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Modelling and Simulation for e-Social Science (MoSeS)

Modelling and Simulation for e-Social Science (MoSeS). Mark Birkin, Martin Clarke, Phil Rees, Andy Turner, Belinda Wu (School of Geography) Haibo Chen (Institute for Transport Studies) Justin Keen (Institute for Health Sciences) Pete Dew, Jie Xu, John Hodrien (School of Computing).

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Modelling and Simulation for e-Social Science (MoSeS)

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  1. Modelling and Simulation for e-Social Science (MoSeS) Mark Birkin, Martin Clarke, Phil Rees, Andy Turner, Belinda Wu (School of Geography) Haibo Chen (Institute for Transport Studies) Justin Keen (Institute for Health Sciences) Pete Dew, Jie Xu, John Hodrien (School of Computing)

  2. e-Science and Grid Computing • Computer Scientists are building the next generation of computational infrastructure • ‘[The Grid] intends to make access to computing power, scientific data repositories and experimental facilities as easy as the Web makes access to information.’ (Tony Blair, 2002) • Will provide the capability for virtual data sharing, easy and secure access to massive computational resources within collaborative working environments (‘virtual organisations’)

  3. e-Science and Grid Computing • Example applications • AstroGrid • MyGrid • … • UK Research Councils and DTI have invested at least £340 million since 2001 • How should social scientists be exploiting this technology?

  4. Use e-Science as a basis for renewed interest in urban simulation modelling: • ‘SimCity for Real’ • www.future-cities.org

  5. Some Objectives • Develop and package a suite of modelling tools that allow for specific research and policy questions to be addressed • Create a model of the UK population as individuals and households • Develop case study applications with specific reference to • Health • Business • Transport

  6. Modelling and Simulation Tools • Utilise UK National Grid Service • SRB • GROWL • Dynamic • Microsimulation • Multi Agent and Environment Based • Scenario Based Forecasting

  7. Health Scenario • Track health needs of one population cohort (elderly?) • Combination of primary and secondary health services plus social care • Lifetime histories poorly understood (modelling & data issues) (“joined up government”) • Substitution effects • Social networks dimension – Sports Clubs, Community Centres…

  8. Example Scenario • The Northern Way Business Plan aims to reduce the ‘output gap’ between the Region and the National average • too few in employment, • insufficient skill levels, • and an economy lacking in dynamism. • One important component of reducing the gap is through improving the quality and efficiency of the Region’s transportation infrastructure, roads, railways, ports and airports. Specifically, the Plan refers to (C6,C7,C8) objectives of: • Increasing to 17.2 million business and 6.4 million inbound leisure travellers each year through Northern Airports • Increase ship arrivals and throughput tonnes of Northern ports to 25% and 35% of the national total • Reduce congestion in the inter-urban strategic road network to below the national average by 2010

  9. MOSES MicroSimulation Model Aggregate Trip Distribution Model (SATURN? AIMSUN? PARAMICS?) Example Scenario • ‘Conventional’ approach: e.g. Birkin and M. Clarke (1987) Simmonds and G. Clarke (2005)

  10. MOSES MicroSimulation Model Agent-based model? Random utility theory? Example Scenario • Alternative approach: Examples?? PUMA?

  11. Challenges • Grid enabling data and tools • Visualisation • Google Earth • Computer Games • Collaboration • Retaining a problem focus • Design and Development

  12. Transport Applications • Explore environmental consequences of demographic change • Forecast traffic change • SATURN • Estimate vehicle emissions factors • AIMSUN, PARAMICS, DRACULA

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