These are the slides to accompany a two day course with the same name. See https://github.com/Robinlovelace/smsim-course for more information.
An introduction to spatial
microsimulation with R
A Leeds Short Course funded by the CDRC
Robin Lovelace, University of Leeds
28th May 2015
About this course
Aims to introduce both theory and practice of
Divided into 3 main parts:
– Creating spatial microdata with R
– Applying the method
The agenda... and the handout
9:30 – 11:00 Introduction
– What is it? What it does.
11:15 – 13:00 Smsim in R
– Loading the data, IPF in R
13:30 – 16:30 An example
– Preparing input data
9:30 – 11:00 Analysis
11:15 – 13:30 Extensions
2) IPF in R
3) CakeMap /
Why do you need spatial
To tackle the modifiable areal unit problem
(MAUP) (Openshaw 1983)
The world is like this: complex
(“areal units”) look like this
- a little arbitrary!
look (a bit)
Why do you need spatial
As an input into
models (image is
in the context of
To create a
known as synthetic
What is spatial
A method for
microdata based on
survey and areal input
data (Lovelace and
What is spatial microsimulation? II
A procedure to translate
from 'wide' to 'long' data
Restrictive data anonymity
Applications – some examples
To estimate local smoking rates (Tomintz et al.
To investigate commuter patterns and model
distributional impact of future scenarios
(Lovelace et al. 2014)
Farmer participation in agri-environment
schemes (Hynes et al. 2008)
Microsimulation and ABMs
It's tricky – you'll generally only simulate things
you do not know
But very important: possible to mislead with this
Internal validation: compare with prior
expectations – is the model working OK?
External validation: compare with the real world
Edwards, K. L., Clarke, G. P., Thomas, J., & Forman, D. (2011). Internal and external
validation of spatial microsimulation models: Small area estimates of adult obesity.
Applied Spatial Analysis and Policy, 4(4), 281-300. (The importance of validation).
Hynes, S., Farrelly, N., Murphy, E., & O'Donoghue, C. (2008). Modelling habitat
conservation and participation in agri-environmental schemes: a spatial microsimulation
approach. Ecological economics, 66(2), 258-269. (Agricultural application).
Lovelace, R., Ballas, D., & Watson, M. (2013). A spatial microsimulation approach for the
analysis of commuter patterns: from individual to regional levels. Journal of Transport
Geography. (Policy-relevant application).
Lovelace, Robin, and Dimitris Ballas. ‘Truncate, replicate, sample’: A method for creating
integer weights for spatial microsimulation. Computers, Environment and Urban Systems
41 (2013): 1-11. (Method).
Openshaw, S. (1983). The modifiable areal unit problem (Vol. 38). Norwich: Geo Books.
Tomintz, M. N., Clarke, G. P., & Rigby, J. E. (2008). The geography of smoking in Leeds:
estimating individual smoking rates and the implications for the location of stop smoking
services. Area, 40(3), 341-353. (Health application).