Here are links to some of the core texts in spatial microsimulation and R. These are by no means comprehensive, but should provide useful starting points for an introduction to spatial microsimulation and a little on its history and applications.
These references are available for free online. It is recommended that course participants acquaint themselves with the basics of spatial microsimulation before the course:
Ballas, D., Dorling, D., Thomas, B., Rossiter, D., 2005. Geography matters: simulating the local impacts of national social policies. Joseph Roundtree Foundation.
Lovelace, R., 2013. Supplementary Information: A user manual for the integerisation of IPF weights using R 1–18. (Linked to TRS paper).
To get to grips with R, users are recommended to install the excellent RStudio, and test-run R based on an introductory user guides:
RStudio, the recommended R Integrated Development Envrionment (IDE), available free of charge for Windows, Mac and Linux from http://www.rstudio.com/
Torfs, P. and Brauer, C., 2012. A (very) short Introduction to R Torfs, P. and Brauer. Comprehensive R Archive Network
These references show how spatial microsimulation can be applied to solve practical policy problems. There is much scope for more applied spatial microsimulation work.
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.
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.
Spatial microsimulation has a long history dating back to at least the 1940s when some of the methods still used today were developed. More recent methodological papers are generally more accessible.
Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. The Annals of Mathematical Statistics, 11(4), 427-444.
Fienberg, S. E. (1970). An iterative procedure for estimation in contingency tables. The Annals of Mathematical Statistics, 907-917.
Lovelace, Robin, and Ballas, D., 2013 “‘Truncate, Replicate, Sample’: A Method for Creating Integer Weights for Spatial Microsimulation.” Computers, Environment and Urban Systems 41: 1–11.
Norman, P. (1999). Putting iterative proportional fitting on the researcher's desk. University of Leeds working paper.