Introducing spatial microsimulation with
Who this book is for and how to use it
A definition of spatial microsimulation
Learning by doing
Why spatial microsimulation with R?
Learning the R language
An overview of the book
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 (R). 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.
". . . 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 . . ."
-Ezra Haber Glenn, Journal of Statistical Software
"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
" . . .this book is an excellent resource for everyone who
want to learn how to do spatial microsimulation. The possibility to
download the contents of the book, compile it andwork interactively
with the code also makes it a great example of dynamic documents
and reproducible research."
-Netherlands Environmental Assessment Agency (PBL)
"There are multiple books on spatial microsimulation and
hundreds more research papers detailing the various applications of
studies. However, bar a few exceptions, they lack transparency and
reproducibility. It creates a situation whereby researchers
simultaneously encourage the uptake of the method, whilst also
creating barriers by obscuring methodologies. Lovelace and Dumont's
book sets to address this flaw through demonstrating how to
undertake a spatial microsimulation. . . Where the book sets itself
apart from other books is in its applied nature. At the centre of
their approach is a 'learn by doing' mentality which serves the
book well. Examples are used to demonstrate approaches and the
authors encourage thinking beyond the material presented. Of note,
Lovelace and Dumont show step-by-step what is happening when using
IPF and how to code this, rather than jumping straight to bespoke R
packages that can run it in single lines of code (which are also
covered). Content is always clearly set out, with each step
explained in detail. The approach helps to guide the reader along
in understanding fairly complicated methods. . . Lovelace and
Dumont's book is a fine addition to the library of anyone
interested in quantitative methods, let alone those wanting to
generate their own spatial microdata."
-Mark A. Green, Applied Spatial Analysis