1. Discovering the world through Tableau maps
Let’s discover maps in Tableau exploring a fun dataset on the Eurovision Song contest - Europe’s number one entertainment show. In our dataset, we have quite some geographic fields, which we recognize by the presence of a little globe.
Let’s click on that globe.
Tableau chose the correct Geographical role for us, but it’s interesting to know what other options are!
When we drop Performing Country onto canvas, Tableau immediately plots the chart. Notice that Columns and Rows now plot Latitude and Longitude, which is auto-generated by Tableau.
Let’s have a look at the possible charts in Show me. Tableau has two types of map charts: symbol or point maps and filled maps.
The point on the map doesn’t present where the capital city is and merely acts as a symbol of the area.
Let’s change it to the filed map and drop Region onto the color.
These regions are provided in the dataset. How about introducing our own grouping? Let’s remove the region from the color and select the Scandinavian countries, Group them by right-clicking on it - we’ve just created our own custom map grouping!
Let’s also introduce a rough split between Northern, Western, and Eastern Europe.
Removing the individual countries from the Detail, we get a simplified map of Europe.
Let’s copy the country grouping to the Detail and visualize, for example, the average song's liveliness in color.
Wow, Western Europe seems to lead in terms of liveliness. But does it always translate to producing the winning songs? Let’s add another map right next to this one. To do so, we will duplicate the Longitude dimension in Columns and drop the measure Winner onto the color on this respective marks card.
Hmm... it seems like the Western European countries haven’t been winning much between 1998 and 2012, after all. The contest appears to have been somewhat dominated by Scandinavia and Eastern Europe.
Those who watch this show know that singing is only one part of the fun. Another fun part is the voting, where individual countries vote for songs from other countries. Each year this stirs rather heated discussions on the rivalry between European countries, so let’s see what we can find out by studying the Points from to data.
Let’s create a new point map, but this time we will use another way to do it: we will simply drop the latitude and longitude dimensions to the canvas.
We will drop the “Points from to” dimension to the detail and change the visualization to lines:
Hmm... that’s a lot of lines. Let’s highlight only one country at a time.
We will create a new parameter called “Points FROM country” of a string type and feed the names of all available performing countries from the value.
Let’s expose this parameter.
Next, we will filter the lines in this visualization by filtering on the “Points from to” dimension.
We will add the condition, using the StartsWith formula, that the first mentioned country in the Points from to field must equal the country input from the parameter.
Great!
Let’s still add the sum of total points to color and size.
Let’s test it out!
Looks like Sweden hands out a lot of points to neighboring countries!
The Swiss seem more neutral, spreading their points evenly around.
What a handy tool!
And we can even make this map cooler by changing its Style. Using the Maps and Background Layers menu, we can easily adapt it to, for example, a dark background.
Or to Outdoors style, removing, for example, the base.
Feel free to check out other cool options!
Are you ready to explore fun use cases using the maps yourself? Head on to the exercises.
2. Let's practice!