Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis

1. Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis Mark Birkin 6649386

2. Example: Urban Simulation • MoSeS Project • Can we project the population of a city forwards in time over a 25 year period? • technically & intellectually demanding • policy relevant • housing, transport, health care, education, … • Three components • Population reconstruction • Dynamic simulation • Activity and behaviourmodelling

3. Health and social care... 2006 2001 2031 2016

4. Health and Social Care… 2001 2031 Co-dependency LLTI 2031 2001

5. Health and Social Care… 2001 2031 Ethnicity 2001 2031 Multiple Deprivation

6. Moses Dynamic Model • Transition rates for fertility, mortality and migration are spatially disaggregated • E.g. fertility: rates by age, marital status and location • Event is simulated as a Monte Carlo process • Example: married woman, aged 28, living in Aireborough • Probability of maternity is 0.127 • Pull a probability from a distribution of random numbers; if <= 0.127 then the event occurs • All events in discrete intervals of one year

7. MoSeS Data Sources Census Small Area Statistics Special Migration Statistics Health Survey for England Household and Individual SARS International Passenger Statistics National Travel Survey ONS Vital Statistics BHPS General Household Survey Hospital Episode Statistics EASEL Housing Needs Study Google Maps

8. Moses Dynamic Model

9. Moses Dynamic Model

10. Moses Dynamic Model

11. Moses Dynamic Model

12. Moses Dynamic Model

13. Moses Dynamic Model

14. Moses Dynamic Model

15. Moses Dynamic Model

16. MoSeS Dynamic Model

17. Transport… Population and average speed changes in Leeds from 2001 to 2031

18. 2031 2001 Transport… 2015 Traffic Intensity * * Traffic Intensity=Traffic load/Road capacity

19. Scenario-based forecasting

20. Public Policy Source: MAPS2030

21. Simulation of Epidemics Ferguson et al, Nature, 2006

22. The El Farol Bar Problem • Everyone wants to go the bar • - unless it’s too crowded! • Must relax neoclassical economic assumptions (homogeneity of preferences, simultaneous decision-making) • Individual actors/ agent-based decision-making • - generic template for real markets • heterogeneous • out of equilibrium • (Arthur, 1994)

23. NeISS Architecture

24. NeISS Portal

25. NeISS Portal

27. Data Issues and Questions • Complexity • Visualisation • Integration • Proliferation • Generation

28. Complexity of data • Complexity, scale and volume of data inputs

29. Data visualisation

30. Data integration • Modelling and simulation as data integration • “Data diarrhoea, information constipation” • → data compression • → missing data

31. Proliferation of data domains • “customer science” • public/ private/ commercial • Crowd-sourced data

32. Data Generation • Example 1. (Silverburn) • 400 post sectors • 100 destinations • 6 ages • 4 ethnic groups • 4 social/ income groups • 2 car ownership • 516 inputs; 8 million model flows (sparse matrix!) Example 1. (Silverburn) • 400 post sectors • 100 destinations • 6 ages • 4 ethnic groups • 4 social/ income groups • 2 car ownership • 516 inputs; 8 million model flows (sparse matrix!) Example 2. (MoSeS) • 25 years of simulation • 60 million individuals • 200? characteristics • 20? scenarios Example 2. (MoSeS) • 25 years of simulation • 60 million individuals • 200? characteristics • 20? scenarios Example 3. (Epstein, 2009) • 8 billion agents! • Dynamic resolution at 10 minute intervals?!! Example 3. (Epstein, 2009) • 8 billion agents! • Dynamic resolution at 10 minute intervals?!!

33. Conclusion • Social simulation involves quite a lot of data intensive research!! • Note that quite a lot of social scientists have so far failed to appreciate this important fact!!!