Last week the Eurovision Song Contest took place in Baku, Azerbadjan.

I suspect many countries vote for their neighbours. That is of course partly natural because they have the same kind of culture, but I wanted to take a closer look to see if I could find some kind of pattern and if someone benefits from it more than others. So this year I decided to do some spatial analysis and show the voting patterns on a map. From the scoreboard (http://www.eurovision.tv/page/history/by-year/contest?event=1593#Scoreboard) I could quickly recognise some values that differ from the others. For example the 12 and 10 points given to Lithuania, Bosnia & Herzegovina, Cyprus, Romania, Greece, Spain, Macedonia, Ireland, Ukraine and Moldova. But how could I make these visible on a map? This wasn’t as easy as I thought.  I started by making a dataset of the scoreboard. The result is quite confusing with a lot of lines pointing everywhere:

 Picture 1. All countries votes in the Grand Final.

Then I started experimenting with different colours on the lines, by points given or by country. Still the map looked confusing with all too much information.

Picture 2. All 12 and 10 points	Picture 3. 3 highest point values colored by country

Finally I ended up making different maps by each country.

Picture 4. All points for Sweden.	Picture 5. All points for Russia.

 Sweden and Russia (places 1 and 2) got points from all over Europe.

Picture 6. All points for Azerbadjan.	Picture 7. All points for Albania.

While Azerbadjan and Albania (places 4 and 5) got points mainly from countries close by.

I think these maps shows that if it’s a very good (or at least popular) song it will get lot of points independent of which country it is. Naturally it is easier to vote for a neighbouring country if they have a song good enough why countries with many neighbours benefit (but only little) from their location. With other words: neighbours do not solve the winner but they are good to have. These things had been taken into account when setting up the start lists for the semi-finals by setting neighbouring countries in different semi-final.

Maps have been made with esri ArcMap 10.0. Background map is from Natural Earth (www.naturalearthdata.com).