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Spatial Dynamics, Networks and Modelling

Edited by Aura Reggiani and Peter Nijkamp

This important new book provides a valuable set of studies on spatial dynamics, emerging networks and modelling efforts. It employs interdisciplinary concepts alongside innovative trajectories to highlight recent advances in analysing and modelling the spatial economy, transport networks, industrial dynamics and regional systems. It is argued that modelling network processes at different spatial scales provides critical information for the design of plans and policies. Furthermore, a key issue in the current complex and heterogeneous landscape is the adoption and validation of new approaches, models and methodologies, which are able to grasp the emergent aspects of economic uncertainty and discontinuity, as well as overcome the current difficulties of carrying out appropriate forecasts. In exploring diverse pathways for theoretical, methodological and empirical analysis, this exciting volume offers promising and evolutionary perspectives on the modern spatial network society.
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Chapter 14: Using a Spatial Microsimulation Model for the Estimation of the Geographical Impact of British National Government Policies

Dimitris Ballas, Graham Clarke and Danny Dorling


Dimitris Ballas, Graham Clarke, Danny Dorling and David Rossiter 14.1 INTRODUCTION This chapter reports progress on SimBritain, which is an ongoing research project that aims at simulating a detailed social survey of households in Britain. The SimBritain project is based on data from various sources to develop and validate a microsimulation model of the life of households in Britain from 1991 to 2021. Microsimulation can be defined as a methodology that is concerned with the creation of large-scale population microdata sets for the analysis of policy impacts at the micro level. In particular, microsimulation methods aim to examine changes in the life of individuals within households, and to analyse the impact of government policy changes for each individual and each household. Microsimulation methodologies have become accepted tools in the evaluation of economic and social policy and in the analysis of tax-benefit options and in other areas of public policy (Hancock and Sutherland 1992). Nevertheless, there are relatively few examples of spatial models that build on traditional economic microsimulation frameworks by adding a geographical dimension. Geographical microsimulation techniques involve the merging of census and survey data to simulate a population of individuals within households (for different geographical units), whose characteristics are as close to the real population as it is possible to estimate (Clarke 1996; Williamson et al. 1998; Ballas 2001). Dynamic microsimulation involves forecasting key socioeconomic variables into the future based either on current trends or the consequences of different policy scenarios. 367 368 Dynamics in Regional Systems One of...

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