Spatial Microsimulation Using a Generalised Regression Model

Chapter
Part of the Understanding Population Trends and Processes book series (UPTA, volume 6)

Abstract

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.

Keywords

Poverty Rate Accuracy Criterion Survey Dataset Benchmark Class Spatial Microsimulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer Science+Business Media Dordrecht. 2012

Authors and Affiliations

  1. 1.National Centre for Social and Economic ModellingUniversity of CanberraCanberraAustralia

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