Facebook connections around the world

Paul Butler a member of facebook's data infrastructure team map created a very visual map of users around the world.
The map clearly shows areas where facebook is widely used and some areas like china and central africa where facebook does not have many users.

From the map you can see the impact in Kenya and east africa.This goes to prove the social networking users are increasing daily in  East Africa.A lot of people have twitter, facebook and other popular web 2.0 accounts.

This what Paul said on his blog.


Visualizing data is like photography. Instead of starting with a blank canvas, you manipulate the lens used to present the data from a certain angle.

When the data is the social graph of 500 million people, there are a lot of lenses through which you can view it. One that piqued my curiosity was the locality of friendship. I was interested in seeing how geography and political borders affected where people lived relative to their friends. I wanted a visualization that would show which cities had a lot of friendships between them.

I began by taking a sample of about ten million pairs of friends from Apache Hive, our data warehouse. I combined that data with each user's current city and summed the number of friends between each pair of cities. Then I merged the data with the longitude and latitude of each city.

At that point, I began exploring it in R, an open-source statistics environment. As a sanity check, I plotted points at some of the latitude and longitude coordinates. To my relief, what I saw was roughly an outline of the world. Next I erased the dots and plotted lines between the points. After a few minutes of rendering, a big white blob appeared in the center of the map. Some of the outer edges of the blob vaguely resembled the continents, but it was clear that I had too much data to get interesting results just by drawing lines. I thought that making the lines semi-transparent would do the trick, but I quickly realized that my graphing environment couldn't handle enough shades of color for it to work the way I wanted.

Instead I found a way to simulate the effect I wanted. I defined weights for each pair of cities as a function of the Euclidean distance between them and the number of friends between them. Then I plotted lines between the pairs by weight, so that pairs of cities with the most friendships between them were drawn on top of the others. I used a color ramp from black to blue to white, with each line's color depending on its weight. I also transformed some of the lines to wrap around the image, rather than spanning more than halfway around the world.