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N. Barr, J. Falkingham, H. Glennerster (1995)
Education funding, equity and the life cycle
D. Ballas, G. Clarke, I. Turton (2003)
A Spatial Microsimulation Model for Social Policy Evaluation
J. Veldhuisen, H. Timmermans, L. Kapoen (2000)
RAMBLAS: A Regional Planning Model Based on the Microsimulation of Daily Activity Travel PatternsEnvironment and Planning A, 32
D. Ballas, G. Clarke (2001)
Modelling the Local Impacts of National Social Policies: A Spatial Microsimulation ApproachEnvironment and Planning C: Government and Policy, 19
R. Mitchell, D. Dorling, M. Shaw (2000)
Inequalities in life and death: What if Britain were more equal?
P. Williamson, M. Birkin, P. Rees (1998)
The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised RecordsEnvironment and Planning A, 30
T. Nakaya (2001)
Local spatial interaction modelling based on the geographically weighted regression approachGeoJournal, 53
E. Holm, U. Lindgren, G. Malmberg, K. Mäkilä (1996)
Simulating an entire nation
D. Ballas, G. Clarke, D. Dorling, H. Eyre, Bethan Thomas, D. Rossiter (2005)
SimBritain: a spatial microsimulation approach to population dynamicsPopulation Space and Place, 11
M. Birkin, M. Clarke (1988)
Synthesis—A Synthetic Spatial Information System for Urban and Regional Analysis: Methods and ExamplesEnvironment and Planning A, 20
A. Fotheringham, T. Nakaya, K. Yano, S. Openshaw, Y. Ishikawa (2001)
Hierarchical Destination Choice and Spatial Interaction Modelling: A Simulation ExperimentEnvironment and Planning A, 33
Edgar Morgenroth (2002)
Evaluating Methods for Short to Medium Term County Population ForecastingJournal of the Statistical and Social Inquiry Society of Ireland
R. Mitchell, D. Dorling, M. Shaw (2002)
Population production and modelling mortality--an application of geographic information systems in health inequalities research.Health & place, 8 1
G. Orcutt, J. Merz, H. Quinke (1986)
Microanalytic simulation models to support social and financial policy
D. Wong (1992)
The Reliability of Using the Iterative Proportional Fitting ProcedureThe Professional Geographer, 44
A. King, Jeannie McLellan, R. Lloyd (2002)
Regional microsimulation for improved service delivery in Australia: Centrelink's CuSP model
S. Fienberg (1970)
An Iterative Procedure for Estimation in Contingency TablesAnnals of Mathematical Statistics, 41
S. Caldwell, G. Clarke, L. Keister (1998)
Modelling Regional Changes in US Household Income and Wealth: A Research AgendaEnvironment and Planning C: Government and Policy, 16
C. O’Donoghue (2001)
Dynamic Microsimulation: A Methodological Survey, 4
D. Ballas, G. Clarke, I. Turton (1999)
Exploring Microsimulation methodologies for the estimation of household attributes
E. Imhoff, W. Post (1998)
Microsimulation methods for population projection.Population. English selection, 10 1
C. Vencatasawmy, E. Holm, Terry Rephann, Johan Esko, N. Swan, Marianne Öhman, M. Åström, E. Alfredsson (1999)
Building a spatial microsimulation model
Peter Rogerson, D. Plane (1998)
The Dynamics of Neighborhood Age CompositionEnvironment and Planning A, 30
D. Ballas, G. Clarke (2000)
GIS and microsimulation for local labour market analysisComputers, Environment and Urban Systems, 24
D. Ballas, D. Rossiter, Bethan Thomas, G. Clarke, D. Dorling (2004)
Geography Matters: Simulating the Local Impacts of National Social Policies
J. Fitzgerald, Íde Kearney, Edgar Morgenroth, D. Smyth (1999)
National Investment Priorities For The Period 2000-2006Research Papers in Economics
Microsimulation describes economic and social events by modelling the behaviour of individual agents. These models have proved useful in evaluating the impact of policy changes at the micro‐level. Spatial microsimulation models contain geographical information and allow for a regional or local approach to policy analysis. This paper builds on previous work on urban systems by employing similar modelling techniques for the analysis of rural areas. It describes the development of the SMILE (Simulation Model for the Irish Local Economy) model. SMILE is a dynamic spatial microsimulation model designed to analyse the impact of policy change and economic development on rural areas in Ireland. At its core, SMILE is a model of population. It simulates the basic components of population change, fertility, mortality and internal migration, at a small area level. This paper describes the method for projecting population change at the sub‐county level. Results from the 1991 and 1996 dynamic model at county level are discussed, and a brief comparison is made with other methods. Finally, the features that distinguish microsimulation models from other population projection models are discussed. Copyright © 2005 John Wiley & Sons, Ltd.
Population, Space and Place – Wiley
Published: May 1, 2005
Keywords: ; ;
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