How deep learning algorithms can be used to measure social distancing | Syndication - TNW

This article is republished from The Conversation by Ronnie Das, Lecturer in Digital & Data Analytics, Newcastle University and Philip James, Professor of Urban Data, Newcastle University under a Creative Commons license. 
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Many countries have introduced social distancing measures to slow the spread of the COVID-19 pandemic by The Conversation. To understand if these recommendations are effective, we need to assess how far they are being followed.
 

Photo: Screenshot from Phil James Video

To assist with this, our team has developed an urban data dashboard to help understand the impact of social distancing measures on people and vehicle movement within a metropolitan city in real time.

The Newcastle University Urban Observatory was established to better understand the dynamics of movement in a city. It makes use of thousands of sensors and data sharing agreements to monitor movement around the city, from traffic and pedestrian flow to congestion, car park occupancy and bus GPS trackers. It also monitors energy consumption, air quality, climate and many other variables...

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

The Newcastle Urban Observatory is part of a global movement to develop what are known as smart cities: where embedded sensors provide real-time data on city systems to optimize performance and enable evidence-based decision making.
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Source: TNW