Abstract
AbstractThis article discusses the technology of city digital twins (CDTs) and its potential applications in the policymaking context. The article analyzes the history of the development of the concept of digital twins and how it is now being adopted on a city-scale. One of the most advanced projects in the field—Virtual Singapore—is discussed in detail to determine the scope of its potential domains of application and highlight challenges associated with it. Concerns related to data privacy, availability, and its applicability for predictive simulations are analyzed, and potential usage of synthetic data is proposed as a way to address these challenges. The authors argue that despite the abundance of urban data, the historical data are not always applicable for predictions about the events for which there does not exist any data, as well as discuss the potential privacy challenges of the usage of micro-level individual mobility data in CDTs. A task-based approach to urban mobility data generation is proposed in the last section of the article. This approach suggests that city authorities can establish services responsible for asking people to conduct certain activities in an urban environment in order to create data for possible policy interventions for which there does not exist useful historical data. This approach can help in addressing the challenges associated with the availability of data without raising privacy concerns, as the data generated through this approach will not represent any real individual in society.
Highlights
The growing complexity of the world requires new analytical tools to be deployed for harnessing the potential benefits of evidence-based policy
The synthetic population approaches have shown to be effective in epidemic simulation applications (Wu et al, 2018) and in deriving synthetic population from the country-wide census for privacy protection of the individuals (Wickramasinghe et al, 2020). This approach has vast policy implications, because by using synthetic populations, policymakers can evaluate the city-scale built environment policies (He et al, 2020), analyze the risk for cardiovascular disease (Krauland et al, 2020), and evaluate electricity consumption in a neighborhood. This wide spectrum of applications makes the approach of synthetic population derivation from the micro-level urban mobility data a potentially effective addition to the City digital twins (CDTs), as it will allow for both the simulation with individual-level data without violating privacy laws, as well as provide an instrument for “what-if” simulations about the events for which there are currently no available data
This article investigates the potential that the technology of CDTs has for simulating policy interventions
Summary
The growing complexity of the world requires new analytical tools to be deployed for harnessing the potential benefits of evidence-based policy. The final section of the article proposes an alternative task-based approach to urban mobility data generation. The city authorities can establish services responsible for asking people to conduct certain activities in an urban environment in order to create data for possible policy interventions for which there does not exist useful historical data. This can potentially allow the governments to collect unique data about possible behavioral responses to certain interventions, while simultaneously not violating any privacy concerns as the data generated through this approach will not be representative of any single individual
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