The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create “augmented datasets” to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policies' impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest.
Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation.
The package: simPop is a user-friendly R-package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression.
The following applications further shows the methods and package:
We firstly demonstrated the use of simPop by creating
a synthetic population of Austria based on the
European Statistics of Income and Living Conditions (Alfons et al., 2011)
including the evaluation of the quality of the generated population.
In this contribution, the mathematical details of functions
simComponents are given in detail.
The disclosure risk of this synthetic population has been evaluated in (Templ and Alfons, 2012) using large-scale simulation studies.
Employer-employee data were created in Templ and Filzmoser (2014) whereby the structure of companies and employees are considered.
Finally, the R package simPop is presented in full detail in Templ et al. (2017). In this paper - the main reference to this work - all functions and the S4 class structure of the package are described in detail. For beginners, this paper might be the starting point to learn about the methods and package.
|License:||GPL (>= 2)|
Bernhard Meindl, Matthias Templ, Andreas Alfons, Alexander Kowarik,
Maintainer: Matthias Templ <[email protected]>
M. Templ, B. Meindl, A. Kowarik, A. Alfons, O. Dupriez (2017) Simulation of Synthetic Populations for Survey Data Considering Auxiliary Information. Journal of Statistical Survey, 79 (10), 1–38. doi: 10.18637/jss.v079.i10
A. Alfons, M. Templ (2011) Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods & Applications, 20 (3), 383–407. doi: 10.1007/s10260-011-0163-2
M. Templ, P. Filzmoser (2014) Simulation and quality of a synthetic close-to-reality employer-employee population. Journal of Applied Statistics, 41 (5), 1053–1072. doi: 10.1080/02664763.2013.859237
M. Templ, A. Alfons (2012) Disclosure Risk of Synthetic Population Data with Application in the Case of EU-SILC. In J Domingo-Ferrer, E Magkos (eds.), Privacy in Statistical Databases, 6344 of Lecture Notes in Computer Science, 174–186. Springer Verlag, Heidelberg. doi: 10.1007/978-3-642-15838-4_16
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## we use synthetic eusilcS survey sample data ## included in the package to simulate a population ## create the structure data(eusilcS) ## Not run: ## approx. 20 seconds computation time inp <- specifyInput(data=eusilcS, hhid="db030", hhsize="hsize", strata="db040", weight="db090") ## in the following, nr_cpus are selected automatically simPop <- simStructure(data=inp, method="direct", basicHHvars=c("age", "rb090")) simPop <- simCategorical(simPop, additional=c("pl030", "pb220a"), method="multinom", nr_cpus=1) simPop class(simPop) regModel = ~rb090+hsize+pl030+pb220a ## multinomial model with random draws eusilcM <- simContinuous(simPop, additional="netIncome", regModel = regModel, upper=200000, equidist=FALSE, nr_cpus=1) class(eusilcM) ## End(Not run) ## this is already a basic synthetic population, but ## many other functions in the package might now ## be used for fine-tuning, adding further variables, ## evaluating the quality, adding finer geographical details, ## using different methods, calibrating surveys or populations, etc. ## -- see Templ et al. (2017) for more details.
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