  Population synthesis involves generating synthetic yet realistic
representations of a target population of micro-agents for behavioral modeling
and simulation. Traditional methods, often reliant on target population
samples, such as census data or travel surveys, face limitations due to high
costs and small sample sizes, particularly at smaller geographical scales. We
propose a novel framework based on copulas to generate synthetic data for
target populations where only empirical marginal distributions are known. This
method utilizes samples from different populations with similar marginal
dependencies, introduces a spatial component into population synthesis, and
considers various information sources for more realistic generators.
Concretely, the process involves normalizing the data and treat it as
realizations of a given copula, and then training a generative model before
incorporating the information on the marginals of the target population.
Utilizing American Community Survey data, we assess our framework's performance
through standardized root mean squared error (SRMSE) and so-called sampled
zeros. We focus on its capacity to transfer a model learned from one population
to another. Our experiments include transfer tests between regions at the same
geographical level as well as to lower geographical levels, hence evaluating
the framework's adaptability in varied spatial contexts. We compare Bayesian
Networks, Variational Autoencoders, and Generative Adversarial Networks, both
individually and combined with our copula framework. Results show that the
copula enhances machine learning methods in matching the marginals of the
reference data. Furthermore, it consistently surpasses Iterative Proportional
Fitting in terms of SRMSE in the transferability experiments, while introducing
unique observations not found in the original training sample.
