Weak city centres have too many shops, and five other things we learned from the latest Centre for Cities report

Oh dear. Image: Getty.

The latest instalment of our series, in which we use the Centre for Cities’ data tools to crunch some of the numbers on Britain’s cities. 

The supply, type and quality of available commercial space is a key variable in a city’s economy success, probably.

I say “probably”, because we don’t actually know for sure: nobody has bothered to check what commercial property is available in different cities, and whether there is really any difference between those that are vibrant and those that are struggling.

Until now – because those pioneers at the Centre for Cities (CfC) have done it again. In its new Building Blocks report, the think tank has analysed the composition of commercial space in UK cities, and charted how it varies between weak and strong economies.

It can sometimes be a bit tough to work out which way causality runs here: just as the property available will influence a city’s economic performance, so its economy will influence the local property market. But with that caveat out there, here’s what we learned.

1. City centres look very different from their suburbs

Hey look, some charts!

Click to expand.

These pie charts show the breakdown of different type of commercial property in city centres and their suburbs across the UK.

Retail and, especially, offices dominate the city centres, making up a combined 76 per cent of all commercial property – nearly three times as big a share as the 27 per cent in the suburbs. With warehousing and industrial facilities, the picture is revered: 62 per cent in the suburbs, compared to just 13 per cent in the centres.

You can see this trend in individual cities, too. Here’s the same data but this time only for Leicester:

Click to expand.

The centre is 67 per cent office or retail, and 16 per cent industry or warehouse. For the suburbs, those numbers are 20 per cent and 71 per cent.

In short: the centres get the offices, and the suburbs get the warehouses. This is no huge surprise, but it’s always nice to put numbers on your hunch.

2. Economically successful city centres have more offices

To demonstrate this, we first need to define what success looks like. Drawing on earlier CfC research, the report defines its terms thus:

Strong city centres have a higher than average share of jobs in exporting firms, and a higher than average share of these exporting jobs are high-skilled.

2. Weak city centres have a lower than average share of jobs in exporting firms, and a lower than average share of these are high-skilled.

Helpfully enough, there’s a graph. Basically, if you want your city to be rich, you want to be top right.

Click to expand.

So, that behind us, how do strong and weak centres differ? Basically, like this:

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Strong city centres have a nearly three times as big a share of their commercial space dedicated to offices (62 per cent, compared to 23 per cent in weak centres). They also have a far smaller share of commercial space dedicated to retail (43 per cent, compared to 18 per cent).

Once again, you can see this in individual cities. Here’s Leeds compared to Doncaster:

Click to expand.

3. Economically successful city centres don’t just have more offices: they have better ones, too

The report uses energy efficiency ratings as a proxy for building quality – on the grounds that newer, or more recently refurbished, buildings will get higher ratings.

Here are those ratings plotted against the share of a space accounted for by offices. The light green dots – representing strong city centres – tend to do well on both.

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4. Weak city centres have too many shops

“Weak city centres dominated by retail do not have enough demand to sustain all these shops,” the report says, “which is why so many lie empty.”

Here’s a map of cities showing vacancy rates:

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With a few exceptions – booming Warrington has loads of empty space; Liverpool, which is often seen as struggling, has hardly any – this looks a lot like the map of city economic performance we all know and love.

5. Higher skilled suburbs are more  office-y and less warehouse-y than lower skilled ones

Click to expand.

Which is probably what you’d expect. (“Higher skilled” here means “more jobs in high-skilled sectors”.) But the differences are relatively minor: there’s less variation in suburbs than there is in city centres.

That said, there are very striking differences between the suburbs of individual cities. Here are York and Northampton:

Click to expand.

The share of Northampton given over to warehouses is 18 times that of York. Whoa.

6. Suburbs often have better offices, too

Last one, but it’s a strange one. Check out the quality of office space in different types of city and their suburbs:

Click to expand.

The weaker the city centre, the more likely it is to have poor quality offices – and the greater the gap with its suburbs.

This is strange, at first glance. But it probably reflects the difficulty of attracting property investment in certain cities – and perhaps also a tendency, by weaker cities, to invest in out of town office parks.


I’m going to stop there. But if you want to know more, you can download the full Building Blocks report here.

Jonn Elledge is the editor of CityMetric. He is on Twitter as @jonnelledge and on Facebook as JonnElledgeWrites

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Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

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