In his great blog The Daily Viz Matt Stiles recently posted this map of US crime rates. The map shows murder rates in different cities as bubble symbols and it strongly reminded me to write about the problem of overlapping map symbols.
As some of you pointed out in the comments of my last post, taking equidistant colors in the HSV color space is no solution for finding a set of colors that are perceived as equidistant. This post describes what’s wrong with HSV and what we can do about this. Note that since this post contains interactive elements built on the latest web technologies, you might need a modern browser to get the most out of it.
click here for ultimate color geekyness
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:
I used to love them, but it’s over now: diverging red-green color scales.
I bet the reason for the popularity of red-green color scales is that they are so easy to interpret (at least in my culture). Green means good, red means bad. For instance, the above map shows the income of private households for European regions. I used a diverging color scale to show the difference from the average income (bright yellow) with higher incomes in green and lower incomes in red. In fact, I picked the exact colors from good old flare visualization toolkit.
But there’s some trouble with this scale. Firstly, as alluded to above, the meaning of red and green vary a lot in different cultures. According to this helpful collection of Color Meanings By Culture, green is negatively associated in some Eastern cultures like China or Indonesia while red is associated to love, happiness and a long life.
In this post I’ll describe the process of bringing together the region shapes from the Natural Earth dataset with the regions provided in the GeoCityLite database. In the GeoCityLite db, the regions are referenced by a two-letter ID (FIPS10-4 for some countries, ISO3366-2 for others). Initially I thought that those IDs would be same as used in the Geonames admin-level 1 region db, which brought me to the first idea of mapping the regions via name similarity.
In this post I will explain the rendering process of the Piwik country maps. The results will basically look like this:
With this map I tried to visualize the global digital divide. It shows more than 80,000 populated places in blue and about 350,000 locations of IP addresses in red. White dots indicate places where many people live and many IP addresses are available.
The IP address locations are taken from the GeoLiteCity database by MaxMind. The database of populated places is taken from geonames.org. The visual style is largely inspired by Eric Fischer’s wonderful Flickr-vs-Twitter maps.
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.