Every year Russian deputies publish their income declarations. In addition to earnings, MPs and their family members should declare real estate owned or rented (apartments, country houses, garages, and other objects). In Their total area An average Russian city could live on this amount for a whole year. For example, about the same is the average annual budget of Penza or Belgorod cities. MPs and their family members should declare real estate owned or rented ( in 2020 it was 43 km2. This is also comparable to the area of a small town near Moscow, for example, Sergiev Posad or
This publication came out a few months before the Russian parliamentary elections in September 2021 and became part of Novaya Gazeta’s coverage of the State Duma. We have collected the comprehensive dataset on the assets of Russian deputies, which allowed us to separately consider different types of real estate and land plots. It was the first publication that not only showed the enormous wealth of Russian MPs but offered a compelling data-driven story and a powerful visualization.
We collected and parsed income declarations of State Duma deputies since 2011. We had to combine both Python and Google Sheets functionality to properly clean this data and get correct calculations of all assets owned and used by deputies and their family members.
Using regular expressions, we divided and labeled all assets into 8 categories and calculated how the total area of objects of each type changed every year.
Then we developed interactive visualization which presents all the wealth of deputies on a cartoonish map of Deputatovka Luxury Village — the imaginary habitat of Russian officials where all lands and property are assembled together.
We created all elements of visualization from scratch using Adobe Illustrator and animated them with D3.js.
What was the hardest part of this project?
At the very beginning of the project, we assumed that getting and labeling the data would be the easiest part. We expected to use the open API of declarator.org, the project by Transparency International — Russia, focused on creating an open database of Russian officials’ incomes. But the structure and features of their database didn’t fit our needs. That’s why we had to use the source data/ Then we developed an algorithm for structuring and labeling information presented in natural language.
What can others learn from this project?
There is no necessary to present quite ordinary and at first sight boring data in a routine way. Data journalists can find a way not only to inform the readers but to entertain them.