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The generation of synthetic data is an essential tool to study complexsystems, allowing for example to test models of these in precisely controlledsettings, or to parametrize simulation models when data is missing. This paperfocuses on the generation of synthetic data with an emphasis on correlationstructure. We introduce a new methodology to generate such correlated syntheticdata. It is implemented in the field of socio-spatial systems, more preciselyby coupling an urban growth model with a transportation network generationmodel. We also show the genericity of the method with an application onfinancial time-series. The simulation results show that the generation ofcorrelated synthetic data for such systems is indeed feasible within a broadrange of correlations, and suggest applications of such synthetic datasets.

Second-order Control of Complex Systems with Correlated Synthetic Data