ODI Covid-19 Project

Last Sunday, April 19, with the capital’s roads bereft of traffic and the sun high in the sky, 39,889 trips were taken on London-based Santander Cycles — or, as most of us still tend to call them, 'Boris Bikes’. That was the busiest day of the year for bike rentals so far, according to Transport for London. And this past weekend was almost as busy: 37,995 on Saturday 25th, and 38,756 on Sunday.

While tube usage is down 93 per cent and bus usage is down 74 per cent, Boris biking (which requires more manual contact than either of those transport methods) appears to be soaring. Our anecdotal evidence (at least) suggests that insufficient knowledge about how the disease can spread across surfaces could be an important driver of the higher leisure usage we are seeing.

In New York, demand for the city's bike-share programme in the last week of March was down by 71 per cent compared with the same week the year before, according to a group of software developers and data explorers working with data feeds from NYC's Bike Share system. In Paris, where there are strict rules on when and where you can exercise, usage of public Velib bikes has fallen by 75 per cent year on year. 

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.

Purpose should govern the choice of dataset to focus on Standards should be the primary guide to the design of the datasets User engagement should influence engagement activities ‘on top of’ published data to secure prioritised outcomes New user needs should feed into standard extension and development User engagement should shape the initiatives built on top of data

The call for ‘raw data now‘ was not without purpose: but it was about the purpose of particular groups of actors: not least semantic web reseachers looking for a large corpus of data to test their methods on. This call configured open data towards the needs and preferences of a particular set of (technical) actors, based on the theory that they would then act as intermediaries, creating a range of products and platforms that would serve the purpose of other groups. That theory hasn’t delivered in practice

They describe a process that started with a purpose (“get better bids on contract opportunities”), and then engaged with vendors to discuss and test out datasets that were useful to them.

This volume explores these questions and introduces the principles and practice of a quietly growing field: systems thinking. With roots in disciplines as varied as biology, cybernetics, and ecology, systems thinking provides a way of looking at how the world works that differs markedly from the traditional reductionistic, analytic view. Why is a systemic perspective an important complement to analytic thinking? One reason is that understanding how systems work – and how we play a role in them – lets us function more effectively and proactively within them. The more we understand systemic behavior, the more we can anticipate that behavior and work with systems (rather than being controlled by them) to shape the quality of our lives.

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.

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.

Local public health officials and medics have complained that the government has not supplied sufficiently detailed information on local infections, the lack of which they say hampers attempts to quash new outbreaks.

Councils have been promised postcode-level data for weeks from Public Health England and the newly created Joint Biosecurity Centre. But some public health directors are concerned the centre has not been sharing data about potential clusters of infections with councils, which could enforce school or workplace closures that could suppress an outbreak at an early stage.

“If the only data you’re getting is ‘in this population of 90,000 people there are 40 positives’ it’s like looking for a needle in a haystack. If Leicester had got the data sooner they could have had a fighting chance of managing it,” said one public health director, who asked not to be named.

Aggregated Location Data Analysis of aggregated location data can be used to identify hotspots of transmission and forecast future trends on transmission. This can help governments measure the efficacy of existing measures as well as guide government decision-making going forward, on subjects such as public-health interventions and where to allocate testing and medical resources. This kind of data will be particularly significant for governments as lockdowns are eased; it is essential that governments are able to gather real-time insights on the effectiveness of their interventions.

Medical experts currently believe that the virus is transmissible within 2 metres – meaning a person must come in contact within 2 metres of an infected person to have a chance of contracting it from social interactions. Therefore, effective digital contract tracing requires highly precise data. However, most extant technology was not designed to rapidly geolocate devices at that level of precision, meaning most location data is less precise than 2 metres. The ongoing challenge for technologists is to either adapt extant technology for a purpose for which it was not designed or build new solutions that can deliver the required level of precision.

ost notably, if the data used for generating location and mobility insights is weak (low precision and low accuracy), then the privacy implications may be less stark – but the value of the exercise also decreases. Both individual tracking and generating aggregated mobility insights based on weak location data can result in flawed insights. This can have a range of undesirable costs for both individuals and governments.