LIDA Showcase 2019
Simulating the City
Alison Heppenstall, Nick Malleson, Nik Lomax, Dan Birks, Minh Le Kieu, Yuanxuan Yang
University of Leeds and The Alan Turing Institute, UK
Slides available at: https://urban-analytics.github.io/dust/presentations.html
How many people are there in Trafalgar Square right now?
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?
City Simulation
Understanding and predicting the impact of short and long-term urban flows
SPENSER
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..!
Related Projects
Systems Science in Public Health and Health Economic Research (SIPHER)
Modelling health interventions
QUIPP:Quantifying Uncertainty and Individual Privacy Protection
Developing methods that track uncertainty and retain privacy in sensitive data sets
Bringing the Social to the Smart City
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
Problem: Models will Diverge
Uncertainty abounds
Inputs (measurement noise)
Parameter values
Model structure
Nonlinear models predict near future well, but diverge over time.
Possible Solution: Data Assimilation
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.
Related Projects
Data Assimilation for Agent-Based Models (DUST)
Create new methods for dynamically assimilating data into agent-based models.
Uncertainty in agent-based models for smart city forecasts
Developing methods that can be used to better understand uncertainty in individual-level models of cities
Finally
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
LIDA Showcase 2019
City Simulation
Alison Heppenstall, Nick Malleson, Nik Lomax, Dan Birks, Minh Le Kieu, Yuanxuan Yang
University of Leeds and The Alan Turing Institute, UK
These slides: https://urban-analytics.github.io/dust/presentations.html