ISBN:
9789400746237
Language:
English
Pages:
Online-Ressource (VIII, 277 p. 51 illus., 10 illus. in color, digital)
Series Statement:
Understanding Population Trends and Processes 6
Series Statement:
SpringerLink
Series Statement:
Bücher
Parallel Title:
Buchausg. u.d.T. Spatial microsimulation
Keywords:
Social sciences
;
Quality of Life
;
Geography
;
Economics Statistics
;
Quality of Life Research
;
Demography
;
Social Sciences
;
Social sciences
;
Quality of Life
;
Geography
;
Economics Statistics
;
Quality of Life Research
;
Demography
;
Spatial analysis (Statistics)
;
Space
;
Computer simulation
;
Statistical matching
;
Demography
;
Demographie
;
Räumliche Statistik
;
Mikrosimulation
Abstract:
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.
Description / Table of Contents:
Spatial Microsimulation: A Reference Guide for Users; Foreword; Contents; Part I: Background; Chapter 1: Introduction to Spatial Microsimulation: History, Methods and Applications; 1.1 Introduction; 1.2 History of Spatial Microsimulation; 1.3 Applications of Spatial Microsimulation Models; 1.4 Validation of Spatial Microsimulation Models; 1.5 The Future; 1.6 Conclusion; References; Chapter 2: Building a Static Spatial Microsimulation Model: Data Preparation; 2.1 Data Sources and Requirements; 2.2 Sample Scope; 2.3 Unit of Analysis; 2.3.1 Non-private Dwellings
Description / Table of Contents:
2.3.2 Non-classifiable Households2.4 Population Imputation; 2.4.1 Imputation of Child Records; 2.4.2 Imputation of a Non-private Dwelling Population; 2.5 Matching Variable Definitions in the Sample Survey and the Census; 2.6 Uprating and Deflating; 2.7 Balancing Data; 2.8 Conclusion; References; Part II: Static Spatial Microsimulation Models; Chapter 3: An Evaluation of Two Synthetic Small-Area Microdata Simulation Methodologies: Synthetic Reconstruction and Combinatorial Optimisation; 3.1 Background; 3.2 Synthetic Reconstruction and Combinatorial Optimisation Methodologies
Description / Table of Contents:
3.2.1 Synthetic Reconstruction3.2.2 Combinatorial Optimisation; 3.3 Innovations in Synthetic Reconstruction; 3.3.1 Modified Monte Carlo Sampling; 3.3.2 Statistical Justification of Reconstruction Order; 3.3.3 Modelled 100% Counts of 10% Data; 3.3.4 Improved Data Linkage; 3.3.5 Data Reconciliation; 3.4 Innovations in Combinatorial Optimisation; 3.4.1 Validated Random Number Generation; 3.4.2 Sequential Table Fitting; 3.4.3 Stratified Household Selection; 3.4.4 RSSZ*: A New Selection Criterion; 3.4.5 Stopping Rules; 3.5 Understanding Between-Area Variation; 3.5.1 Spatial Concentration
Description / Table of Contents:
3.5.2 Multicollinearity3.6 A Framework for Validating Small-Area Microdata; 3.6.1 Identification of Appropriate Measures of Fit; 3.6.2 Innovations in Types of Fit Measured; 3.7 The Impact on Combinatorial Optimisation of Selected Improvements; 3.7.1 Substitution of TAE with RSSZ *; 3.7.2 Stratified Household Selection; 3.8 Synthetic Reconstruction vs. Combinatorial Optimisation; 3.8.1 ED-Level Mean Fit; 3.8.2 ED-Level Fit of the Mean; 3.8.3 Ward-Level Fit; 3.8.4 Fit of Unconstrained Counts; 3.9 Conclusion; References
Description / Table of Contents:
Chapter 4: Estimating Small-Area Income Deprivation : An Iterative Proportional Fitting Approach4.1 Background; 4.2 Small-Area Income Estimation Methods; 4.3 The Iterative Proportion Fitting Approach; 4.3.1 Definition of Income; 4.3.2 Choice of Constraint Variables; 4.3.3 Small-Area IPF Algorithm Implementation; 4.4 Results; 4.5 Validation; 4.6 Conclusions and Future Directions; References; Chapter 5: SimObesity: Combinatorial Optimisation (Deterministic) Model; 5.1 Introduction; 5.2 Why Use Spatial Microsimulation Modelling to Model Disease Data?; 5.2.1 Why Use a Deterministic Model?
Description / Table of Contents:
5.3 SimObesity Methodology
Description / Table of Contents:
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 and Cathal 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..
Note:
Description based upon print version of record
DOI:
10.1007/978-94-007-4623-7
URL:
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