MIM8: Local Digital Twins
Description
This MIM describes interoperability in terms of the application domain of Local Digital Twins. In support of describing minimal interoperability mechanisms in this domain the MIM8 working group put forward a minimal definition of Local (or Urban) Digital Twins. This does not preclude any other definitions to be valid, and we recognise that depending on applications features may vary.
A Local (or Urban) Digital Twin is a digital representation of physical assets, systems, or processes in a defined local context (e.g., city, district, building, industry, port, airport). It leverages either on historical data, near real-time data, or real-time data enables visualization, analysis, simulation and reasoning for supporting decision-making.
The working group understands the diversity of local digital twins depending on their functionalities. Below is a table of different functionalities of a Local (or Urban) Digital Twin can have and as such are commonly categorised as: Awareness LDT, Experimental LDT, Predictive LDT, and Intelligent LDT.
Awareness
X
Experimental
X
X
Predictive
X
X
X
Intelligent
X
X
X
X
*Analytics can include 'diagnostic', 'descriptive', 'predictive', and 'prescriptive' capabilities.
While we align with commonly agreed definitions (such as the one above), communities have expressed that simulations require more complex work process than analytics, and hence, experimental digital twins are viewed more complex as predictive LDTs.
We discern the following additional functionalities across the following layers:
Layer 1: Data acquisition (historic/near real-time/real-time data)
sensing
extraction
synthetic data generation
Layer 2: Connectivity
APIs
protocols
file sharing
Layer 3: Data pre-processing
semantics
interoperability
aggregation
Layer 4: Analysis & simulation
AI Models
Physics-based simulations
Layer 5: Communication of results (visualisation)
Dashboard
Multi-dimensional visualisation
natural language interface (like chatbot)
VR
Layer 6: Decision-making
prescription
support
automation
Literature
Deutsches Institut für Normung e. V. (2024). DIN SPEC 91607:2024-11 – Digitale Zwillinge für Städte und Kommunen [Digital twins for cities and municipalities]. DIN e. V https://dx.doi.org/10.31030/3575521
Gil, J., Petrova-Antonova, D., & Kemp, G. J. (2024). Redefining urban digital twins for the federated data spaces ecosystem: A perspective. Environment and Planning B: Urban Analytics and City Science, 0(0). https://doi.org/10.1177/23998083241302578
European Commission: Directorate-General for Communications Networks, Content and Technology, Robalo Correia, A., Sousa, M., Mulquin, M., Santos, F. et al., Mapping EU-based LDT providers and users, European Commission, 2023, https://data.europa.eu/doi/10.2759/547098
Tartia, J., & Hämäläinen, M. (2024). Co-creation processes and urban digital twins in sustainable and smart urban district development – Case Kera district in Espoo, Finland. Open Research Europe, 4, 130. https://doi.org/10.12688/openreseurope.17791.1
Objective
The guiding principle for the objective of this MIM is:
To ensure Local (/Urban) Digital Twins can communicate with other data ecosystems, can scale up and they can integrate new data sets, services and components easily.
Capabilities and Requirements
C1: LDTs are able to share data with other data ecosystems
R1: Data can be interlinked (see MIM1), represented (see MIM2), exchanged (see MIM3) and secured (see MIM6) across silos/sectors.
R2: LDTs (also across sectors) can work together to inform a use case
C2: LDTs are able to share services
R1: Services can be exchanged across silos/sectors
R2: The LDT can be applied to other data ecosystems with minimal integration effort
R3: Common services can serve multiple local digital twin instantiations
C3: LDTs are able to share components*
R1: Common components can serve multiple local digital twin instantiations
R2: The LDT is able to integrate new components across any of the layers, possibly from other LDTs from different sectors
R3: The components from Layers (2), 3, 4 and 5 can be applied to other data ecosystems from different silos/sectors
*Note: components are individual implementations within each of the Digital Twin's layers; such as: a visualisation tool, an identity and access management system, a predictive model, etc...
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