Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis<br />Mark Birkin 6649386<br />
Example: Urban Simulation<br />MoSeS Project<br /><ul><li>Can we project the population of a city forwards in time over a ...
	technically & intellectually demanding
policy relevant
	housing, transport, health care, education, …
Three components
Population reconstruction
Dynamic simulation
Activity and behaviourmodelling</li></li></ul><li>Health and social care...<br />2006<br />2001<br />2031<br />2016<br />
Health and Social Care…<br />2001<br />2031<br />Co-dependency<br />LLTI<br />2031<br />2001<br />
Health and Social Care…<br />2001<br />2031<br />Ethnicity<br />2031<br />2001<br />Multiple<br />Deprivation<br />
Moses Dynamic Model<br />Transition rates for fertility, mortality and migration are spatially disaggregated<br />E.g. fer...
MoSeS Data Sources<br />Census Small Area Statistics<br />Special Migration Statistics<br />Health Survey for England<br /...
Moses Dynamic Model<br />
Moses Dynamic Model<br />
Moses Dynamic Model<br />
Moses Dynamic Model<br />
Moses Dynamic Model<br />
Moses Dynamic Model<br />
Moses Dynamic Model<br />
Moses Dynamic Model<br />
MoSeS Dynamic Model<br />
Transport…<br />Population and average speed changes in Leeds from 2001 to 2031<br />
2031<br />2001<br />Transport…<br />2015<br />Traffic Intensity *<br />* Traffic Intensity=Traffic load/Road capacity<br />
Scenario-based forecasting<br />
Public Policy<br />     Source: MAPS2030<br />
Simulation of Epidemics<br />Ferguson et al, Nature, 2006<br />
The El Farol Bar Problem<br /><ul><li>Everyone wants to go the bar </li></ul>		-	unless it’s too crowded!<br /><ul><li>Mus...
Individual actors/ agent-based decision-making</li></ul>		-	generic template for real markets<br />				heterogeneous<br />...
NeISS Architecture<br />
NeISS Portal<br />
NeISS Portal<br />
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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

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

    1. 1. Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis<br />Mark Birkin 6649386<br />
    2. 2. Example: Urban Simulation<br />MoSeS Project<br /><ul><li>Can we project the population of a city forwards in time over a 25 year period?
    3. 3. technically & intellectually demanding
    4. 4. policy relevant
    5. 5. housing, transport, health care, education, …
    6. 6. Three components
    7. 7. Population reconstruction
    8. 8. Dynamic simulation
    9. 9. Activity and behaviourmodelling</li></li></ul><li>Health and social care...<br />2006<br />2001<br />2031<br />2016<br />
    10. 10. Health and Social Care…<br />2001<br />2031<br />Co-dependency<br />LLTI<br />2031<br />2001<br />
    11. 11. Health and Social Care…<br />2001<br />2031<br />Ethnicity<br />2031<br />2001<br />Multiple<br />Deprivation<br />
    12. 12. Moses Dynamic Model<br />Transition rates for fertility, mortality and migration are spatially disaggregated<br />E.g. fertility: rates by age, marital status and location<br />Event is simulated as a Monte Carlo process<br />Example: married woman, aged 28, living in Aireborough<br />Probability of maternity is 0.127<br />Pull a probability from a distribution of random numbers; if <= 0.127 then the event occurs<br />All events in discrete intervals of one year<br />
    13. 13. MoSeS Data Sources<br />Census Small Area Statistics<br />Special Migration Statistics<br />Health Survey for England<br />Household and Individual SARS<br />International Passenger Statistics<br />National Travel Survey<br />ONS Vital Statistics<br />BHPS<br />General Household Survey<br />Hospital Episode Statistics<br />EASEL Housing Needs Study<br />Google Maps<br />
    14. 14. Moses Dynamic Model<br />
    15. 15. Moses Dynamic Model<br />
    16. 16. Moses Dynamic Model<br />
    17. 17. Moses Dynamic Model<br />
    18. 18. Moses Dynamic Model<br />
    19. 19. Moses Dynamic Model<br />
    20. 20. Moses Dynamic Model<br />
    21. 21. Moses Dynamic Model<br />
    22. 22. MoSeS Dynamic Model<br />
    23. 23. Transport…<br />Population and average speed changes in Leeds from 2001 to 2031<br />
    24. 24. 2031<br />2001<br />Transport…<br />2015<br />Traffic Intensity *<br />* Traffic Intensity=Traffic load/Road capacity<br />
    25. 25. Scenario-based forecasting<br />
    26. 26. Public Policy<br /> Source: MAPS2030<br />
    27. 27. Simulation of Epidemics<br />Ferguson et al, Nature, 2006<br />
    28. 28. The El Farol Bar Problem<br /><ul><li>Everyone wants to go the bar </li></ul> - unless it’s too crowded!<br /><ul><li>Must relax neoclassical economic assumptions (homogeneity of preferences, simultaneous decision-making)
    29. 29. Individual actors/ agent-based decision-making</li></ul> - generic template for real markets<br /> heterogeneous<br /> out of equilibrium<br /> (Arthur, 1994)<br />
    30. 30. NeISS Architecture<br />
    31. 31. NeISS Portal<br />
    32. 32. NeISS Portal<br />
    33. 33.
    34. 34. Data Issues and Questions<br /><ul><li>Complexity
    35. 35. Visualisation
    36. 36. Integration
    37. 37. Proliferation
    38. 38. Generation</li></li></ul><li>Complexity of data<br />Complexity, scale and volume of data inputs<br />
    39. 39. Data visualisation<br />
    40. 40. Data integration<br />Modelling and simulation as data integration<br /><ul><li>“Data diarrhoea, information constipation”
    41. 41. -> data compression
    42. 42. -> missing data</li></li></ul><li>Proliferation of data domains<br /><ul><li>“customer science”
    43. 43. public/ private/ commercial
    44. 44. Crowd-sourced data</li></li></ul><li>Data Generation<br />Example 1. (Silverburn)<br /><ul><li>400 post sectors
    45. 45. 100 destinations
    46. 46. 6 ages
    47. 47. 4 ethnic groups
    48. 48. 4 social/ income groups
    49. 49. 2 car ownership
    50. 50. 516 inputs; 8 million model flows (sparse matrix!)</li></ul>Example 2. (MoSeS)<br /><ul><li>25 years of simulation
    51. 51. 60 million individuals
    52. 52. 200? characteristics
    53. 53. 20? scenarios</li></ul>Example 1. (Silverburn)<br /><ul><li>400 post sectors
    54. 54. 100 destinations
    55. 55. 6 ages
    56. 56. 4 ethnic groups
    57. 57. 4 social/ income groups
    58. 58. 2 car ownership
    59. 59. 516 inputs; 8 million model flows (sparse matrix!)</li></ul>Example 2. (MoSeS)<br /><ul><li>25 years of simulation
    60. 60. 60 million individuals
    61. 61. 200? characteristics
    62. 62. 20? scenarios</li></ul>Example 3. (Epstein, 2009)<br /><ul><li>8 billion agents!
    63. 63. Dynamic resolution at 10 minute intervals?!!</li></ul>Example 3. (Epstein, 2009)<br /><ul><li>8 billion agents!
    64. 64. Dynamic resolution at 10 minute intervals?!!</li></li></ul><li>Conclusion<br /><ul><li>Social simulation involves quite a lot of data intensive research!!
    65. 65. Note that quite a lot of social scientists have so far failed to appreciate this important fact!!!</li>
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