Smart cities need to be more human, so we’re creating Sims-style virtual worlds

The Sims 2 on show in 2005. Image: Getty.

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.

Difficult questions

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.

We know where – but why? Image: Worldoflard/Flickr/creative commons.

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.

The ConversationThere’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.

Nick Malleson, Associate Professor of Geographical Information Systems, University of Leeds and Alison Heppenstall, Professor in Geocomputation, University of Leeds.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Treating towns as bastions of Brexit ignores the reasons for the referendum result – and how to address them

Newcastle: not all cities are booming. Image: Getty.

The EU Referendum result has often been characterised as a revolt of Britain’s “left-behind” towns and rural areas against the “metropolitan elite”. But this view diverts attention from the underlying issues which drove the Brexit vote – and ironically has diverted policy attention away from addressing them too.

It’s true that a number of big urban authorities, led by London, voted to stay. And overall people living in cities were less likely to vote leave than towns. Setting aside Scottish cities and towns, which both voted very strongly for remain, Leave polled 51 per cent of the vote in English and Welsh cities, compared to 56 per cent in local authorities that include towns. (Consistent data isn’t available below local authority level.)

Yet there is a lot of variation underlying this average across towns. In Boston, 75 per cent voted Leave, and in Hartlepool and Grimsby it was 70 per cent. But at the other end of the scale, there were a number of towns that voted to stay. For example, Leave polled at 49 per cent in Horsham and Harrogate, and 46 per cent in Windsor and Hitchin. In places such as Winchester, Leamington Spa and Bath, the Leave voted amounted to less than 42 per cent of the vote.

What drives this variation across towns? Data from the Centre for Cities’ recent report Talk of the Town shows economic outcomes were the biggest factor – with towns that voted Remain also having stronger economies.

For a start, pro-Remain towns generally have smaller shares of people who were either unemployed or claiming long-term benefit. (This is based on 2011 data, the latest available.)

Towns which voted Remain also had a higher share of jobs in high-skilled exporting businesses – an indication of how successful they have been at attracting and retaining high-paid job opportunities.

And both measures will have been influenced by the skills of the residents in each town: the higher the share of residents with a degree, the stronger the Remain vote.

So the Brexit vote was reflective of the varying economic outcomes for people in different parts of the country. Places which have responded well to changes in the national economy voted to Remain in the EU, and those that have been ‘left behind’ – be they towns or cities – were more likely to have voted to Leave.

This sends a clear message to politicians about the need to improve the economic outcomes of the people that live in these towns and cities. But the irony is that the fallout from the Brexit has left no room for domestic policy, and little progress has been made on addressing the problem that, in part, is likely to have been responsible for the referendum outcome in the first place.

Indeed, politicians of all stripes have seemed more concerned about jostling for position within their parties, than setting out ideas for domestic policy agenda. Most worryingly, progress on devolution – a crucial way of giving areas a greater political voice – has stalled.


There was talk earlier this year of Theresa May relaunching her premiership next summer focusing on domestic policy. One of her biggest concerns should be that so many cities perform below the national average on a range of measures, and so do not make the contribution that they should to the national economy.

But addressing this problem wouldn’t ignore towns – quite the opposite. What Talk of the Town shows is that the underperformance of a number of cities is bad not just for their residents or the national economy, but also for the residents in surrounding towns too. A poorly performing neighbouring city limits both the job opportunities open to its residents and impacts on nearby towns’ ability to attract-in business investment and create higher paid jobs.

This isn’t the only factor – as the last chart above suggests, addressing poor skills should be central to any serious domestic policy agenda. But place has an influence on economic outcomes for people too, and policy needs recognise that different places play different roles. It also needs to reflect the importance of the relationships between places to improve the access that people across the country have to job opportunities and higher wages.

The Brexit vote didn’t result from a split between cities and towns. And if we are to address the reasons for it, we need to better understand the relationship between them, rather than seeing them as opposing entities.

Paul Swinney is head of policy & research at the Centre for Cities, on whose blog this article first appeared.

Read the Centre’s Talk of the Town report to find out more about the relationship between cities and towns, and what this means for policy.