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Data science: More than a breath of fresh air
Data science: More than a breath of fresh air By Professor Kieran Arasaratnam, Professor-in-Practice and Co-Director of the Gandhi Centre for Inclusive Innovation at Imperial College Business School
There is a reason London is called ‘the Big Smoke’. Elderly Londoners will remember the ‘pea-soupers’ of the mid-20th century – thick layers of dirty fog that would occasionally engulf the city. London was very much the Beijing of its day. Much has improved since those days but the city still has problems.
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round 2 million Londoners – including 400,000 children – still live with illegal levels of air pollution. Under the law, hourly measurements of toxic nitrogen dioxide must not exceed 200 micrograms per cubic metre more than 18 times in a whole year. In London, toxic air has exceeded legal limits every year since 2010, killing an estimated 9,400 people on an annual basis. The pollution is so bad that the Mayor of London, Sadiq Khan, has called it a “public health emergency”. To counter the problem, the mayor has brought cleaner buses to routes through blackspots and introduced charges to deter dirty vehicles. This has helped bring pollution levels down, but it is no easy fix. Improving air quality requires a deeper understanding of vehicle emissions. Today’s air quality metrics, used by Transport for London and the Greater London Authority, are not real-time. Without granular data on traffic, the city’s air pollution may be underestimated. To tackle this problem,
one group of data scientists is using the pre-existing network of traffic cameras around London to classify transport and estimate vehicle count and velocity, moment to moment. This gives us a better picture of air pollution in the capital, which can be used to optimise traffic and alleviate emissions. This is one of the many projects being developed at the Gandhi Centre for Inclusive Innovation, at the Imperial College Business School, as part of the 12-week Data Science for Social Good (DSSG) Fellowship. The full-time summer fellowship is the result of collaboration with the University of Chicago. The programme brings together several disciplines in which Imperial College London excels: financial innovation, information technology, artificial intelligence and social impact. By bringing together the greatest minds – comprising undergraduates and recent graduates – from all over the world to work on machine learning, big data and data science projects, the fellowship is producing data scientists with the skill set needed to solve real-world problems.
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The right kind of solution The goal of the fellowship is threefold: to train aspiring data scientists who want to tackle social challenges, to encourage governments and non-profits to better use data to make better decisions, and to create a community of people and organisations that can work together to make a positive impact. As the title suggests, social good is at the heart of this work. The fellowship is about developing technology that doesn’t just provide commercial benefit but has massive implications for society and the environment. Our focus is to use ethical data science to extract actionable insight from big data that generates meaningful and positive outcomes. Advances in fields such as artificial intelligence and machine learning have the potential to transform society, but systemic imbalances still exist and must be addressed. The United Nations Sustainable Development Goals are a big part of this. That is why it is important that DSSG projects are developed within a fair and ethical framework, rooted in
Tip of the iceberg The work being done with the City of London to tackle air pollution really is just the tip of the iceberg. It is one of five projects selected from over 50 submissions working with global governments and NGOs. Other projects taking place under the auspices of DSSG are:
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The use of data science to expand a pre-emptive scheme to identify high-frequency 911 callers, improve healthcare and free up emergency services in the City of Memphis.
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A partnership with a Ugandabased not-for-profit group that offers legal aid to people with no access to lawyers, using questionnaire data to increase the capacity and efficiency.
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Improving heart health diagnoses from echocardiogram images using machine learning, in collaboration with the cardiology AI team at the University of Salamanca Hospital.
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Enabling data-driven recommendations for the Institute of Employment and Vocational Training in Portugal to connect job seekers with more relevant and effective jobs and interventions.
In London, toxic air has exceeded legal limits every year since 2010, killing an estimated 9,400 people on an annual basis.
Issue two / 2019–20
