness to threat in the sample.
// package dependencies
ssc install splineplot
// small multiples
gr hbox gdpc, over(region, sort(1) des) mark(1, ms(i) mlab(countrycode) mlabp(0)) name(boxes, replace)
hist fr, bin(4) by(region, total) name(bins, replace)
// missing data
gen full = !mi(fr, gdpc)
spineplot full region
Going a bit further with regression results, a variety of graphs can be useful for running diagnostics. The first one shown below is a LOESS fit across the residuals against the fitted values, and the second one is an example of weighted markers where the error term is shown along the linear fit.
// residuals
gen loggdpc = ln(gdpc)
reg fr loggdpc
predict r, resid
predict yhat
// residuals-versus-fitted values, plus LOESS
sc r yhat, mlab(countrycode) yline(0) ms(i) mlabp(0) || lowess r yhat, ///
name(residuals_loess, replace)
// linear fit with residually weighted points
sc fr loggdpc if abs(r) > .3 [w = abs(r)], ms(O) mc(gs14) mfc(gs12) || ///
lfit fr loggdpc || ///
sc fr loggdpc, ms(i) mlab(countrycode) mlabc(gs6) mlabp(0) legend(off) ///
name(residuals_rvf, replace)
Last, a map of the residuals can also be informative if there is suspicion of spatial dependence in the error term:
// package dependencies
ssc install spmap
// map of residuals (caution with intervals)
merge 1:1 countrycode using world-d, keep(match master) gen(mapmerge)
spmap r using world-c, id(_ID) clmethod(boxplot) ///
fcolor(RdYlGn) ndocolor(gs12) ndfcolor(gs14) ocolor(none ..) ///
legstyle(1) legend(ring(1) pos(3)) ///
name(residuals_map, replace)
Country-level data is an ideal candidate for plot tweaks such as using marker labels instead of observations. With survey data, there would be more work to do at the level of the data itsef, and text labels would have to be taken from aggregate measures like relative frequencies or averages, which makes it more complex to plot the data quickly and efficiently.
Observed US Temperature Change
A new report by the US Global Change Research Program explores climate change and its implications. The first draft, issued for public review, is the work of a 60-person advisory committee and 240 different authors. It draws on data from across US agencies.
Via the report (PDF):
U.S. temperatures will continue to rise, with the next few decades projected to see another 2°F to 4°F of warming in most areas. The amount of warming by the end of the century is projected to correspond closely to the cumulative global emissions of greenhouse gases up to that time: roughly 3°F to 5°F under a lower emissions scenario involving substantial reductions in emissions after 2050 (referred to as the “B1 scenario”), and 5°F to 10°F for a higher emissions scenario assuming continued increases in emissions (referred to as the “A2 scenario”)…
Human-induced climate change means much more than just hotter weather. Increases in ocean and freshwater temperatures, frost-free days, and heavy downpours have all been documented. Sea level has risen, and there have been large reductions in snow-cover extent, glaciers, permafrost, and sea ice. Winter storms along the west coast and the coast of New England have increased slightly in frequency and intensity. These changes and other climatic changes have affected and will continue to affect human health, water supply, agriculture, transportation, energy, and many other aspects of society.
Image: Observed US Temperature Change, via the NCADAC. “The colors on the map show temperature changes over the past 20 years in °F (1991-2011) compared to the 1901-1960 average. The bars on the graphs show the average temperature changes by decade for 1901-2011 (relative to the 1901-1960 average) for each region. The far right bar in each graph (2000s decade) includes 2011. The period from 2001 to 2011 was warmer than any previous decade in every region. (Figure source: NOAA NCDC / CICS-NC. Data from NOAA NCDC.)” Select to embiggen.
(via sunfoundation)
cabspotting data. prints coming soon.
The rest of the blog is worth looking at if you love nice maps.
![theatlantic:
Mapping Acceptance of Same-Sex Marriage
According to the rule books, same-sex marriage is mostly unacceptable in the U.S. But that’s not the case when looking at the opinions of the American people. According to a new set of maps from Esri, same-sex marriage is popular in large swaths of the country.
The maps break support for same-sex marriage down by county. Green and yellow dots represent counties where people support same-sex marriage, while orange and red dots represent places where people do not. As you can see, there’s no consensus across the country.
Read more. [Image: Esri]](http://25.media.tumblr.com/tumblr_mapgrnmdn41qcokc4o1_500.jpg)
Mapping Acceptance of Same-Sex Marriage
According to the rule books, same-sex marriage is mostly unacceptable in the U.S. But that’s not the case when looking at the opinions of the American people. According to a new set of maps from Esri, same-sex marriage is popular in large swaths of the country.
The maps break support for same-sex marriage down by county. Green and yellow dots represent counties where people support same-sex marriage, while orange and red dots represent places where people do not. As you can see, there’s no consensus across the country.
Read more. [Image: Esri]
(via sunfoundation)
“In keeping with Google’s more-data-is-better-data mantra, the maps team, largely driven by Street View, is publishing more imagery data every two weeks than Google possessed total in 2006.” (via How Google Builds Its Maps—and What It Means for the Future of Everything - Alexis C. Madrigal - The Atlantic)
