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While I was working for a training on data visualization, I wanted to get a nice visual for John Snow’s cholera dataset. This dataset can actually be found in a great package of famous historical datasets.

library(HistData)
data(Snow.deaths)
data(Snow.streets)

One can easily visualize the deaths, on a simplified map, with the streets (here simple grey segments, see Vincent Arel-Bundock’s post)

plot(Snow.deaths[,c("x","y")], col="red", pch=19, cex=.7,xlab="", ylab="", xlim=c(3,20), ylim=c(3,20))
slist <- split(Snow.streets[,c("x","y")],as.factor(Snow.streets[,"street"]))
invisible(lapply(slist, lines, col="grey"))

Of course, one might add isodensity curves (estimated using kernels)

require(KernSmooth)
kde2d <- bkde2D(Snow.deaths[,2:3], bandwidth=c(0.5,0.5))
contour(x=kde2d$x1, y=kde2d$x2,z=kde2d$fhat, add=TRUE) Now, what if we want to visualize that dataset on a nice background, from Google Maps, or OpenStreetMaps? The problem here is that locations are in a weird coordinate representation system. So let us use a different dataset. For instance, on Robin Wilson’s blog, one can get datasets in a more traditional representation (here the epsg 27700). We can extract the dataset from library(foreign) deaths=read.dbf(".../Cholera_Deaths.dbf") Then, we need our background, library(OpenStreetMap) map = openmap(c(lat= 51.516, lon= -.141), c(lat= 51.511, lon= -.133)) map=openproj(map, projection = "+init=epsg:27700") plot(map) points([email protected],col="red", pch=19, cex=.7 ) If we zoom in (the code above will be just fine), we get And then, we can compute the density [email protected] kde2d <- bkde2D(X, bandwidth=c(bw.ucv(X[,1]),bw.ucv(X[,2]))) based on the same function as before (here I use marginal cross-validation techniques to get optimal bandwidths). To get a nice gradient, we can use clrs=colorRampPalette(c(rgb(0,0,1,0), rgb(0,0,1,1)), alpha = TRUE)(20) and finally, we add it on the map image(x=kde2d$x1, y=kde2d$x2,z=kde2d$fhat, add=TRUE,col=clrs)
contour(x=kde2d$x1, y=kde2d$x2,z=kde2d\$fhat, add=TRUE)