Visualizing Olympic medals data in a creative way

The olympic games are one of the most glorious sports events. Every country can show its best athletes in different categories of sports. But, who of all the countries is the really the best? The goal of this visualization is to visualize the change over time of the 10 top countries having the most medals. In order to create a cool and interesting visualization, we have to do some steps before reaching this goal.

visualizing procedure

Step 1: A quick exploration of all the tools out there:

There are a big number of tools that can help you visualize datasets. However, what tool fits you? Tools like Tableau have an easy to use interface to make interactive and interesting visualizations.

For the more advanced geeks who like to program tools like D3  or Kendo UI can be considered. For Python, there are also a ton of interesting libraries out there, just do a quick google search. Changing, and parsing data in Python is nice because of the data structures in the Python language.

Step 2: Making sure the data is in the right form  

If you have picked and you have picked your type of visualization, it is important to ensure that the data is in the right form so that the tool can interpret the data properly. Sometimes this can require some manual work, so be patient

Step 3: Visualize

This requires some work. Especially if you are not using a UI based tool the process of visualizing can be a bit hard. But, as always, hard work pays off.

Step 3: Iterate

I think this is the most important step to make an interesting visualization. Ask people you know for feedback. Can people understand your visualization? Is it explanatory? Do the colors fit? For a visualization to be a good focus on all types of users, young people, older people, color blind people. Be critical, does it really visualize the data in a nice way?  Does the visualization tell the story?

 
Result :

 

The result is an interesting colorful visualization. For this visualization, in particular, the following tools were used: rawgraphs.io, Adobe Illustrator. 

First, of course, picking the right tool, parsing the data in the right form. Generating the graph in rawgraph.io. Raw grapes generate a linear graph and in Illustrator the after work was performed.  If you are interested in buying the visualization or having your own visualization contact: j.konijn@student.utwente.nl

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