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How to use smart city technology to measure social distancing

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How to use smart city technology to measure social distancing

Professor Phil James and Dr Ronnie Das discuss how urban data can help us understand the impact of social distancing measures on people and vehicle movement.

The UK and many other countries worldwide have introduced social distancing measures. The aim is to slow the spread of the COVID-19 pandemic. To understand if these are effective, we need to assess how far they are being followed.

To assist with this, the team at our Urban Observatory has developed an urban data dashboard. It shows the impact of social distancing measures on journeys within a city in real time.

Understanding the dynamics of movement

Newcastle University Urban Observatory aims to understand the dynamics of movement in a city.

It makes use of thousands of sensors and data sharing agreements. It tracks movement around the city. This includes:

  • traffic and pedestrian flow
  • congestion
  • car park occupancy
  • bus GPS trackers

It also monitors energy consumption, air quality, climate and many other variables.

A visual representation of data collected on social distancing.

The team has analysed more than 1.8 billion individual pieces of observational data. They've looked at this, as well as other data sources, with deep learning algorithms. These inform and update the dashboard in real time.

Pedestrian movement reduced by 95%

Research shows pedestrian movement has reduced by 95% compared to the annual average. This means people have been following government guidelines closely. The most profound decrease in footfall occurred after strict regulations introduced on March 23. This suggests that the stronger message had the desired effect.

With vehicle movement, traffic reduced at a much slower pace. It was to about 50% of the annual average early in the first week of lockdown. This is possibly due to people shifting to using cars rather than public transport. The team estimate there's been 612,000 lost journeys on public transport since March 1 in Tyne and Wear.

Public Health England has also suggested that people stay a minimum of two metres apart when out. This advice has been widely advertised. It is difficult to assess whether it is being followed. Using computer vision and image processing, the team has developed algorithms. They can automatically measure social distancing in public areas.

Traffic light system

The team produced models which can measure the distance between pedestrians in public. With a traffic light system, the algorithm anonymously identifies people who maintain safe distances. It can also flag instances in red where social distancing measures are violated. Using this information, it is possible to identify:

  • bottlenecks where social distancing cannot be maintained
  • how citizens adapt as restrictions are imposed or lifted

This type of data not only shows how physical distancing is changing in real time. It also provides detailed insight into long-term behavioural changes.

This analysis of the current situation presents an opportunity to be better prepared for the next crisis. It helps quantify the impacts of large-scale social change.

World Health Organization expert has claimed the UK was 10 days late in implementing strict social distancing. This was perhaps due to a lack of insight into widespread public behaviour. Observational infrastructure developed through technology may lie at the heart of future crisis management responses.

Real-time data on city systems

The Newcastle Urban Observatory is part of a global movement to develop "smart cities". This is where embedded sensors provide real-time data on city systems. They help optimise performance and enable evidence-based decision making.

This analysis of the current situation presents an opportunity to be better prepared for the next crisis. It helps quantify the impacts of large-scale social change.