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There are three key things to note in this: Looking between late-February and mid-March, we see that the drop-off in footfall happened earlier and was much sharper in London than the other cities. Looking between early-April and mid-June, we see that the small and the medium-sized cities experienced less of a decline than London and the other large cities, and they also started to recover from this earlier. Looking between mid-June (when non-essential retail reopened) and Saturday 4 July, we see that while the trajectory is upwards everywhere, the small and the medium-sized cities have seen a much sharper climb back up towards normal.

There are three things potentially playing into this: City centres of large cities tend to have less residential and industrial space and are often concentrations of office jobs, which have been and still are being done from home. This limits how many workers are in the city centre compared to before.  Larger cities, especially London are more reliant on public transport than their smaller counterparts. With public transport still limited in both capacity and use for public health reasons, it is now harder for people to travel into the centres of these cities. Due to their size, larger cities have more options for going to the pub or shopping beyond the centre and it may be that this has further reduced footfall in the city centre.

This and our other analysis on the topic suggests that we are unlikely to see large-scale changes in footfall and a ‘return to the normal’ in the city centres of the largest cities until office workers are welcome to and do return. 

Self-employed people in the North and Midlands are more likely to be in insecure, lower-paid roles at high risk from economic shocks

Cities in the Greater South East are more likely to be able to shift to working from home

The jobs that could be more easily done from home – such as consultants or finance – are concentrated in cities in the Greater South East (see the figure below). Assuming some sectors could completely shift to home working if necessary, up to one in two workers in London could shift to working from home. Meanwhile in Reading, Aldershot and Edinburgh over 40 per cent of workers could too.

The crisis more than ever demonstrated there is a very clear need for data in real time (or as near to real time) as possible to help inform decisions. It showed that problems-to-be-solved can’t be solved by the data one organisation holds alone: inevitably joining-up data from other sources is required. It also told us that without greater data collaboration our routes to creative, scalable solutions will remain limited.

The UK’s unusually fragmented approach to public sector data means often we talk more about how data is not shared or is not available than how it is more seamlessly used to understand common needs or meet shared objectives.

For example, City Hall is using aggregated data from Vodafone, O2 and Mastercard payments to add to our view of the observance of lockdown restrictions and add our understanding of the health of local economies.

A new map from the developer Meli Harvey shows exactly how narrow the sidewalks in New York are, with colors overlaid onto the city’s grid. (Harvey used data from New York City’s sidewalk dataset to construct their map.) The result is a stark validation of what most New Yorkers felt to be true: there’s just not enough space for most people to stay the recommended six feet apart.

The red colour of the QR code on Ms Sun’s “Hangzhou Health Code” app indicated that she was supposed to be undergoing 14 days of self-quarantine. Had the code been yellow, it would have meant she was a lower risk and had to isolate herself for seven days. For free passage around the city, people must produce their phones at checkpoints and show they have a green QR code. Pictured is another method of keeping tabs on people: drivers have to scan the code held up by a drone to register for entry into the city, in this case Shenzhen.

But those efforts involve only a single province. Creating such systems is far harder when it entails data-sharing between provinces, or between provincial and central authorities. Co-operation is undermined by competition for favour in Beijing. The boss of a foreign artificial-intelligence developer in China says that fusing datasets within a single firm is often quick, but not if it involves co-operation between different institutions. “The person in charge is unwilling to take the risk,” he says, and usually reckons that doing nothing is safer than sharing.

The coronavirus is killing black and Latino people in New York City at twice the rate that it is killing white people

In New York City, Latinos represent 34 percent of the people who have died of the coronavirus but make up 29 percent of the city’s population, according to preliminary data from the city’s Health Department. Black people represent 28 percent of deaths but make up 22 percent of the population.

In Chicago, for example, black people account for 72 percent of virus-related fatalities, even though they make up a little less than a third of the population.

He said he was taking this decision not just as a prime minister, but as our family elder. Who else can decide, without consulting the state governments that would have to deal with the fallout of this decision, that a nation of 1.38bn people should be locked down with zero preparation and with four hours’ notice? His methods definitely give the impression that India’s prime minister thinks of citizens as a hostile force that needs to be ambushed, taken by surprise, but never trusted.

The scene was biblical. Or perhaps not. The Bible could not have known numbers such as these. The lockdown to enforce physical distancing had resulted in the opposite — physical compression on an unthinkable scale. This is true even within India’s towns and cities. The main roads might be empty, but the poor are sealed into cramped quarters in slums and shanties.

Whatever it is, coronavirus has made the mighty kneel and brought the world to a halt like nothing else could. Our minds are still racing back and forth, longing for a return to “normality”, trying to stitch our future to our past and refusing to acknowledge the rupture. But the rupture exists. And in the midst of this terrible despair, it offers us a chance to rethink the doomsday machine we have built for ourselves. Nothing could be worse than a return to normality.

Positive Deviants are individuals, groups, cities, regions etc. who outperform their peers in a comparable context thanks to creative and highly adaptive solutions they have come up with.

“Agent-based models are particularly good in situations where you need to explicitly model the interactions and the behaviour of the individual components of a system,” says Nick Malleson, a professor of spatial science at the University of Leeds, who has worked with Improbable to study crime patterns. “I think the reason that they've become popular for [studying] disease spread is that very often in a disease spread, you might need to look at how people are interacting – when they come into contact in shops, how the social networks affect how people move, how they behave, how they interact, all these kinds of things.”

LGIM’s asset allocation team takes Apple users’ requests for travel directions and adjusts them for weekly seasonality before projecting the data onto estimates for gross domestic product. So far, their analysis shows that the U.S. economy is holding up better than other regions and is gradually reopening, while there are signs of improvement in southern Europe as countries like Italy relax their movement restrictions.

In addition to LGIM, Societe Generale SA and Deutsche Bank AG are among those tracking mobility data. SocGen quant strategists led by Andrew Lapthorne said in a note on Monday that the data has helped them see that despite the easing of lockdowns in major economies, activity continues to be weak.

Over at Deutsche Bank, strategists are using Google data to monitor any pick-up in activity in various New York communities.

Looking at the news across the UK, there are indications that the easing of lockdown restrictions has led to serious traffic problems. For instance, police were forced to close Falkirk Council’s Roughmute recycling centre two hours after opening it due to traffic building up on roads approaching the site. In Milton Keynes, IKEA was forced to close its car park just two hours after opening due to traffic volumes. Transport Scotland indicated a 60% increase in traffic on Saturday 30th May, compared to the previous Saturday, with traffic at the tourist and leisure hotspot of Loch Lomond up by 200%.

There are various ways to measure traffic volumes. Here, we look at Split Cycle Offset Optimisation Technique (SCOOT) data. The data is gathered from detectors installed at traffic lights. The purpose of the system is to coordinate traffic lights to improve the flow of vehicles. We accessed data on Glasgow’s traffic through an API provided by Glasgow City Council.

The aggregate pattern hides substantial variation at the different locations where the measurements are taken, which could explain why people may have seen large increases in traffic in their local area.

In a crisis situation like the one we are in, data can be an essential tool for crafting responses, allocating resources, measuring the effectiveness of interventions, such as social distancing, and telling us when we might reopen economies. However, incomplete or incorrect data can also muddy the waters, obscuring important nuances within communities, ignoring important factors such as socioeconomic realities, and creating false senses of panic or safety, not to mention other harms such as needlessly exposing private information.

Unfortunately, many of these technological solutions — however well intended — do not provide the clear picture they purport to. In many cases, there is insufficient engagement with subject-matter experts, such as epidemiologists who specialize in modeling the spread of infectious diseases or front-line clinicians who can help prioritize needs. But because technology and telecom companies have greater access to mobile device data, enormous financial resources, and larger teams of data scientists, than academic researchers do, their data products are being rolled out at a higher volume than high quality studies.

To some extent, all data risk breaching the privacy of individual or group identities, but publishing scorecards for specific neighborhoods risks shaming or punishing communities, while ignoring the socioeconomic realities of people’s lives that make it difficult for them to stay home.

Data companies are offering to mine troves of personal and public information to help local officials in the UK identify people who are struggling in the aftermath of the coronavirus crisis.

The aim is to move beyond assigning risk just based on an individual’s health and also include those who might be at greater risk of domestic violence, marital breakdown and financial difficulties, said Xantura’s chief executive Wajid Shafiq.

Xantura’s software runs the data against a set of risk factors and demographic data, as well as the NHS’s “shielded list” of individuals believed to be most at risk from Covid-19 complications, scoring households according to their risk profile.