Great Britain has voted to leave the E.U., and election result cartograms are all over the internet. However, for our map we decided to stick with a simple map instead.
Using open source tools it is now super easy to make your own map tiles, and with a little extra work you can render them in whatever map projection you want. No more excuses to use Mercator! For example, here is a map we published today at The Upshot. It shows where prime-age women are working more or less then average, and includes data from county-level in the overview map down to every census tract once you zoom in. And all is nicely projected in Albers Equal-Area Conic, a projection widely adopted as standard for U.S. maps.
While working on the soon-to-be-released map widget for Piwik (heck, it’s been over two years since the first sketches!) I implemented two map symbol clustering algorithms into Kartograph.js. Last year I wrote about why this is a good idea, and now I turned that advice into re-usable code.
In this post I want to share my findings after experimenting with different clustering techniques.
Over the last two years, cartography has drawn my attention from time to time. In 2009 I started my work in the field by porting the PROJ.4 library to ActionScript. My first notable interactive map application was a world map widget for the Piwik Analytics project, which is in use until today. It was born from the need to have a simple world map that is lightweight, easy to use and completely independent from external map services like Google Maps.
Over the last week I had some fun playing with choropleth maps. Thereby I analyzed the following US poverty map, which was recently published at the Guardian data blog:
To be honest, the first time I saw this map I didn’t thought much about it. Ok, poverty is highest in south central of the United States, especially near Mexican border. But recently I used the same data to demonstrate a choropleth map that I created from-scratch and I was really surprised to see a somewhat different picture:
In this post I will explain the rendering process of the Piwik country maps. The results will basically look like this:
You probably remember this map of Facebook friendships, which made it’s way through the web in December 2010.
In this related article, the creator and Facebook intern Paul Butler explains the process of rendering the map. The most interesting remark he made can be found in the last sentence:
It’s not just a pretty picture, it’s a reaffirmation of the impact we have in connecting people, even across oceans and borders.
Well, there are some nice things about the Facebook friendships map. I like the colors. I like the density of information. I like the patterns. But there’s one big part of the picture missing in the map.
And that’s the internet.
After all, Facebook is “just” a website and websites can be accessed through the internet. So, when speaking of “connecting people, even across oceans and borders“, we should demonstrate our respect for the internet. To do so, I re-created my old map of the internet:
It shows more than 300k geographic locations of ip adresses, based on the free geo location database by MaxMind. Interestingly, the map shows almost the same “patterns” and complexity that is visible in the Facebook data. Given that, the remaining insight we get of the Facebook map is that Facebook is accessed by people all around the web (except for China).
No big news here.
Here’s a short documentation of my current progress in re-creating the Piwik maps in SVG/JS. Since we’re going to create at least one pre-generated map source file per country, file size is going to be an important issue. That’s why we thought about removing all polygons whose area is below a certain threshold, say 10 square km.