For over two months, 50 teams representing 34 nationalities competed for a spot in the top ten of the World Data League (WDL) – a quest to find long-lasting solutions for social-oriented problems using data.
“For the past decade, many governmental entities, companies, and NGOs have been collecting data, and we know this data is the key to solving many social problems. We strongly believe that companies cannot solve all these challenges, and that’s why we created World Data League – an international competition that gathers brilliant minds in data science, AI and ML to find solutions for social-oriented problems collectively. This year’s topic was “Data-Driven Cities,” and it’s our contribution towards the 11th UN Sustainable Goal – Sustainable Cities and Communities”, as explained by the founders of WDL, Leonid Kholkine, Miguel José Monteiro, and Rui Mendes.
The journey started back in March this year. From more than 300 applications, 186 participants were accepted to join the League.
The League went on for four stages, with challenges on topics from Public Transportation to Environment. “Identifying road segments with potential safety hazards” or “Strategies to reduce the environmental and health impacts of pollution” were some of the problems solved by the teams. All the challenges and data were provided by municipalities like Valle de Aburrá (Colombia) and Porto (Portugal), private organizations such as PSE and OpenWeatherMaps, or research labs such as the Urban Co-Creation Data Lab.
The top ten teams were granted access to the WDL Finals during the first weekend of July to compete on a single challenge about Noise Pollution. For around 60 hours, participants from around the globe worked on “Improving the quality of life by reducing city noise levels”, a challenge provided by the City of Turin (Italy).
The international jury evaluated the submissions and selected three teams to pitch their solution to specialists on this topic: Stefaan Verhulst, from the GovLab at New York City University; Jan Potter, an acoustic specialist and chair of the Working Group Noise of Eurocities (a focus group on Noise Monitoring Cities); and Enrico Gallo, manager of the Environmental Department of the City of Turin and co-chair of the Working Group Noise of Eurocities.
Data Sloths, an international team formed by Alexander Obenauff (Germany), Carolina Bellani (Italy), Sonia Seyedallaei (Portugal), and Susan Wang (Australia), was the overall winner of the first edition of the World Data League. Second and third places went to two Portuguese teams: Tech Moguls and Children of Jupyter.
The winning solution consisted of a model that predicts noise and peaks at night, a study on the feasibility of predicting the complaints using noise level data, and a framework to integrate the model for decision-making and resource allocation. The team studied the data from the nightlife quarter of San Salvario and considered football events, public holidays, and the academic calendar to get to an hourly noise prediction. Besides measures similar to the already existent like “Restrict the selling of alcohol after certain hour” or “prohibiting outdoor consumption of alcohol/outdoor restaurant activity (after certain times)”, the solution included new measures such as “Noise measure controls by business owner”, and “Giving grants to the business owner to enable more inside space”. The team estimated that by following this approach, the noise reduction could be 5% to 15% (compared to the pre-COVID measures data), having a considerable impact on citizens’ quality of life, citizens’ compensations system, and public health system.
Adding to the competition, the League included many workshops, roundtables, and activities featuring numerous international guests.
All the solutions presented are open source and published for public access and use under an MIT license. The organization of WDL will publish a white paper by September this year with all the insights and knowledge acquired during the League. By doing this, WDL will fulfill its fundamental mission of using data for good and positively impacting society.
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