How is an ageing population changing urban Britain?

Pensioners in Brighton. Image: Getty.

British cities have grey weather, grey concrete, and increasingly, grey heads. People across the country are living longer, and this means the population of our cities are living longer too.

On average, most cities are younger than the rest of the country and will remain so. But a few cities are older than the rest of the country and are ageing fast – with big consequences for policy makers.

Ageing will change society’s needs, and will require more resources to be spent on pensions, healthcare, and social care. And this will have larger implications for some cities than others. The Centre for Cities’ Cities Outlook 2018 report offers some insight into population changes in UK cities over recent years.

But to get a better picture of how ageing will affect different places, it’s necessary to dig deeper into the data and to look at the share of the population in each city aged over 65 (notwithstanding the fact that some people work beyond retirement age).

Click to expand.

Currently 18 per cent of the UK’s population are aged 65 or over, compared to 15 per cent in UK cities. Some of these cities are especially young: 12 per cent of the population of Luton are over 65, while in Slough it is only 10 per cent. Interestingly the youngest cities tend to be southern inland cities (as shown by the smaller bubbles on the map).

But this isn’t the case everywhere. In total there are 19 cities which are older than the UK average. The oldest of these include Blackpool, Bournemouth, Worthing, Southend, Birkenhead, and Swansea – all smaller coastal cities.

Cities have also been ageing at different speeds over the last decade

Seven cities, such as Crawley, Brighton, Coventry, and Dundee, have surprisingly seen a decline in the share of people aged 65+ even as the country has aged. This has mainly been driven by large increases in those in younger age groups.

However, in the other 56 cities across the UK the share of people aged 65+ has increased, and in 22 cities this demographic has grown by two or more percentage points. This was led by Wigan (see the table below), where the share increased from 15 percent to 18.8 percent.

Click to expand. Source: NOMIS, Mid-year population estimates.

Of the cities that have seen the largest increase, there are two main trends. The first is the presence of a number of new towns in this group, such as Telford, Milton Keynes, Warrington and Basildon. Ageing in these places reflects in part a number of original movers to the new towns turning 65, and means they are now likely to be dealing with greater demand for adult social care than in the past.

The second is that in many places (which includes some new towns), the rise in the share of those aged 65+ was not only the result of an increase in the number of older people, but also because of a fall in the population aged 16-49. These are the largest, darkest bubbles on the map. This may reflect an underlying weakness in their economies, as younger people move elsewhere for job opportunities.


Policy implications

All cities will face greater funding demands as pressure on social care increases from a combination of an ageing population and budget cuts (as the infamous “Graph of Doom” shows). And the data above shows that this will be particularly acute in certain places.

In the short term the government has announced stop gap funding of £150m to spend on social care. But this doesn’t address the longer term growing pressures on services. Allowing local authorities to keep a greater share of their business rates is a potential longer term response, but will be more effective in places with stronger economies (such as Milton Keynes) than those with weaker ones (such as Wigan and Southend). If – as suggested above – weaker economies do experience faster ageing, the current system risks creating and widening inequalities between places in the quality of care.

Ultimately, government reform of social care is required to balance the funding demands of an ageing population between the taxpayer and wealthier pensioners. The politics of the situation make this difficult, of course. Indeed, the most recent proposal to reform social care lasted only four days, before being dropped by the Prime Minister Theresa May after being branded “the dementia tax”.

There has been little movement on this since, and the Chancellor Philip Hammond did not even mention social care once in the November budget. However, it is an issue which will only grow in urgency in over the coming years. And an answer will be needed if we are to avoid further Northamptonshire-style local authority financial calamities in the future.

Anthony Breach is an economic analyst at the Centre for Cities, on whose blog this post first appeared. 

 
 
 
 

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.