“Who are the people that voluntarily want to work with our data?” That is a question many managers in both local…
Paris Open Data: taking a look at the Council of Paris
A tweet, praising the style of councilwoman Monique Brown, first got our attention. We headed straight to the city of Paris Open Data Portal, powered by OpenDataSoft, where a structured dataset was waiting for us. Au menu: a quirky infographic, a picture gallery worthy of « Guess who? » and colorful graphs.
— Awa (@AwaNdiaye_) February 23, 2015
Our drawn investigation
<div style="clear:both"><a href="https://www.opendatasoft.com/2015/03/10/paris-open-data-weekly-taking-a-look-at-the-council-of-paris/"><img src="http://www.opendatasoft.com/wp-content/uploads/sites/13/2015/03/opendataweekly_paris_council.png" title="Paris Open Data" alt="Infographic by OpenDataSoft about the council of paris in open data" border="0" /></a></div ><div style="text-align: center;>Courtesy of: <a href="https://opendatasoft.com/">OpenDataSoft</a></div>
Graphs that make sense
When we asked ourselves about the age differences between councillors, we immediately turned to our chart tool. In order to get more relevant results, we narrowed our analysis down to the 2008-2014 term of office. By clicking on the timeframe of interest, our platform automatically sorted the data to give us a beautiful graph.
As one can see, the youngest councillor and the oldest are 58 years apart. We can also realize that 1951 was quite rich in term of councillors.
Even better, by sorting the councillors by year of birth in the Table tab, pictures in the Gallery tab automatically synced up. Thus, in just a few clicks, the late Mr. Pierre-Christian Taittinger appeared on top of our list. He’s just below, on the top-left corner.
Of course, the gallery is sharable and embeddable on any website within a few clicks.
The extreme quality of Paris Open Data allowed us to extract the broad strokes of our investigation in a matter of minutes. Open Data truly are open when anyone can easily understand what they’re about. Their richness can be defined by their granularity, their completeness, their consistency and their actuality. The more a dataset is structured and enriched, the easier it can foster new ideas.
The top 3 most represented jobs are journalists, company directors and administrative officers.
How did we count bow ties?
With elbow grease and a never-ending passion for bow ties.
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