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Spatial data underpins digital twins of the natural and built environments 

Digital twin solutions have been at the forefront of developments in data intelligence for some time, and increasingly there is a focus on the importance of geospatial data when using digital twin technology. 

As our ability to gather and store data has improved, so too have our techniques for using this data. Geospatial Digital Twins allow organisations to gather, record and update the behaviour of assets in location models in real time, this data has new capabilities to represent the real world, matching the operational reality of the physical environments they represent. 

Having a validated Geospatial Digital Twin gives you a host of powerful capabilities, from predicting asset failure to easing traffic incidents by altering traffic light patterns using the data captured in real time. The data captured as part of the geospatial digital twin process gives your data unprecedented context, meaning any business decisions can be made with confidence.

What is a Geospatial Digital Twin? 

A Geospatial Digital Twin is a like-for-like digital representation of a real-world environment, which is updated in real time. The live update approach used to create a digital twin differs fundamentally to more traditional geospatial data sets, which represent the idealised versions of the assets as they were when first constructed and at a single snapshot in time.

Using geospatial data with a digital twin means the model will do more than simply mirror objects virtually, there will now be geospatial context to the data. This context is extremely valuable, allowing you to improve the quality of visualisation, expand analytical capabilities and improve predictive processes. 

Geospatial Digital Twin Examples 

An example of a geospatial digital twin and its benefits would be the emergency management for a water pipe network, based on how the network is connected and what is above ground (the geospatial aspect). The latest pressure and flow data for those pipes (the live sensor aspect) would also help to model how the network is behaving and will continue to behave.

Another example would be a digital twin of a building. This may include its main structure, internal networks such as plumbing and wiring and sensor information about the environment such as electricity flow, temperature, or air quality. By providing the geospatial context then not only does the data allow for calculations such as heating requirements based on heat loss, or electricity demands based on usage and solar panel capacity; but by placing the building within the wider geospatial context, you can then factor in aspects such as shade from surrounding environment, or the capacity of the electricity supply cables.

The role of data management and Digital Twins 

A Geospatial Digital Twin contains two fundamental elements: The as-built geospatial representation of the assets and the live sensor data that relates to their current operating condition. 

The as-built geospatial representation includes the attributes and geometry of the features in your digital twin. These attributes underpin the validity and quality of your Digital Twin. Accurately recorded attributes are core to the reliability and value of your data. Without the ability to validate and ensure the attributes in your Geospatial Digital Twin are fit for purpose, you can never have full confidence in decisions you make based on attributes. But how do you ensure validation and accuracy are embedded into the Geospatial Digital Twin you are creating?

With a more traditional data model, it is normal for data capture and validation to happen extensively at the start of a project, developing a perfect snapshot of the environment you are recording at that moment in time. From this point of initial data capture, you may schedule updates that focus on limited areas of the entire data set, meaning operational reality across the whole network/dataset can never be attained.

Of course, this method of data capture and utilisation can be suitable for many applications, particularly when the expectation is data will remain static or go through very few changes, but what can be done in environments with constantly changing characteristics?

Keeping data up to date with operational reality 

To make the move from traditional data models to a Geospatial Digital Twin, you must keep your data up to date with operational reality. From the starting point of the initial snapshot, your model must ingest data live. This can be achieved by using the right data capture methods from inputs such as cameras, sensors or even crowdsourcing. 

A continuous cycle of live updates mean that you can ensure that the digital representation you have is as close to operational reality as possible and having this accurate real-time model of your network, building or environment allows the most accurate simulation and analysis to be performed that are simply not possible when using the more traditional “snapshot” gathering method. 

Benefits of Geospatial Digital Twins

Key Data Management Considerations for Developing Geospatial Digital Twin 

Location Master Data Management and digital twins

Geospatial Digital Twins have applications across industries and organisations, from understanding the operational reality of a road or water network, to regional and National governments better understanding and responding to their built environments (smart cities).

Creating a complex and reactive model is of course a lengthy, technical, and challenging process and not all organisations will yet be at a point where making the jump makes financial, or indeed practical sense.

Wherever you are on your journey to creating a Digital Twin, by drawing on the power of Location Master Data Management, you can ensure your data will be ready.

Case Study: A 3D City Model for the City of Aarhus (Denmark)

Read about the Danish Agency for Data Supply and Efficiency (SDFE) pilot project focusing on Data Quality Validation For a National 3D City Model.

Find out more