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

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

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

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  • Macroscopic model: not sensitive to e.g. doubling in physical size of Leeds or dramatic counter-urbanisation? (maybe just needs a tweak for average trip length).
  • Transcript

    • 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?
      • 3. technically & intellectually demanding
      • 4. policy relevant
      • 5. housing, transport, health care, education, …
      • 6. Three components
      • 7. Population reconstruction
      • 8. Dynamic simulation
      • 9. Activity and behaviourmodelling
    • Health and social care...
      2006
      2001
      2031
      2016
    • 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
    • 28. 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)
      • 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
    • 33.
    • 34. Data Issues and Questions
    • Complexity of data
      Complexity, scale and volume of data inputs
    • 39. Data visualisation
    • 40. Data integration
      Modelling and simulation as data integration
      • “Data diarrhoea, information constipation”
      • 41. -> data compression
      • 42. -> missing data
    • Proliferation of data domains
      • “customer science”
      • 43. public/ private/ commercial
      • 44. Crowd-sourced data
    • Data Generation
      Example 1. (Silverburn)
      • 400 post sectors
      • 45. 100 destinations
      • 46. 6 ages
      • 47. 4 ethnic groups
      • 48. 4 social/ income groups
      • 49. 2 car ownership
      • 50. 516 inputs; 8 million model flows (sparse matrix!)
      Example 2. (MoSeS)
      • 25 years of simulation
      • 51. 60 million individuals
      • 52. 200? characteristics
      • 53. 20? scenarios
      Example 1. (Silverburn)
      • 400 post sectors
      • 54. 100 destinations
      • 55. 6 ages
      • 56. 4 ethnic groups
      • 57. 4 social/ income groups
      • 58. 2 car ownership
      • 59. 516 inputs; 8 million model flows (sparse matrix!)
      Example 2. (MoSeS)
      • 25 years of simulation
      • 60. 60 million individuals
      • 61. 200? characteristics
      • 62. 20? scenarios
      Example 3. (Epstein, 2009)
      • 8 billion agents!
      • 63. Dynamic resolution at 10 minute intervals?!!
      Example 3. (Epstein, 2009)
      • 8 billion agents!
      • 64. Dynamic resolution at 10 minute intervals?!!
    • Conclusion
      • Social simulation involves quite a lot of data intensive research!!
      • 65. Note that quite a lot of social scientists have so far failed to appreciate this important fact!!!

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