Slides available at: https://urban-analytics.github.io/dust/presentations.html
We need to better understand urban flows:
Crime – how many possible victims?
Pollution – who is being exposed? Where are the hotspots?
Economy – can we attract more people to our city centre?
Health - can we encourage more active travel?
Understanding and predicting the impact of short and long-term urban flows
Synthetic population estimation and scenario projection model
Creates dynamic and high resolution household populations
Customisable scenario projections
Set assumptions for the future (economic, health, policy)
Forecast the impacts of Brexit..!
Understanding urban flows
Smart (OD) and Social (media) data
Analysing data using different methods
Forecast the impacts of disruption
Use new information to better parameterise individual-based models
Yang Y, Heppenstall A, Turner A, Comber A. 2019. Who, Where, Why and When? Using Smart Card and Social Media Data to Understand Urban Mobility. ISPRS International Journal of Geo-Information. 8.6
Yang Y, Heppenstall A, Turner A, Comber A. 2019. A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile. Computers, Environment and Urban Systems. 77
Inputs (measurement noise)
Nonlinear models predict near future well, but diverge over time.
Used in meteorology and hydrology to constrain models closer to reality.
Try to improve estimates of the true system state by combining:
Noisy, real-world observations
Model estimates of the system state
Should be more accurate than data / observations in isolation.
Urban analytics @ established and growing
Work on uncertainty and probabilistic programming
New members of staff: Dr Jiaqi Ge and Prof Ed Manley
Large scale ABM, engagement with policy makers
Applied ML approaches: Reinforcement Learning