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.
I just found the link to an infographic on Italian Poverty via @datavis. Before looking at the graphic I was interessted about how they pointed out the well known fact that in Italien the north is very rich compared to the south. So actually I expected a map or something, but the creator of the graphic decided to do it with some kind of spider chart. After looking at the graphic for a while, I noted that the creator made several mistakes and
in order to “enlighten” the visualization world with my knowledge I decided to publish my notes right here.
WELT ONLINE braucht wohl dringend jemanden, der ihnen Infografiken bastelt. Einen schlechteren “Überblick” über Zahlen als in diesem Artikel hat es seit Menschengedenken noch nicht gegeben.
Dabei wäre es so einfach gewesen. Fünf Minuten Arbeit reichen aus, um mit OpenOffice Calc ein nettes kleines Diagramm aus der Zahlenkolonne zu basteln..
Yesterday I read an interesting article in the “AI Journal“, which is the german news journal of amnesty international. It was about how many people in the world having internet access and how some governments and companies are censoring internet content. Thereby I saw this map:
Thanks to the Mercator projection, the European countries are about twice as large as there are in reality, Iceland appears as big as Spain and Alaska as big as Australia. As it’s impossible to map the surface of a sphere to a plane without distortions, every map projection has to deal with some kind of errors. But there is at least one mistake that cannot be tolerated. Can anybody see where Greenland has gone? I mean, it’s quite a large country so it’s not easy to forget. I think they simply left it out. Who cares about the 57,000 people living there? Who cares about all the people seeing this map in the journal? Who cares about visualization integrity? At least not the editorial staff from the German AI Journal.
So, I want to apologize to all people living in Greenland by completing this post with a map of Greenland in it’s real size and proportion. I know you are there!
Kürzlich stand ich bei einer Visualisierung vor folgendem Problem: Welche Textfarben kann man noch gut auf einem weißen Hintergrund erkennen und welche nicht? Ich erinnere mich noch eine brute-force-Lösung, die ich vor einigen Jahren bei einem ähnlichen Problem gewählt hatte: Nimm die Farbe und mische sie mit schwarz! Continue reading