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Short abstract

What kind of city gets generated when a digital twin increasingly runs on synthetic data rather than observed reality? This paper argues that the turn to synthetic data for modelling urban life marks a broader epistemic shift in how populations and spaces are defined, measured, and governed.

Long abstract

Urban digital twins are usually presented as data-driven mirrors of the city, assembled from sensors, administrative records, and predictive models. At the same time, many twins now operate, at least in part, on synthetic data: generated populations, mobility traces, and behavioural patterns used to fill gaps, satisfy privacy constraints, train models, or extend forecasting where empirical data are thin. This paper asks what happens when the twin is no longer copying a city but generating one, and what kind of city gets generated in the process.

It develops the concept of synthetic twinning to name the coupling of generative data practices with the infrastructural work of building a twin. Through this coupling, synthetic data render urban territory calculable by translating heterogeneous neighbourhoods, infrastructures, and movements into comparable variables and model-ready surfaces. Drawing on STS approaches to modelling and infrastructural politics, the paper examines how generative models define what counts as normal, plausible, or risky. They impose distributions, smooth extremes, and introduce artefacts of their own, including reduced variability and the disappearance of outlier places. These moves travel into municipal decision-making, reshaping what counts as evidence and where responsibility lands.

The argument draws on preliminary empirical work on the Bologna municipal digital twin, including interviews and project documentation. The case examines how synthetic data are mobilised where datasets are missing across space or time, and how decisions about realism and bias quietly shape what the twin can predict, and what those predictions come to justify in urban governance.