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).

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

  • Spatial Microsimulation for City Modelling, Social Forecasting and Urban Policy Analysis
    Mark Birkin 6649386
  • 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
  • Health and social care...
    2006
    2001
    2031
    2016
  • Health and Social Care…
    2001
    2031
    Co-dependency
    LLTI
    2031
    2001
  • Health and Social Care…
    2001
    2031
    Ethnicity
    2031
    2001
    Multiple
    Deprivation
  • 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
  • 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
  • Moses Dynamic Model
  • Moses Dynamic Model
  • Moses Dynamic Model
  • Moses Dynamic Model
  • Moses Dynamic Model
  • Moses Dynamic Model
  • Moses Dynamic Model
  • Moses Dynamic Model
  • MoSeS Dynamic Model
  • Transport…
    Population and average speed changes in Leeds from 2001 to 2031
  • 2031
    2001
    Transport…
    2015
    Traffic Intensity *
    * Traffic Intensity=Traffic load/Road capacity
  • Scenario-based forecasting
  • Public Policy
    Source: MAPS2030
  • Simulation of Epidemics
    Ferguson et al, Nature, 2006
  • 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)
  • NeISS Architecture
  • NeISS Portal
  • NeISS Portal
  • Data Issues and Questions
    • Complexity
    • Visualisation
    • Integration
    • Proliferation
    • Generation
  • Complexity of data
    Complexity, scale and volume of data inputs
  • Data visualisation
  • Data integration
    Modelling and simulation as data integration
    • “Data diarrhoea, information constipation”
    • -> data compression
    • -> missing data
  • Proliferation of data domains
    • “customer science”
    • public/ private/ commercial
    • Crowd-sourced data
  • 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 2. (MoSeS)
    • 25 years of simulation
    • 60 million individuals
    • 200? characteristics
    • 20? scenarios
    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 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?!!
  • 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!!!