At the University of Manchester, we are proud to host the first 3D immersive
visualisation suite in Europe.
It's offers interactive 3D,
visualisation of data and virtual reality worlds, with laser sharp definition on a
cylindrical matrix of 72 full HD screens.
Since its inception in 2019,
we have built a number of platforms allowing us to explore societal and commercial challenges and their
solutions.
One such platform will present today, is the Greater Manchester Digital Twin Model.
A fully immersive, virtual reality model of Greater Manchester,
on which we can superimpose a variety of data sets depending on the purpose of our
experiments.
The foundation layer is automatically created from open street map and includes 3D
buildings and features in the environment,
such as rivers and motorways.
Flying over Manchester,
you can see the BBC studios on the banks of Salford Keys,
while further back,
we have Manchester Town Hall and the Cylindrical City library. We also have rich information
about local facilities, such a shops,
company offices and other buildings.
On top of this basic layer,
we can superimpose information about air quality, traffic,
local crime incidents, house prices,
deprivation data and similar items of researcher's interest.
One of our colleagues even found a relationship between house prices and the geometry of nearby streets.
We can also show COVID infection levels as heat maps,
at the high or medium resolution level. We also present COVID cases as three
dimensional bar charts,
which can show us the cumulative incidences of COVID-19 infections over a period of,
say, 40 weeks.
This enables decision makers, and the public to see how levels of COVID-19 infections have
changed across different parts of the city over time
and what the possible contagion paths are. Having a number of diverse
data sets such as multiple deprivation indices,
air pollution,
motorways,
schools and hospitals, mixed with COVID infection levels could,
for instance,
help city health chiefs gain a better understanding of why specific areas of the city might be
more prone to higher levels of infections than others and how to micromanage infection levels by
micro lock down strategies.
The heat map enables you to see where the infections are highest,
and you can also drill down to street levels if necessary, to discuss as a group of
specialists how different factors might be leading to the spread of infections, such as maybe
the presence of a taxi rank or a large secondary school in a specific area.
In theory,
we can create these maps for virtually every city in the world,
but our platform is not limited to city modelling.
We can experiment with the layouts of shelves in a supermarket,
simulate dangerous environments for training purposes,
or display multiple data sets in a single space,
allowing scientists and policy makers to interact with the data and discover new patterns and insights.