Project description:

The scale of the Indian election meant there was an immense amount of data being recorded by a variety of government sources. The graphics team scraped eight separate data sources programmatically. This included over 400,000 pdfs which were scoured using an algorithm to retrieve specific data points the team were interested in.

Data from these websites were then joined together using javascript, allowing us to discover new connections and make compelling visual projects such as this piece which was the first to actually show all candidates, but also visualised their criminal cases and wealth.

Impact reached:

The piece was a hit with Reuters clients but was also shared widely in India as well as elsewhere in the world.

Techniques/technologies used:

Lots and lots of javascript work to compile and merge the data. R was used to then analyse that master dataset.

The presentation itself is a combination of rendering in the browser using custom code and polishing in Adobe’s Creative Suite.

 

What was the hardest part of this project?

The key challenge for this piece was flawlessly matching two completely different data sets that were scraped so as not to put a wrong photograph or name against an incorrect number of criminal cases.

 

By joining these datasets together, we became to first news organisation to put every candidate’s face to a name along with their criminal cases and assets. The presentation of the project was a creative way to illustrate the scale of the election.

What can others learn from this project?

Preparing data well in advance can be a powerful weapon when preparing for a big event. Data scraping started months ahead of India’s election day.

 

Project links:

graphics.reuters.com/INDIA-ELECTION-CRIMINAL-CANDIDATES/0100925031T/index.html