DUET has been a unique collaborative endeavour where technology providers worked side by side with public administrations to create a LDT solution that is right for them, taking into account population size, policy needs, data culture, governance model, and more. The presence of cities with different properties meant that we could study our implementation results from different viewpoints to better understand what works and doesn’t in different contexts. The main impacts and lessons learned are derived from real-life experience of building and testing LDTs in Athens, Flanders, and Pilsen.
Athens: One of the challenges and main lessons learned concern the openness, accessibility, quality and format of available datasets. In Greece, local open data is only starting to gain traction. Many datasets are not aggregated, provided in non-compatible formats or simply closed (private). Through local testing cycles involving citizens, businesses, academia and policy makers, the Athens pilot concluded that a LDT can become a trusted urban data collector and/or repository of city information which stakeholders from various different domains and backgrounds can use to support better informed decisions and improve urban situations.
Flanders: One of the highlights of the Flemish pilot is the integration of local and regional models, systems and datasets e.g. the use of Flanders traffic model in Ghent, the fusion of regional air quality models (from VITO) with local 3D LOD2 data, and the merger of local and regional sensor networks for better coverage/granularity. The pilot was able to make these different components “talk to each other” via a complex system of several standards-based subsystems. Also, the process required extensive cooperation between different departments (e.g. data and information, mobility, environment) especially in the field of roadworks.
Pilsen: LDT adoption required changes in established practices and a desire to try to do things differently than before. What was especially important to obtain is buy-in from key stakeholders, so the Pilsen pilot had to convince local decision makers to give their endorsement, by showcasing the technology’s benefits using concrete examples, such as those provided on citytwin.eu. Building a digital twin is a gradual, step-by-step process. The pilot started with available data in one or two domains (e.g. traffic, environment, 3D data), focusing on high-quality datasets, including IoT data, and subsequently added other domains to the mix. The next step was to move from data analysis and visualisation towards predictive models. This allowed LDT outputs to provide powerful data-based evidence for policy making.
