Our Innovator of the Month is a data scientist. Forget all the dusty clichés about Professor Calculus, Rand Hindi, founder of :Snips, is an urban algorithm architect. His watchword is: bring the data into play in order to adapt the city to the needs of citizens.
An innovator? Yes, and a highly precocious one at that. At just ten years of age, Rand Hindi was already coding. He explains:“One day, my mother stuck a book in my hand, sat me down in front of the computer and said: Here you are, learn about that; I think it’ll be important later on. And she was right. Thanks, Mum!”
Code, check. Next up was the entrepreneurial world. At the age when youngsters are generally just enjoying their first rave-ups, he and a friend set up his first company, Planetultra, a social network aimed at Paris high school students. PlanetUltra helped to organise parties and share the photos. That was 1999. Rand was then just fourteen. Then from web services development to experimenting on himself, for this man is also an early adopter of the Quantified Self. He went to England to study at University College, London, where he took a BSc in Computer Science, then a PhD in bioinformatics. “I realise with hindsight that I was in fact doing Big Data applied to biology,” he points out.
A disruptive idea? Carry out predictive modelling for cities. In concrete terms, Rand Hindi’s startup, :Snips, sets out to understand what is happening in a town, anticipate behaviours, and so turn the place into a really ‘smart’ city: watchword – “Don’t react, anticipate.” The :Snips team puts together existing data depending on the intended applications – sometimes it might be open data, but frequently data belonging to corporations or institutions, obtained, initially, in return for partnership stakes. This data is then translated into mathematical models, which in turn underpin the creation of an "impactful" product or service.
Rand cites as an example Tranquilien, developed for French national railway company SNCF. Inserted into an app, the algorithm precisely predicts passenger flows on Transilien line trains and RER express commuter trains in the Paris region, which enables a passenger to plan his/her itinerary for maximum on-board comfort, not necessarily on the basis of the actual travel time. The algorithm could lead to the creation of a whole series of prediction tools that could help to manage rail traffic. Explains Rand Hindi: “We could help them to take decisions around a major event, such as a Johnny Halliday concert at the Stade de France or a snowstorm, so that they can lay on more rolling stock or allocate extra resources.”
What’s so interesting about data? It’s data, yes, but it’s all information about the city. At one time Rand Hindi was especially interested in health-related data but at that moment he came up against a number of obstacles. “The timing wasn’t right. So we seized on a better opportunity and focused on the city.” The potential spinoffs from the model are virtually infinite. He identifies a number of different fields in which urban data can be usefully exploited: urban mobility, security, energy, waste management. Models have already been set up for transport (Tranquilien), parking management, and even crime. Energy is in the pipeline but still needs to be followed through.
So how does this affect us? Today the ‘Three Musketeers’ at :Snips have a number of modelling successes to their credit. For each new task they use the same basic model and take the same working approach: look at what happened in the past, understand the environment, then set up a mathematical model. And the citizen has a dual role to play in the model: an active role, by revealing what s/he’s doing; and a passive role by allowing the data scientists to harvest the data generated by his/her use of the apps set up. Stresses Rand Hindi: “We’re not just trying to convince people that urban data should be open, but that all data should be open and accessible, full stop. We want people to realise just how much can be done to make the world we live in a better place through smart use of the data.”
What are the future prospects? In the long term, Rand envisages a city usage system that automatically adapts to the habits and needs of the local population, explaining: “Today people who live in a city are regimented by the city. What we would like to see is the city adapting to its inhabitants. We’d like to turn the tables around.” Back in the present day, if you’re in the Paris region, you can check out the Tranquilien app, which has been up and running since 24 June.