"Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis. Presentation", Mark Birkin, March 2010

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

3. technically & intellectually demanding

4. policy relevant

5. housing, transport, health care, education, …

6. Three components

7. Population reconstruction

8. Dynamic simulation

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

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

12. 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

13. 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

14. Moses Dynamic Model

15. Moses Dynamic Model

16. Moses Dynamic Model

17. Moses Dynamic Model

18. Moses Dynamic Model

19. Moses Dynamic Model

20. Moses Dynamic Model

21. Moses Dynamic Model

22. MoSeS Dynamic Model

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

24. 2031 2001 Transport… 2015 Traffic Intensity * * Traffic Intensity=Traffic load/Road capacity

25. Scenario-based forecasting

26. Public Policy Source: MAPS2030

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

29. Individual actors/ agent-based decision-making - generic template for real markets heterogeneous out of equilibrium (Arthur, 1994)

30. NeISS Architecture

31. NeISS Portal

32. NeISS Portal

35. Visualisation

36. Integration

37. Proliferation

39. Data visualisation

41. -> data compression

43. public/ private/ commercial

45. 100 destinations

46. 6 ages

47. 4 ethnic groups

48. 4 social/ income groups

49. 2 car ownership

51. 60 million individuals

52. 200? characteristics

54. 100 destinations

55. 6 ages

56. 4 ethnic groups

57. 4 social/ income groups

58. 2 car ownership

60. 60 million individuals

61. 200? characteristics

65. Note that quite a lot of social scientists have so far failed to appreciate this important fact!!!