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9781498711548

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In this groundbreaking book, the authors present the ideas behind spatial microsimulation, giving clear, user-friendly guidance using the open source software R. Spatial Microsimulation with R provides the reader with firm knowledge of the field as well as the tools to apply the methods to his or her own data. Written in an extremely accessible way, this book demonstrates the key steps in spatial microsimulation, from theory into practice. It will be deservedly instrumental in fuelling the growing interest in spatial microsimulation amongst geographers, economists, urban and regional planners, and public- and private-sector decision makers. -Richard Harris, Professor of Quantitative Social Geography, University of Bristol This book fills an important gap in the existing literature. What is currently missing is a book mapping out the complete picture from what spatial microsimulation is and why it is useful, how to prepare the data, how to actually build the model, how to validate it, and how to use the resulting dataset. A particular strength of the book is the close connection between theory and implementation. The book includes very useful code snippets while the complete scripts are provided on the corresponding Github repository-this clearly sets standards for open science. -Ulrike Deetjen, Oxford University Lovelace and Dumont provide a great service to the microsimulation community in developing a clear and coherent exposition of the use of the R computer language for implementing spatial applications. Required reading for all those involved in agent-based and microsimulation modeling. -Michael Batty, Centre for Advanced Spatial Analysis, University College London ...the book provides an excellent introduction to the theory and practice of spatial microsimulation, as well as a bridge to working in R to actually do the various tasks described... And putting 'theories into practice' is exactly where the strengths of the book come to light. An impressive inclusion is a section comparing two different R packages to perform the crucial step of iterative proportional fiting (IPF): Alexander Blocker's ipfp and Barthelemy, Suesse, and Namazi-Rad's mipfp package; a lesser volume would have simply chosen one or the other... the book would serve as an excellent text for a higher-level course on the subject. -Journal of Statistical Software

This book fills an important gap in the existing literature. What is currently missing is a book mapping out the complete picture from what spatial microsimulation is and why it is useful, how to prepare the data, how to actually build the model, how to validate it, and how to use the resulting dataset. A particular strength of the book is the close connection between theory and implementation. The book includes very useful code snippets while the complete scripts are provided on the corresponding Github repository-this clearly sets standards for open science. -Ulrike Deetjen, Oxford University Lovelace and Dumont provide a great service to the microsimulation community in developing a clear and coherent exposition of the use of the R computer language for implementing spatial applications. Required reading for all those involved in agent-based and microsimulation modeling. -Michael Batty, Centre for Advanced Spatial Analysis, University College London

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Robin Lovelace is a University Academic Fellow at the University of Leeds specializing in methods of spatial data analysis and applied transport modeling. Creator of the stplanr package and a number of popular tutorials, he is an experienced R user, teacher, and developer. Robin uses open source software daily for spatial analysis, map making, statistics, and modeling. His current research focuses on online interactive mapping and modeling to provide the evidence base needed for a transition away from fossil fuels in the transport sector. Morgane Dumont is an applied mathematician currently undertaking a PhD at the University of Namur. She has a wealth of experience programming in R, Python, C, Fortran, and MATLAB®. Her research focuses on forecasting the health needs of the elderly in 2030 for Belgium. To achieve this aim, Morgane is developing a synthetic population for Belgium as an input to an agent-based model.

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