There is currently no single clear and widely used definition of a digital twin city. As such, what were are seeing are many different shades of discussion when it comes to digital twin cities. But regardless of the perspective, one of the key elements of a digital twin city seems to be the combination of different data sources. However, the ways in which data are combined are emphasised slightly differently depending on the perspective.
In planning and civic engagement, the emphasis is on visualisation. The aim is to form as comprehensive a picture as possible of the future city by integrating models of planning sites, for example, into the model of the entire city. The potential applications of this type of approach include participatory planning, examining how planning sites fit into the cityscape, land use planning and the collection of feedback from groups with particular interests.
Another frequently mentioned perspective on digital twin cities is sensor data and IoT data sources. A key element of this perspective is the combination of real-time and static data, such as combining measured temperature or energy consumption data with geospatial data about locations in the urban environment. This kind of digital twin can serve as the basis for things such as monitoring the energy consumption of buildings, identifying problems spots and various types of analyses.
Instead of being a single massive database, a digital twin city can actually consist of multiple networked systems. When it comes to combining data, the original location of the data does not matter as much as having access to the data via APIs.
The data architecture creates the foundation
Regardless of what a digital twin city is considered to include, it is the city’s data and operating architecture that provides the foundation for its development. With the combination of data being a core aspect of a digital twin, the data being combined must, of course, be accessible, of sufficiently high quality and available in a format that allows it to be combined. As such, there is a tremendous amount of data and process work that goes into producing a digital twin city.
A well-designed operating architecture and effective information systems also facilitate the production of high-quality data. Managed data structures, consistent identifiers and high-quality metadata all facilitate the combination of the data. A well-designed operating architecture also strives to ensure that every data source has an owner and a clear point of origin. Processes should also be designed with the aim of limiting the number of individual copies of data, as the fewer copies there are, the easier it is to keep the data up to date.
It should also be noted that when the aim is to combine different types of data, not all of the data needs to be stored on a single system. Instead of being a single massive database, a digital twin city can actually consist of multiple networked systems. When it comes to combining data, the original location of the data does not matter as much as having access to the data via APIs. Functional APIs make it possible to combine different types of data in later processes.
As a result of the above, the building of a digital twin city often consists of the development of processes for combining data from multiple sources. If we imagine data as being the ‘fuel’ of a digital twin city, then the various automated data handling processes, or data pipelines, are the parts that make up its engine.
How is a digital twin city visible to the city’s residents?
The purpose of a city is to provide high-quality living environments and services for residents and operating environments for companies. With this in mind, a digital twin city cannot be built just for the fun of it or in pursuit of an abstract goal. Instead, the aim should be to find applications that also provide benefits for residents.
The first clear benefits that the building of a digital twin city can provide include various types of new planning tools and methods. Making various locations in the urban environment available for examination on the same systems can make it easier to identify problems, improve cooperation and thus improve the quality of the urban environment and operations. This can also make it easier to open up various participation and feedback channels for residents and facilitate the harmonisation of tools.
However, when it comes to benefits, it can sometimes be difficult to determine with any certainty which benefits stem from the digital twin city and which ones are the result of the underlying data architecture. After all, interoperable systems are both a prerequisite for the building of a digital twin and a mechanism by which its benefits are realised. Perhaps a digital twin city is but one manifestation of a greater development and the path towards functional city information.
Technology is ever developing, which also affects the definition and implementation of digital twin cities. New data sources appear and new platforms for building tools become available. What is next for the development of digital twin cities?
The data sources made available as a result of the digitalisation of transport are a good example of new data that are highly relevant to digital twin cities. Cars, utility vehicles and the numerous new mobility services used in the urban environment all generate data on their own operation and environments in addition to providing transportation. Examining the routes that their users take in an aggregated manner makes it possible to identify areas prone to congestion or find ideal locations for new services. However, it is currently unclear as to how much a digital twin city can expand in the context of mobility before it would make more sense to talk about something different – such as a digital twin for mobility.
Game engine technologies, VR headsets and augmented reality technologies open up new opportunities to use the data of digital twin cities on new platforms. This post has focused largely on the data-related aspects of digital twin cities – the combination of data and the prerequisites thereof. However, different visualisation technologies are also an interesting and necessary aspect of digital twin cities in terms of the applications offered to residents.
The social dimension – although the discussion surrounding digital twin cities is often quite technical, we should keep in mind that digital twin cities are also linked to various social processes. After all, it is the people and organisations composed of people who produce and use data in a city. The progress towards more detailed data about things like mobility inevitably also brings us closer to the boundaries of personal data and experiences. Perhaps in the future, digital twin cities will start to interact with the personal digital twins of city residents?