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Spatial Microsimulation Models: challenges of dealing with demographics at the small area level

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marriage and migration. competition between individuals for housing, jobs and marriage partners ... National databases such as the Sample of Anonymised Records. ... – PowerPoint PPT presentation

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Title: Spatial Microsimulation Models: challenges of dealing with demographics at the small area level


1
Spatial Microsimulation Models challenges of
dealing with demographics at the small area level
  • Mark Birkin and Martin Clarke

2
Microsimulation a potted history
  • Origins of microsimulation can be traced back to
    the work of Guy Orcutt (1959, 1962)
  • Principal characteristic representation of a
    (economic) system at the micro-level
    individual, household, firm.
  • Individuals are then processed in some
    probabilistic way (e.g. Monte Carlo simulation)
    or rules applied
  • and results aggregated to produce outputs
  • During the 1960s the technique was predominantly
    used by economists to examine impacts of policies
    e.g. Change in taxation or housing benefits

3
1970s
  • Orcutt moved to the Urban Institute and developed
    DYNASIM (Dynamic Simulation of Income) and TRIM
    (Transfer of Income Model)
  • Kain and Apgar (1977) Housing Model
  • These economic models had no spatial
    representation but did model household dynamics
  • Aided and abetted in the US by the availability
    of micro-data

4
Spatial Microsimulation
  • Often traced back to Hagerstrands paper What
    about people in Regional Science in 1970
  • However, microsimulation is not about people
    but a methodology for efficient representation of
    a complex system and an effective way of
    modelling change to this system
  • First reference to spatial microsimulation is
    Wilson and Pownall (1975) which started a rich
    tradition of microsimulation modelling at Leeds
    which continues to the present day

5
Model Applications
  • Economists finance, taxation, pensions, benefits
  • Leeds
  • Health Care
  • Education
  • Retail
  • Income/Housing

6
Different types of Microsimulation Models
  • Static Microsimulation used to look at impacts
    of policy changes e.g. taxation
  • - relatively straightforward in most cases
  • Dynamic Microsimulation modelling household
    dynamics.
  • - intrinsically difficult from a modelling
    perspective
  • - interdependence of events
  • e.g. labour market,
    housing market and migration
  • marriage and
    migration
  • competition
    between individuals for housing, jobs and
    marriage partners
  • Attempts made to link macro- and micro-models to
    model markets

7
Creating an initial population
  • In an ideal world we would access relevant real
    world micro-data with suitable specified
    attributes ………
  • In some cases it is possible to merge different
    micro-data sets to create a new combined one
  • In the 70s and 80s use of Iterative Proportional
    Fitting to create a synthetic population
    consistent with known aggregate distributions
  • More recently we have seen the use of genetic
    algorithms to create populations at the small
    area by sampling from National databases such as
    the Sample of Anonymised Records. Computationally
    very time consuming!

8
Computational Issues
  • Earliest attempts at spatial microsimulation were
    seriously compromised by lack of computing power.
    Sample data of say 5000 households were about the
    limits of what was possible for a 10 year dynamic
    model looking at household dynamics, housing and
    labour markets
  • Nowadays this is not so much a problem but as we
    shall see increasingly ambitious models pose
    their own computational problems

9
Changing Times
  • In the 1970s the household composition of the UK
    was relatively straightforward most people
    lived in nuclear households and typically lived
    near where they worked
  • In the 2000s household composition is much more
    complicated
  • - Houses of multiple
    occupation
  • - Married but living apart
  • - long distance commuting
  • - increased student population
  • - people in institutions e.g.
    care homes
  • These changes present significant challenges to
    both the specification of initial populations and
    modelling household dynamics

10
Spatial Representation a paradox
  • Models of 1970s and 80s would typically use
    census wards 33 in Leeds with a population of
    c25,000
  • - using Monte Carlo sampling
    provided fairly robust results at this level
  • 2000s use Output Areas 2400 in Leeds with a
    population of c300
  • - using Monte Carlo sampling
    does not provide robust results at this level

11
A Microsimulation Research Agenda
  • Development of methods/ optimisation
  • Dynamic modelling
  • Agent-based simulation
  • Applications
  • Validation

12
A Microsimulation Research Agenda
  • Development of methods/ optimisation
  • Dynamic modelling
  • Agent-based simulation
  • Applications
  • Validation

13
Methods…
Harland, Heppenstall, Birkin, Smith (2009)
14
Methods…
15
Dynamics…
2006
2001
2031
2016
16
Dynamics…
17
Agent-based models
18
Agents…
19
Agents…
20
Applications…
21
Validation…
Predicted

Observed
22
Conclusion
  • P. Williamson (1992) Community Health Care
    Policies for the Elderly A Microsimulation
    Approach.
  • J. Jin (2009) A Small Area Microsimulation Model
    for Water Demand.
  • J. Williams (1993) A simulation model of the
    diffusion of HIV and AIDS in the United Kingdom
  • C. Duley (1989) A model for updating
    census-based household and population information
    for inter-censal years
  • B. Wu (2010???) A hybrid geographical decision
    support system for strategic planning in UK
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