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Dynamic microsimulation with spatial interactions

Dynamic microsimulation with spatial interactions. B.M.Wu, M.H.Birkin and P.H.Rees School of Geography University of Leeds. Outline. Introduction Modelling objectives Model description Initial analysis Model improvment Conclusion and future work. Introduction. Moses

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Dynamic microsimulation with spatial interactions

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  1. Dynamic microsimulationwith spatial interactions B.M.Wu, M.H.Birkin and P.H.Rees School of Geography University of Leeds ESRC Seminar, London, 02/04/09

  2. Outline • Introduction • Modelling objectives • Model description • Initial analysis • Model improvment • Conclusion and future work ESRC Seminar, London, 02/04/09

  3. Introduction • Moses • Modelling and Simulation of e-Social Science • Modelling objectives: • To develop a complete representation of the UK population at a fine spatial scale • To produce rich, detailed and robust forecasts of the future population of the UK • To investigate scenarios which relate demographics to service provision - emphasis on policy applications within the health and transport policy sectors ESRC Seminar, London, 02/04/09

  4. Some large scale MSMs • DYNASIM (Orcutt 1986) • CORSIM (Caldwell, 1998), DYNACAN (Morrison, 2003)and SVERIGE (Rephann, 1999) • APPSIM (Harding, 2007) • EUROMOD (Sutherland, 2007) ESRC Seminar, London, 02/04/09

  5. Modelling Description(1) • Dynamic representation of key demographic events /transactions in a geographically identified population • Macrosimulation and microsimulation models (MSM) are alternative ways of realising the processes (van Imhoff and Post, 1998) • We use a spatial MSM of the population and its dynamics, but the structure parallels the macro multi-state cohort-component (MSCC) projection model • An MSM depends on good data on the important transitions experienced by individuals • We experimented with an Agent Based Model(ABM) for a sub-population, students, where empirical data on migration has often proved problematic ESRC Seminar, London, 02/04/09

  6. Model Description (2) • Individual-based representations, forecasts and scenarios • What does this mean? • Leeds population:720,000+; UK: 60 million+ • Each individual has about 60 individual variables + 20 household variables + area variables • Various probabilities/rates eg: localised single year of age based mortality rates for Leeds • Distinctive behaviours from various population groups in different demographic processes • Interdependency of household and individual variables in different demographic processes ESRC Seminar, London, 02/04/09

  7. Demographic processes in the MSM • 6 modularised processes : • simple processes • multi-stage processes • Household formation and dissolution ESRC Seminar, London, 02/04/09

  8. Initial Results (1) An example of standard age-sex representations of Leeds population ESRC Seminar, London, 02/04/09

  9. Initial Results (2) ESRC Seminar, London, 02/04/09

  10. Improving Migration Model • We combine two approaches: • A person-specific “general” model, using probabilities of migration derived from the BHPS applied to “cloned” individuals in households derived from the 2001 Census SAR • Location specific information about migration intensities in small areas (2001 Census SMS), which are used to modify the results of the person-specific model • The model has a two stage procedure: • Migrant generation procedure • Migrant distribution procedure ESRC Seminar, London, 02/04/09

  11. Migrant generation procedure • Assess migration probabilities from an analysis of BHPS data, 2000-2004 for • a) households • b) groups • c) individuals • Major drivers of migration identified using a stepwise chi-squared estimation procedure • Households: age of head, household size, housing type • Individuals: age, household size, marital status • Groups: merged with individuals (small numbers) • National rates are locally adjusted by age using the Census Special Migration Statistics (SMS) ESRC Seminar, London, 02/04/09

  12. Migrant distribution procedure • The process is explored through a number of simplifying assumptions (later to be relaxed) • Net migration balance of zero between emigration from the city region and immigration to the city region • No new housing • No change in individual or household characteristics • Only considers complete household moves • Vacancy chain model of household migration ESRC Seminar, London, 02/04/09

  13. Migrant distribution procedure • The problem can be described as follows: • Estimate migration rates by location, age, household size and housing type: this process creates a stock of vacant housing • For each migrant, by location and household type (age, size) find a destination location by location and house type • Calibrate this process using data on known moves (by distance – from the census SMS) and known assignments of household type to house type (BHPS) ESRC Seminar, London, 02/04/09

  14. Simulation Database 1 Update Location and Dwelling Characteristics 5 Migrant generation model 2 2 Aggregate To Migrant Population Aggregate To Vacant Dwellings Migration distribution procedure (Birkin and Clarke 1987; Wu et al, 2008) Spatial Interaction Model 3 Compute dwelling preference for each migrant 4 ESRC Seminar, London, 02/04/09

  15. Migration Results ESRC Seminar, London, 02/04/09

  16. Characteristics of student migrants • Students are highly mobile during their studies in the universities • Mostly only move around the area close to the universities where they study, not in the suburban areas. • More importantly, most of them will leave the city once they finish their study, instead of settling down and growing old in the area • Due to the replenishment of the student population each year, the population of the wards in which university student stay tends to remain younger than that in other wards. ESRC Seminar, London, 02/04/09

  17. ABM • An alternative approach that models individuals as agents through their interactions with each other and the environment that they live in. • It is very flexible to introduce heterogeneous agents with distinctive behaviours through their built-in rules • It is useful in modelling features in the model where knowledge and theory is lacking (Billari et al. , 2002). ESRC Seminar, London, 02/04/09

  18. Student Migrants: experimenting with ABM • We recognise the following groups: • First year undergraduates • Other undergraduates • Master students • Doctoral students • We apply the following rules: • Each group is allowed set years to stay in the area • Students prefer to stay with their fellow students • Students stay close to their university of study, subject to housing availability • They don’t “do” marriage and fertility ESRC Seminar, London, 02/04/09

  19. Comparison of Results: Pure MSM Observed Predicted ESRC Seminar, London, 02/04/09

  20. Comparison of Results: MSM with ABM Observed Predicted ESRC Seminar, London, 02/04/09

  21. Comparison of Results: Observed, MSM and ABM Observed MSM ABM ESRC Seminar, London, 02/04/09

  22. Potential usage of the model Limiting long-term illness in Leeds 2031 ESRC Seminar, London, 02/04/09

  23. Conclusions and Future Work • We have built the foundations of an ambitious hybrid model which combines MSM, SIM and ABM features • Next steps: • Genesis (Generative e-Social Science) • One: Result alignment - towards validation - by matching the assumptions used in ONS projections • Two: Learn from the model and improve various sub-models according to the recent population trends etc. until satisfied “reality” is being reproduced. • Three: Explore the potential of usage of ABM in conjunction with MSM, eg: interaction between individuals/environment, individual behaviours, impact of personal history etc. ESRC Seminar, London, 02/04/09

  24. References • Billari, F., Ongaro, F., & Prskawetz, A. (2002). Agent-based computational demography: Using simulation to improve our understanding of demographic behaviour, in F. Billari & A. Prskawetz (Eds.), (pp. 1–18). London/Heidelberg: Springer/Physica. • Birkin M. and Clarke M. (1987) Comprehensive models and efficient accounting frameworks for urban and regional systems. In Griffith D., and Haining R. (Eds) Transformations through space and time, Martinus Nijhoff, The Hague, 169-195. • Caldwell, S.; Clarke, G. and Keister, L. (1998) Modelling regional changes in US household income and wealth: a research agenda. Environment and Planning C: Government and Policy 16: 707–722. • Champion T., Fotheringham S., Rees P., Bramley G. and others (2002) Development of a Migration Model. Office of the Deputy Prime Minister, London. Online at: http://www.odpm.gov.uk/stellent/groups/odpm_housing/documents/page/odpm_house_601865.pdf • Harding, A(2007)APPSIM: The Australian Dynamic Population and Policy Microsimulation Model, the 1st General Conference of the International Microsimulation Association, Vienna, Austria. • ... ESRC Seminar, London, 02/04/09

  25. Morrison, R.J. (2003) Making Pensions out of Nothing at All, The International microsimulation Conference on Population, Ageing and Health: Modelling our Future. • Orcutt, G., J. Merz and H. Quinke, eds. (1986) Microanalytic simulation models to support social and financial policy, North Holland: Amsterdam. • Rephann, T. J. (1999) The education module for SVERIGE: Documentation V 1.0, available at: http://www.equotient.net/papers/educate.pdf • Sutherland, Holly (2007) EUROMOD - the tax-benefit microsimulation model for the European Union, in Anil Gupta , Ann Harding Modelling our Future: population ageing health and aged care , Elsevier Science BV, chapter 10, 477-482, 2007 • van Imhoff E. and Post W. (1998) Microsimulation methods for population projection. Population: An English Selection, 10: 97-138. • Wu, B.M.; Birkin, M.H.and Rees, P.H. (2008)A spatial microsimulation model with student agents, Computers, Environment and Urban Systems 32, 440–453 ESRC Seminar, London, 02/04/09

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