Synthetic data, microsimulation and socio-demographic forecasting Mark Birkin School of Geography Professor of Spatial Analysis and Policy.

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Presentation transcript:

Synthetic data, microsimulation and socio-demographic forecasting Mark Birkin School of Geography Professor of Spatial Analysis and Policy

Introduction Value of synthetic approaches – Population reconstruction – estimation of individual profiles and relationships for small areas – Synthetic linkage – Comparative statics and what-if analysis – Dynamics, projection and forecasting – Platform for agent-based simulation, evolutionary modelling and environmental interactions

The CDRC Vision Unlock the research / commercial potential of consumer data Render consumer data safe to use Stimulate high impact research projects Collaborative and integrated approach to training and capacity building Create conditions for the preservation of legacy retail data The Consumer Data Research Centre

Method Probabilistic join Constraint tables Sample Population Synthetic Population Population created using spatial microsimulation Agent-Based Modelling toolkit for visualisation and behavioural modelling Population 24/7 output destination constraints Origin population constraints Method

Demand Forecasting

Mixed Tenure Housing

Source: Jordan R, Birkin M, Evans A (2014) An Agent-based Model of Residential Mobility: Assessing the Impacts of Urban Regeneration Policy in the EASEL District, Computers, Environment and Urban Systems, forthcoming.

Mixed Tenure Housing

 x

Daily mobility

Daily mobility

Daily mobility...

Daily mobility

Daily Mobility

Daily mobility

Wide variety of applications Supports analysis, simulation, projection and policy appraisal Micro-data of increasing quality and availability, but still constrained by restrictions on linkage and re-use for commercial, public policy and academic purposes Reference: Harland K., Birkin M. (2015) Dynamic Microsimulation in Brunsdon C., Singleton A. (Eds) Geocomputation: A Practical Primer. Conclusions

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