Introducing spatial microsimulation with R: a practical

Lovelace, Robin (2014) Introducing spatial microsimulation with R: a practical. NCRM Working Paper. University of Leeds. (Submitted)

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Abstract

This practical teaches the basic theory and practice of `spatial microsimulation' using the popular free software package R. The term microsimulation means different things in different disciplines, so it is important to be clear at the outset what we will and will not be covering. We will be learning how to create spatial microdata, the basis of all spatial microsimulation models, using iterative proportional fitting (IPF). IPF is an efficient method for allocating in- dividuals from a non-spatial dataset to geographical zones, analogous to the `Furness method' in transport modelling, but with more constraints. There are other ways of generating spatial microdata but, as far as the author is aware,1 this is the most effective and flexible for many applications. An alternative approach using the open source `Flexible Modelling Framework' program is described in detail, with worked examples, by Harland (2013). We will not be learning `dynamic spatial microsimulation' (Ballas et al., 2005): once the spatial microdata have been generated and integerised, it is up to the user how they are used, be it in an agent based model or as a basis for estimates of income distributions at the local level or whatever. We thus define spatial microsimulation narrowly in this tutorial as the process of generating spatial microdata (more on this below). The term can also be used to describe a wider approach that harnesses individual-level data allocated to zones for investigating phenomena that vary over space and between individuals such as income inequality or energy overconsumption. In both cases, the generation of spatial microdata is the critical element of the modelling process so the skills learned in this tutorial will provide a firm foundation for further work.

Item Type:Working Paper (NCRM Working Paper)
Subjects:5. Quantitative Data Handling and Data Analysis > 5.2 Statistical Theory and Methods of Inference
5. Quantitative Data Handling and Data Analysis > 5.4 Microdata Methods
5. Quantitative Data Handling and Data Analysis > 5.9 Spatial Data Analysis
ID Code:3348
Deposited By: TALIS User
Deposited On:03 Jun 2014 15:55
Last Modified:10 Jun 2014 10:55

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