Urban Digital Twins in Practice: A Survey-Based Analysis of Data Integration Levels and Approaches

Data integration is a significant technical challenge that hinders the full implementation of Urban Digital Twins (UDTs). Given their data-driven nature, UDTs rely on the integration of heterogeneous data sources, schemas, and structures. This process is inherently complex due to the diversity of techniques and systems involved. Furthermore, with the emergence of UDTs, the focus has shifted from acquiring new data through sensors to exploring how existing data can be integrated, restructured, and connected to meet the evolving requirements of digital twins. In this context, we raise fundamental questions about data integration within UDTs. Building on a previously proposed conceptual framework defining levels of data integration, this study gathers insights from practitioners actively engaged in UDT implementations. The aim is to understand how these conceptual integration levels are applied in practice, how they are selected across the UDT lifecycle, what tools support their implementation, and how they enable effective integration depending on the availability and nature of the data. By aligning theoretical frameworks with practical implementations, this research aims to enhance the effectiveness and adoption of scalable and flexible data integration strategies in the development, use, and maintenance of UDTs. The findings highlight the relevance of data integration levels for UDT practitioners, demonstrating that they were able to identify which levels are involved in their projects. Additionally, the results provide insights into how each level of integration is commonly applied across different stages of the lifecycle, as well as the tools and methods associated with each level.