Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. 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)) 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)

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) 