This chunk of American Suburbia is named after London’s tube stations for no apparent reason

This wholesome American suburban home, which for no obvious reason is on Regents Park Circle. In America. Image: Google Maps

It turns out that pound isn’t the only sterling worth watching if you’re a worldly-minded Brit.

Sterling is a town in Virginia, in the US, about a 45-minute drive away from Washington, D.C (as measured from the White House, because why not).

And while it has several claims to fame – part of Washington Dulles International Airport sits within its boundaries; former US President James Buchanan had a summer house there; it used to be one of the many US towns that explicitly only allowed whites to live there pre-1960s – it has one quirk that is most interesting from a CityMetric point of view..

In one corner of the town, between the Walmart and the centre-of-town shopping mall, is a cluster of streets named after tube stationsAs in: the stations on the London Underground (and, indeed, Overground) network.

Click to expand. Literally, no joke. Image: Google Maps

You can turn right off Blossom Drive onto Waterloo Station Square, which has a pretty array of colourful, multi-storey, terraced houses, and then continue across the Cabin Branch brook to the intersection of Regents Park Circle and Paddington Station Terrace.

The charming houses of Waterloo Station Square. Image: Google Maps.

The left turn onto Regents Park Circle will then take you past Victoria Station Drive, Cheswick Park Court (as in, Chiswick Park, but they spelt it wrong), Ruislip Manor Way, Turnham Green Court, Ladbroke Grove Court and Tottenham Hale Court.

Regents Park Circle here, obviously. Image: Google Maps.

And down Paddington Station Terrace you can turn left onto Mornington Crescent Terrace, which connects up with Willesden Junction Terrace (I mean, really, of all the stations to choose), take a later left onto Brondesbury Park Terrace (hello, Overground, don’t quite know what you’re doing here).

This is Tottenham Hale Court. Of course it is. Image: Google Maps.

Or pull into the cul-de-sac of Wembley Central Terrace for a quick, quiet, in-car cry because honestly WHY are these roads named after tube stations.

I would say it’s some kind of train-themed neighbourhood – there’s Livingstone Station Street, Railway Terrace, Locomotive Terrace, Conductor Terrace, and Grand Central Square all in the area – but then there’s randomly an Indian Summer Terrace just across the way from Wembley Central Terrace.

Where Regents Park Circle meets Turnham Green Court. Image: Google Maps.

Seriously, though, this is about as weird as weird gets, and if anyone knows the answer to this one, please do let us know.


In the meantime, we’ll just sit here like ¯\_(ツ)_/¯ and see if there are any cheap flights to Washington going for a cheeky pilgrimage.

If you’re after something cheaper, though, there’s a small clump of streets in Amsterdam named after airports – namely Gatwickstraat (London Gatwick, obviously), La Guardiaweg (New York’s La Guardia), and Changiweg (Singapore Changi airport, god amongst airports).

What a world. 

(Hat tip: Oliver O'Brien of UCL.)

Jack May is a regular contributor to CityMetric and tweets as @JackO_May.

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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.