Citizen oriented spatial visualization of EU subsidies

Citizen oriented spatial visualization of EU subsidies

Organisation: Nonprofit Ltd. (Hungary)

Publication Date: 04/10/2016

Size of team/newsroom:small


Our project is a visualization of the subsidies Hungary received from the European Union between 2007 and 2015, broken down into geogpraphical & municipal detail. We also provide self-referencial calculations for citizens to realize how much money was spent on their behalf. A simple visualization to determine if having a government-aligned mayor helps to secure more funding is also provided, and how funding changed with electoral cycles. According to our calculations, the country got roughly 297 billion Euros for economic and social development programmes, which translates to more than a 1 month net minimum income per person each year - including babies and pensioners. Hungary is one of the top beneficiaries of the EU subsidies and it is important to know how the money was spent. There are quite a few cases where the EU supported projects were never finished and we also know about projects where the European Comission’s Anti-Fraud Office, OLAF started an investigation into a corruption case based on Átlátszó’s reports. Albeit the winners of the EU subsidies were already public, to navigate through the government published database, however non-practical and cumbersome. Our main goal was to create a map which makes it easy to understand for the public and our readers how and where those subsidies were spent. The map also allows citizens to understand how much EU subsidy was spent by their municipality in their immediate vicinity, and to relate this fact to the actual effect this money had on their lives. In light of the anti-EU rethoric high on the public agenda in Hungary, it is also important to let people know how much financial support they have received from the EU over the past decade.

What makes this project innovative? What was its impact?

People have difficulty understanding huge numbers in subsidies, and can’t really relate to figures like 100 billion Euros, where their personal finances are in the 100s or 1000s of Euros. Navigating large databases is also counter-intuitive for most people. We’ve provided a very natural and easy to navigate map-based toolset, that allows people to geogpraphically look at their immediate vicinity, and see for themselves how much money was spent on their behalf. Our tool also brings the message home, providing per-citizen metrics for the amount of subsidy received - making it easy to understand how much they personally would have had to contribute for the same effect. At the same time, people can look up all EU projects in detail through our searchable database. We’ve also provided easy to undestand interfaces to understand into how subsidy flow is influenced by the political alignment of a municipality’s mayor with the government, and how subsidies differ between electoral cycles. Our tool also allows people to compare their municipality with others, especially highly ‘successful’ areas which are known to be associated with the prime minister. Such comparisons help people understand the nature of skewed chanelling of EU grants and see corruption as it happens.

Technologies used for this project:

The EU subsidy data as published by the Hungarian government has serious usability issues, and thus hinders Hungarian citizens to view and understand this data. The government agency responsible for pushlising this data refuses to provide it in any usable format. Thus we had to resort to scraping the data by using the ‘printable’ version of the state web site, and receive the data 100 rows at time. We have had to clean the data afterwards from publishing deficiencies and errors such as non-existent municipality names, duplications, etc. We’ve combined this processed EU subsidy data with other data sources, such as a municipalty register for number of inhabitants, geo-spatial databases for administrative boundaries, and also a mayoral database for name & party alignment for the mayor. We’ve created an easy to use, detailed search engine for all this data that also allows for simple aggregation and machine-processible export of the data. For the map-based visualization, the data was aggregated on three administrative levels: counties (19 units), subregions within counties (175 units) and settlements (3151 units). We extracted the data into GEOJSON files and visualized them with the help of the Leaflet.js library on top of an OpenStreetMap (OSM) tileset - ETL processes were dealt with in Python. Additional charts are presented with the help of the D3.js library, which use the same GEOJSON files as the map. Since we wanted to keep the project really simple and portable, there is no database supporting the visualization: everything is read up from files via AJAX adopting NOSQL data modeling. The application can also be embedded into articles as an interactive feature, and is mobile-ready. Data scraping: R (by Searchable structured database: Shiny based on R (by Frontend: Leaflet.js; D3.js; OpenStreetMap (OSM)
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