Be Curious is an annual public engagement event held by the University of Leeds, to showcase the breadth of research at the University, through a variety of free activities for children and their families to enjoy. With this year’s theme, ‘Brighter Futures’ firmly in our minds, the first cohort of Leeds CDT students put our heads together to come up with an exciting concept for the LIDA stand, to represent our PhD research.
With more and more of us living in cities across the world, we themed our activities around the contribution of data to improving our daily lives and the environment we live in. And so, with the help of some wonderful designers, our smart cities play mat came to life!
On Saturday 30 March, Parkinson Court bustled with curious minds, keen to enjoy the feast of activities on display by research groups from across the University.
At the LIDA stand, children had a great time playing with toy cars as they explored the smart cities play mat; matching cards to the symbols on the mat to find all the different data sources around the city and learning how they could contribute towards building brighter futures. From hospitals to supermarkets, fitness trackers and public transport, we generate huge amounts of data every day. The smart cities concept gave us an opportunity to discuss how, when used responsibly, data can provide insights which could lead to improvements in health, public safety, diet and physical activity, efficiency of services, and so much more! It was really interesting to engage with people on the topic of ethical data usage and to understand their concerns, which we will carry forwards in our research conduct. We also gathered opinions via LIDA’s LifeInfo Survey, which explores attitudes towards the possible linkage of behavioural data with individual health records in the future.
As well as learning lots of fun data facts, members of the public also had the opportunity to contribute to some data visualisations. A sticker map showing transport modes and distance travelled to attend Be Curious was simple but effective, quickly revealing a pattern of car use among those living further from the University. While anonymous contributions to an interactive word cloud revealed that the benefits of data to society came across most strongly for health.
With our research spanning urban footfall, geodemographics, physical activity, online retail and diet, it was a challenge to pull such a diverse portfolio of projects together into a single concept. But the smart cities idea proved to be a great success and sparked a lot of interesting conversations at what was, for many of us, our first public engagement event.
Huge quantities of networked sensors have appeared in cities across the world in recent years. These include cameras and sensors that count the number of passers by, devices to sense air quality, traffic flow detectors, and even bee hive monitors. There are also large amounts of information about how people use cities on social media services such as Twitter and foursquare.
Citizens are even making their own sensors – often using smart phones – to monitor their environment and share the information with others; for example, crowd-sourced noise pollution maps are becoming popular. All this information can be used by city leaders to create policies, with the aim of making cities “smarter” and more sustainable.
But these data only tell half the story. While sensors can provide a rich picture of the physical city, they don’t tell us much about the social city: how people move around and use the spaces, what they think about their cities, why they prefer some areas over others, and so on. For instance, while sensors can collect data from travel cards to measure how many people travel into a city every day, they cannot reveal the purpose of their trip, or their experience of the city.
With a better understanding of both social and physical data, researchers could begin to answer tough questions about why some communities end up segregated, how areas become deprived, and where traffic congestion is likely to occur.
Determining how and why such patterns will emerge is extremely difficult. Traffic congestion happens as a result of personal decisions about how to get from A to B, based on factors such as your stage of life, your distance from the workplace, school or shops, your level of income, your knowledge of the roads and so on.
Congestion can build locally at pinch points, placing certain sections of the city’s transport networks under severe strain. This can lead to high levels of air pollution, which in turn has a severe impact on the health of the population. For city leaders, the big question is, which actions – imposing congestion charges, pedestrianising areas or improving local infrastructure – would lead to the biggest improvements in both congestion, and public health.
The irony is, although modern technology has the power to collect vast amounts of data, it doesn’t always provide the means to analyse it. This means that scientists don’t have the tools they need to understand how different factors influence the way cities function and grow. Here, the technique of agent-based modelling could come to the rescue.
The simulated city
Agent-based modelling is a type of computer simulation, which models the behaviour of individual people as they move around and interact inside a virtual world. An agent-based model of a city could include virtual commuters, pedestrians, taxi drivers, shoppers and so on. Each of these individuals has their own characteristics and “rules”, programmed by researchers, based on theories and data about how people behave.
After combining vast urban datasets with an agent-based model of people, scientists will have the capacity to tweak and re-run the model, until they detect the phenomena they’re wanting to study – whether it’s traffic jams or social segregation. When they eventually get the model right, they’ll be able to look back on the characteristics and rules of their virtual citizens, to better understand why some of these problems emerge, and hopefully begin to find ways to resolve them.
For example, scientists might use urban data in an agent-based model to better understand the characteristics of the people who contribute to traffic jams – where they have come from, why they are travelling, what other modes of transport they might be willing to take. From there, they might be able to identify some effective ways of encouraging people to take different routes or modes of transport.
Seeing the future
Also, if the model works well in the present time, then it might be able to produce short-term forecasts. This would allow scientists to develop ways of reacting to changes in cities, in real time. Using live urban data to simulate the city in real-time could help to inform the managers of key services during periods of major disruption, such as severe weather, infrastructure failure or evacuation.
Using real-time data adds another layer of complexity. But fortunately, other scientific disciplines have also been making advances in this area. Over decades, the field of meteorology has developed cutting-edge mathematical methods, which allow their weather and climate models to respond to new weather data, as they arise in real time.
There’s a lot more work to be done before these methods from meteorology can be adapted to work for agent-based models of cities. But if they’re successful, these advancements will allow scientists to build city simulations which are driven by people – and not just the data they produce.