<commit_before>\chapter*{Preface}

Spatial microsimulation is a set of methods for modelling phenomena which operate
at individual and geographical levels simultaneously.
The city is a classic case of a system that the techniques
presented in this book can help to understand: Shanghai
(depicted on the front cover), for example, is a complex metropolis with a
population of more than 10 million incessantly interacting people. Such a system is clearly hard
to simulate. Yet, by breaking the problem up into its
constituent parts --- discrete geographic areas and a sample of the human
population --- spatial microsimulation can be used to model key aspects of the city system
an everyday laptop computer. Of course there are dangers associated with reductionism.
However, the advantages of condensing a problem down to its fundamentals
should be clear to the reader:
we could simulate population growth, increased energy efficiency or even
a ban on cars. Moreover, by linking the synthetic population
to an agent-based model, complex
behaviours such as social segregation could also be simulated.

This therefore book is for anyone who wants to not only understand
but to \emph{use} spatial microsimulation, for applied or academic work.
The emphasis is therefore practical rather than theoretical.
New R packages for assisting with the generation of spatial microdata are described in
detail, with reference to practical examples and reproducible code.
The aim is to enable you to implement the methods on your own
datasets to better understand the world. 

By enabling the modelling of the fundamentals of
complex phenomena that vary over space,
this book should enhance your knowledge and understanding
systems such as Shanghai.
As the well-known saying goes, knowledge is power.
Therefore, in combination with an understanding of social and
political realities (which may not be conducive to computer modelling),
this book should also empower its readers to change the world.
In other words,
spatial microsimulation can be used to encourage evidence-based policy.
% and are best understood with
% reference to my own experience of trying to learn spatial microsimulation.

% Like many PhD students<commit_msg>Fix typo in preface<commit_after>\chapter*{Preface}

Spatial microsimulation is a set of methods for modelling phenomena which operate
at individual and geographical levels simultaneously.
The city is a classic case of a system that the techniques
presented in this book can help to understand: Shanghai
(depicted on the front cover), for example, is a complex metropolis with a
population of more than 10 million incessantly interacting people. Such a system is clearly hard
to simulate. Yet, by breaking the problem up into its
constituent parts --- discrete geographic areas and a sample of the human
population --- spatial microsimulation can be used to model key aspects of the city system
an everyday laptop computer. Of course there are dangers associated with reductionism.
However, the advantages of condensing a problem down to its fundamentals
should be clear to the reader:
we could simulate population growth, increased energy efficiency or even
a ban on cars. Moreover, by linking the synthetic population
to an agent-based model, complex
behaviours such as social segregation could also be simulated.

This book is for anyone who wants to not only understand
but to \emph{use} spatial microsimulation.
The emphasis is practical rather than theoretical.
New R packages for assisting with the generation of spatial microdata are described in
detail, with reference to practical examples and reproducible code.
The aim is to enable you to implement the methods on your own
datasets to better understand the world. 

By enabling the modelling of the fundamentals of
complex phenomena that vary over space,
this book should enhance your knowledge and understanding
systems such as Shanghai.
As the well-known saying goes, knowledge is power.
Therefore, in combination with an understanding of social and
political realities (which may not be conducive to computer modelling),
this book should also empower its readers to change the world.
In other words,
spatial microsimulation can be used to encourage evidence-based policy.
% and are best understood with
% reference to my own experience of trying to learn spatial microsimulation.

% Like many PhD students
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