An introduction to spatial microsimulation with R A Leeds Short Course funded by the CDRC Robin Lovelace, University of Leeds See http://robinlovelace.net/smsim-course/ 28th May 2015 

About this course ● Aims to introduce both theory and practice of spatial microsimulation ● Divided into 3 main parts: – Foundations – Creating spatial microdata with R – Applying the method ● Housekeeping 

The agenda... and the handout ● 9:30 – 11:00 Introduction – What is it? What it does. Reweighting ● 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 – Validation ● 11:15 – 13:30 Extensions – Integerisation 1) Foundations 2) IPF in R 3) CakeMap / other 

Why do you need spatial microsimulation? I ● To tackle the modifiable areal unit problem (MAUP) (Openshaw 1983) The world is like this: complex Administrative zones (“areal units”) look like this - a little arbitrary! Making it look (a bit) like this: oversimple! 

Why do you need spatial microsimulation? II As an input into agent based models (image is in the context of transport modelling – deterministic reweighting = spatial microsimulation) To create a synthetic population, also known as synthetic spatial microdata 

What is spatial microsimulation? I ● A method for synthesising spatial microdata based on survey and areal input data (Lovelace and Ballas 2013) 

What is spatial microsimulation? II ● A procedure to translate from 'wide' to 'long' data formats ● Restrictive data anonymity regulations 

Applications – some examples ● To estimate local smoking rates (Tomintz et al. 2008) ● 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 

Validation ● It's tricky – you'll generally only simulate things you do not know ● But very important: possible to mislead with this technique ● Internal validation: compare with prior expectations – is the model working OK? ● External validation: compare with the real world 

References ● 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).