Spatial microsimulation using a generalised regression model

Robert Tanton, Ann Harding, Justine McNamara

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This chapter outlines a method of spatial microsimulation that uses a reweighting algorithm implemented with the SAS programming language. The reweighting algorithm derives population weights by benchmarking the unit record level survey data to the reliable spatially disaggregated census data. These weights can then be applied to the sample to derive final populations for the small area, just like survey weights provided by national statistical agencies allow aggregation to national totals. This chapter describes in detail the data used, the estimation methodology and the advantages and disadvantages of the generalised regression method. An application to poverty estimation in Australia is also presented. Harding and Tanton (Policy and people at the small area level: Using microsimulation to create synthetic spatial data. In: Stimson R (ed) Handbook in spatially integrated social science research methods. Edward Elgar, Sydney, 2011) provide additional detail on the development of the model and applications of the model.
Original languageEnglish
Title of host publicationSpatial microsimulation: a reference guide for users
EditorsRobert Tanton, Kimberley L Edwards
Place of PublicationNetherlands
Number of pages17
ISBN (Electronic)9789400746237
ISBN (Print)9789400746220
Publication statusPublished - 2013


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