Spatial Microsimulation: A Reference Guide for Users

Academics and policy makers who recognize the vital importance of place in their researches now have this book to refer to. Along with instructions on preparing data, it details a number of methods for both static and dynamic spatial microsimulation modeling.

This book is a practical guide on how to design, create and validate a spatial microsimulation model. These models are becoming more popular as academics and policy makers recognise the value of place in research and policy making. Recent spatial microsimulation models have been used to analyse health and social disadvantage for small areas; and to look at the effect of policy change for small areas. This provides a powerful analysis tool for researchers and policy makers. This book covers preparing the data for spatial microsimulation; a number of methods for both static and dynamic spatial microsimulation models; validation of the models to ensure the outputs are reasonable; and the future of spatial microsimulation. The book will be an essential handbook for any researcher or policy maker looking to design and create a spatial microsimulation model. This book will also be useful to those policy makers who are commissioning a spatial microsimulation model, or looking to commission work using a spatial microsimulation model, as it provides information on the different methods in a non-technical way.

Brings together all the techniques and methods for spatial microsimulation in one book Contains new material on data requirements and preparing data for spatial microsimulation Describes the whole process of creating a spatial microsimulation model, from data preparation/cleaning to validation

Contenu

Part 1: Background: Chapter 1: Introduction to spatial microsimulation History, Methods and Applications: Robert Tanton and Kimberley Edwards .- Chapter 2: Building a static spatial microsimulation model: data preparation: Rebecca Cassells, Riyana Miranti and Ann Harding.- Part 2: Static spatial microsimulation models.- Chapter 3: An Evaluation of Two Synthetic Small-Area Microdata simulation methodologies: Synthetic Reconstruction and Combinatorial Optimisation methodologies: Paul Williamson.- Chapter 4: Estimating Small Area Income Deprivation: An Iterative Proportional Fitting Approach: Ben Anderson.- Chapter 5: SimObesity: Combinatorial Optimisation (deterministic) model: Kimberley Edwards and Graham Clarke .- Chapter 6: Spatial Microsimulation using a generalised regression model: Robert Tanton, Ann Harding and Justine McNamara .- Chapter 7: Creating a Spatial Microsimulation model of the Irish Local Economy: Niall Farrell, Karyn Morrissey andCathal O'Donoghue .- Chapter 8: Linking static spatial microsimulation modelling to meso-scale models: The Relationship between Access to GP services & Long Term Illness: Karyn Morrissey, Graham Clarke and Cathal O'Donoghue .- Chapter 9: Projections using a static Spatial Microsimulation model: Yogi Vidyattama and Robert Tanton .- Chapter 10: Limits of static Spatial Microsimulation models: Robert Tanton and Kimberley Edwards .- Part 3: Dynamic spatial microsimulation models.- Chapter 11: Moses: A dynamic spatial microsimulation model for demographic planning: Belinda Wu and Mark Birkin .- Chapter 12: Design principles for micro models: Einar Holm and Kalle Mäkilä .- Chapter 13: SimEducation: a dynamic spatial microsimulation model for understanding educational inequalities: Dimitris Kavroudakis, Dimitris Ballas and Mark Birkin .- Chapter 14: Challenges for spatial dynamic microsimulation modelling: Mark Birkin .- Part 4: Validation of spatial microsimulation models and conclusion.- Chapter 15: Validation of spatial microsimulation models: Kimberley Edwards and Robert Tanton .- Chapter 16: Conclusions and the future of spatial microsimulation modelling: Graham Clarke and Ann Harding.