Dossier: Slot Machine Gambling

Dossier: Slot Machine Gambling

Organisation: DOSSIER (Austria)

Publication Date: 04/09/2015

Applicant(s)

Description

DOSSIER: Slot machine gambling Dossier: Slot machine gambling is a data-driven journalistic investigation into slot machine gambling in Vienna. Using Web-scraping DOSSIER derived data about licenses for the operation of slot machines in the federal capital of Austria; information on the licenses’ validity, the holders of the licenses and the locations of the slot machines. DOSSIER collected the locations of slot machines in Vienna and marked them on an interactive map. Using “R”, a software environment for statistical computing, DOSSIER linked the locations of slot machines with socioeconomic data and found a negative correlation between the number of slot machines in a district and the average income of the district's residents. In simpler terms: the lower the average income in a district, the higher the density of slot machines. Consequently, DOSSIER could prove a theory that hitherto had not been verified: slot machine gambling equals a form of redistribution from the button to the top. DOSSIERs findings have been quoted in Austria’s and in international media. A subunit of a political party fighting slot machine gambling in Vienna used DOSSIERs data (which runs under Creative Commons and is free to use) to create an interactive map where citizens can enter illegally operated slot machines. Even the Austrian financial police, in charge of fighting illegal slot machine gambling, used DOSSIERs data to cross-reference its data. Background information Reliable data on slot machine gambling have played a minor role in public discussions so far. This is partly due to the industry itself. Discretion is one of the gambling industry’s top priorities. Scientific studies on the Viennese gambling market or on gambling addiction in Austria in general are rare. In addition, authorities and state officials treat the matter with strict confidentiality. Information about locations of slot machines, the awarded licenses to operate them and their validity are kept under tight wraps. DOSSIER wanted to know from the appropriate authorities and political leaders where slot machines are located and who is running them. Official answers were short: A list of all gaming machines would not exist – and even if there would be one, it’d be subject to data protection. So DOSSIER had to find a workaround. After intensive studies of the law, the industrial code and the professional register, DOSSIER found all the necessary information to realize the investigation. For years there have been ongoing discussions whether to ban slot machine gambling in Vienna due to concerns of rising gambling addiction in the capital. DOSSIER has taken these discussions as a starting point to examine slot machine gambling in Vienna. DOSSIERs investigation has coincided with a newly passed law on November 27th, 2014: Since January 1st, 2015 slot machine gambling is prohibited outside of licensed casinos in Vienna. At this stage DOSSIER had already scrapped the locations and licenses that entitle the holder to operate slot machines. On October 19, 2014, there were 2,578 machines at 925 locations in Vienna. DOSSIER first published its findings on December 9th, 2014 and continues to research gambling in Austria.

Technologies used for this project:

Initially, Web-Scraping was performed with a self-written python program and additional open source software. The data were then revised with Open Refine and Excel. The statistical analysis was performed with "R" - an open-source program for statistical analysis. The map is built with html, css and javascript. The map-layer is based on a customized mapbox map emphasizing only relevant map information. The scraped data was converted to the geojson file format (http://converter.mygeodata.eu/) and linked to the geo-data using cocoa json editor. There are two types of objects: nodes representing locations and shapes representing districts. Both have various properties such as type of location and normalized data representing the relative number of e.g. unemployed people. A combination of leaflet.js and mapbox was used to project the data onto the map.
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