ESRC- Alan Turing FellowshipBringing the Social City to the Smart City

Connecting data,revealing hidden patterns

Using simulation to address societal challenges

Quantifying uncertainty and causality

ESRC- Alan Turing FellowshipBringing the Social City to the Smart City

Connecting data,revealing hidden patterns

Using simulation to address societal challenges

Quantifying uncertainty and causality

Integrating Causal Inference and ABM –Alison Heppenstall (Leeds)

Simulating cities with AI agents – Ed Manley (UCL)

Synthetic Population Estimation – Nik Lomax (Leeds)

Uncertainty in agent-based models - Nick Malleson (Leeds)

ESRC- Alan Turing FellowshipBringing the Social City to the Smart City

Connecting data,revealing hidden patterns

Using simulation to address societal challenges

Quantifying uncertainty and causality

Using Network Science and ML to reveal hidden patterns - Roger Beecham, Minh Le Kieu (Leeds) Ed Manley (UCL), Peter Triantifillio, Hakan F (Warwick)

DDA – Serge Guillias (UCL), Jeffery Salmond (Cambridge), Nick M (Leeds)

Quantifying Uncertainty in populations: Martin O’Reilly (Turing), James Geddes (Turing), Sebastien Voller (Turing), Nick & Nik

Digitial Twin 3D-4D visualization – Nick Holliman, Rachel Franklin (NCL)

Police demand modelling – Dirk (Leeds), S Johnston, K Bowers (UCL)

Spatial inequalities – Rachel Franklin KARMA – Susan Grant-MullerLocal migration, health and inequalities – Dianna Smith (Soton)

Integrating Causal Inference and ABM –Alison Heppenstall (Leeds)

Simulating cities with AI agents – Ed Manley (UCL)

Synthetic Population Estimation – Nik Lomax (Leeds)

Uncertainty in agent-based models - Nick Malleson (Leeds)

Identifying how and when patterns will emerge and become significant

Origin Destination

More info at http://www.roger-beecham.com

Agglomerative Clustering

𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 20

linkage = ward

Time

Predict based on clusterrather than individual

station

New York Bike Sharing (Citi bike scheme)

FruchtermanReingold

Next Steps

• Deeper investigation of NNs (and possibly other techniques)• Apply to 8 million Chinese taxi data (with Ed Manley)• Identifying spatial and temporal norms. Can we identify events ‘outside’ the

norms? Deeper understanding of cities and events.

• Turing PI Project: Integrating Causal Inference and Agent-based modelling