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 />
Upcoming SlideShare
Loading in...5
×

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

996

Published on

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

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
996
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
22
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • 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>
    1. A particular slide catching your eye?

      Clipping is a handy way to collect important slides you want to go back to later.

    ×