Visualizing indices

From Wikipedia – “Numbeo is a crowd-sourced global database of reported consumer prices, perceived crime rates, quality of health care, other statistics.”

Last week released 2016’s indices and I have over the years spent more hours on that site than I care to admit. Mainly when daydreaming about other cities where to live, being an analyst I try to look at it objectively, at least that’s what I say to myself.

When I received the email announcing the release of new data I immediately thought how best to visualise it. After recently completing Tableau’s Desktop Qualification I’ve decided that I want to improve my skills using different charts and techniques. I know the data fits the chart and not the other way around, but the way I see it first you learn the tools only after one decides what tool is best for the job.

To get the data I used which I’ve mentioned before on the blog and it took me hardly any time at all to get the data I needed.

Quick tip: If the type of data you are using means that you have lots of CSV’s one per year for instance, there is a quick and easy way to append them all. See here: Merge csv files without any tool


Once I’ve imported the data and being #mappingmonth across Tableau’s community it seem right to look at it using the mapbox integration that came out with 9.2. The integration means that other options are available in terms of maps with nice layers like outdoors, satellites, or even one that looks like a treasure map from a pirate.

You can read more about it here. Tableau + Mapbox integration

Click image for the full interactive version.

In the end due to the aesthetics of the dashboard I thought best to stick to the default map from Tableau.

The map allows the user to quickly scan and see where Consumer Price Index is higher and filter per year. As expected Switzerland is one of the most expensive places on earth, followed by Norway which used to held the title in previous years. The gap between those two countries is now wider than ever before as you’ll see below.

DNA Charts

As mentioned above I’m now trying to learn other types of charts and I’ve recently seen lots of “DNA Charts”, they work well because the viewer can quickly compare two measures. For instance, we can quickly see that Singapore is the country where the Property Price to Income Ratio is higher (23.2) but in terms of CPI Singapore is beaten by two Nordic countries and Switzerland. Indeed Denmark is an expensive place for CPI, but it might be easier to buy there than in France which comes 3rd in Property Price to Income Ratio.

Click image for the full interactive version.

For instructions on how to put these charts together in Tableau see Andy Krieble’s video here DNA Charts

I’ve also added instructions that are displayed by hovering over the dark blue box and the ability to select which indices to compare for each year and the added bonus of limiting the number of countries to be looked at. I wanted the dashboard to be functional and to allow the user to look at the countries and indices they are interested in.


For the last chart I thought a box-plot would be the best way of looking at the data across the years.

Click image for the full interactive version.

Again the ability to choose the index remains and I’ve also added a map that shows the last year and serves two purposes.

  1. by clicking on a country it highlights it in the box plot so that the user can quickly see it’s performance.

  2. by clicking the user brings in a small line graph which is easier to read and selecting more than one country will allow for a quick comparison. The addition of a small like graph was something I saw Bridget Cogley adding to a dashboard and I thought it was great.

The same highlighting action works on the box-plot, if your geography is as bad as mine it’s good to have that visual reference there.

As I’ve mentioned above you can see that since Switzerland overtook Norway for the 1st place on CPI the gap has kept on increasing.

Click image for the full interactive version.

Hope you found it interesting and feel free to leave comments below.

Thanks for reading.


#data #datavisualisation #opendata #tableau