Simulation of Synthetic Complex Data: The R Package simPop

Abstract

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 policy 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. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. 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. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package.

Generation and evaluation of synthetic patient data

“…Finally, we note that several open-source software packages exist for synthetic data generation. Recent examples include the R packages synthpop [30] and SimPop [31], the Python package DataSynthesizer [5], and the Java-based simulator Synthea [7].…”

Treating Nonresponse in Probability-Based Online Panels through Calibration: Empirical Evidence from a Survey of Political Decision-Making Procedures

“…We select for this simulation study the adults of eusilcP data set (population size 47,123), a population available from the R-packages simFrame [38] and simPop [39]. As a panel, we draw a sample of 1500 households stratified by region and select all members in each household.…”

Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia

“…These manually created EA-level urban household type probabilities were multiplied by the predicted household type probability surfaces created in step 3 to create the final 100 m × 100 m household type probability surfaces. Fifth, we simulated a population of realistic households in Oshikoto using the 20% census microdata sample and multinomial logistic regression techniques proposed by Alfons and colleagues (2011) and operationalized by Templ and colleagues (2017) in the R simPop package [7,15]. In this Data 2018, 3, 30 9 of 19 approach, we first calculated the proportion of households to simulate per household-size, per stratum (defined by constituency and urban/rural boundary).…”

“…Combinatorial optimization procedures, such as simulated annealing (SA) [13] or quota sampling [14], can also be used to prevent sub-optimal combinations of attributes in the simulated dataset. Templ and colleagues discuss a model-based approach to simulation of individual or household attributes with regression models, which they implement in an open-source software [15]. Agent-based models (ABMs) can also produce a realistic count of individuals, or "agents", along with key attributes and relationships [16,17].…”