Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Visualizing Earth's Population via a Kernel Approach

DZone's Guide to

Visualizing Earth's Population via a Kernel Approach

Visualization is a common way to convey complex information. Here, I use a kernel-based algorithm to draw population density along latitudinal and longitudinal axes.

· Big Data Zone ·
Free Resource

How to Simplify Apache Kafka. Get eBook.

There was an interesting map on reddit this morning: a visualisation of latitude and longituge of where people live, on Earth.

Image title

So I tried to reproduce it. To compute the density, I used a kernel-based approach:

> library(maps)
> data("world.cities")
> X=world.cities[,c("lat","pop")]
> liss=function(x,h){
+   w=dnorm(x-X[,"lat"],0,h)
+   sum(X[,"pop"]*w)
+ }
> vx=seq(-80,80)
> vy=Vectorize(function(x) liss(x,1))(vx)
> vy=vy/max(vy)
> plot(world.cities$lon,world.cities$lat,)
> for(i in 1:length(vx)) 
+ abline(h=vx[i],col=rgb(1,0,0,vy[i]),lwd=2.7)

For the other axis, we use a miror technique, to ensure that -180 is close the +180

> Y=world.cities[,c("long","pop")]
> Ya=Y; Ya[,1]=Y[,1]-360
> Yb=Y; Yb[,1]=Y[,1]+360
> Y=rbind(Y,Ya,Yb)
> liss=function(y,h){
+   w=dnorm(y-Y[,"long"],0,h)
+   sum(Y[,"pop"]*w)
+ } 
> vx=seq(-180,180)
> vy=Vectorize(function(x) liss(x,1))(vx)
> vy=vy/max(vy)
> plot(world.cities$lon,world.cities$lat,pch=19)
> for(i in 1:length(vx)) 
+ abline(v=vx[i],col=rgb(1,0,0,vy[i]),lwd=2.7)

Now we can add the two, on the same graph:

12 Best Practices for Modern Data Ingestion. Download White Paper.

Topics:
visualization

Published at DZone with permission of

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}