Consolidating feedbacks and expertise of Digital Twins of Territories’ engineers in nation-wide frameworks
Keywords: Digital Twin of Territory, Metadata, User Feedback, Minecraft, Data integration
Abstract. Digital Twins of Territories (DTTs) are increasingly adopted by municipalities to support ecological transition, crisis resilience, and participatory decision-making. Designing a DTT that fits local needs requires engineers to combine multiple areas of expertise (data discovery, integration, modeling, visualization, and stakeholder interaction) while working with heterogeneous geospatial datasets of varying quality. Nation-wide DTT frameworks aim to assist these efforts, yet they currently lack mechanisms to consolidate the expertise produced during local DTT developments. This paper introduces dttrecipe, a model designed to capture, structure, and share DTT engineers’ feedback and decision-making processes. Building on the prov, wfdesc and wfprov ontologies, and inspired by the OGC Geospatial User Feedback standard, dttrecipe formalizes the description of territorial stakes, data workflows, encountered problems, and the rationale behind design choices. It supports both complete and partial workflow descriptions, encouraging collaboration, reproducibility, and cross-territorial knowledge reuse. The model is qualitatively evaluated via a case study focused on bicycle-mobility planning and citizen engagement in a rural city. The resulting recipe highlights recurrent categories of DTT engineering challenges, including data discoverability and usability issues, multi-source misalignment, documentation accessibility, and limited local expertise. Explicit documentation of these challenges shows how engineers’ often implicit expertise can be converted into reusable knowledge for other territories facing similar constraints. The work shows that structured documentation of DTT engineering practices can strengthen national DTT frameworks by improving interoperability and enabling efficient knowledge transfer. Future work will address querying mechanisms and evaluate the reuse of shared recipes at scale.
