It is no newsflash that governments at all levels are using data more to improve public policy and meet both the increasingly complex challenges they face and the expectations of a more data literate business sector and citizenry. Much of this data comes from what might be termed traditional sources – official statistics, surveys and censuses, etc. But, as a recent Organisation for Economic Co-operation and Development (OECD) report on government innovation identified, governments are increasingly drawing on non-traditional data sources to help design and implement better services. They are also using experimental tools to help navigate difficult and unpredictable environments. One popular tool utilising non-traditional data sources the OECD highlights is simulations known as a digital twin.
Let’s look at digital twin technology in the context of one of its common uses, smart city initiatives, and the data challenges involved in these. As part of this, I want to discuss how CKAN – the Comprehensive Knowledge Archive Network – an open source software with a global reputation for building management solutions, can help to support digital twin initiatives by ensuring standardised data management and seamless real time information sharing across the complex collaborative data ecosystems necessary for these to function.
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What is a smart city?
The European Union defines a smart city as an urban space where ‘traditional networks and services are made more efficient with the use of digital solutions for the benefit of its inhabitants and business’. The concept came into being in the late 1990s and is still evolving and has no universal definition.
Data is gathered by sensors, cameras, smart metres and geospatial technology devices installed throughout the city. It is then integrated with advanced data analytics and, increasingly, artificial intelligence to enable more informed and responsive decision making in relation to the efficiency, liveability and sustainability of built-up urban spaces. Crucially, data is also fed back to residents and city workers via websites and apps that can enable them to do everything from report power outages and accidents, to paying fines and receiving news of cancelled services.
Smart city technology has many uses: monitoring traffic and pollution levels, smart lighting and heating, alerting council workers to when a public waste bin needs to be emptied, and motion sensors to help the elderly safely cross a road. Some smart city models can detect when a car passes a toll booth and deduct a toll from a user’s account and even allow for a variable toll to be assessed based on traffic conditions. Smart parking-metres can communicate with a central server and then to a user app to advise a driver when a parking space is available and guide them to it.
Singapore, Japan and Spain utilise smart city technology to improve traffic flow and public transport by combining a central system for traffic light controls with sensors for detecting delays or the amount of traffic at a particular intersection. The system can adjust the timing of lights, buses and emergency vehicles, depending on how well traffic is flowing, or suggest alternative routes. South Korea has adopted the technology to monitor pedestrian safely and mobility. Amsterdam has focused on smart waste management and sustainability.
The digital twin: an old idea given a high tech makeover
The digital twin takes an idea with a lengthy history and gives it a digital makeover. That is, in the words of the American Planning Association, ‘when most industries set out to develop a new product or solve a problem, they rely on using laboratories, testbeds, or other workspaces to refine and optimize their ideas.’ While engineers, construction companies and town planners have long used computer generated designs to plan and create projects, smart city technologies can now deliver real time data to build a simulated virtual interactive model of an entire city on which to test policy alternatives and future scenarios.
According to the American Planning Association, a smart city digital twin depends on the various data layers that build on top of each other, including data on topographical features, buildings and infrastructure, and the movement of people and traffic, etc. ‘The digital twin uses the data generated in the virtual smart city layer to perform additional simulations; this information is fed back through the layers of the model, where it can be implemented in the physical world.’ Also crucial to the digital twin model is a range of other technologies that have been increasing in sophistication: cloud computing, artificial intelligence, and 3D geospatial mapping and immersive visualisation, such as virtual and augmented reality.
Where can digital twins help most?
A digital twin enables urban planners, designers and engineers to measure the potential impact of changes before they are implemented and improve or modify their plans. Incorporating continuous real-time data into evaluation, modelling and planning also enables planners and policy makers reduce time lags associated with conventional feedback loops and learn quicker from mistakes.
Other potential benefits cited in the OECD report include facilitating improved stakeholder collaboration, and better services, whether this is improved traffic flow, or smarter heating, ventilation and air conditioning, leading to reduced carbon emissions. Ideally, smart city digital twin technology can also facilitate better communication between residents and city authorities and promote public participation in urban planning and decision-making.
How CKAN can help meet the challenges of the smart city digital twin approach
Alongside their benefits, smart city digital twin technology presents significant challenges. This includes maintaining public trust in the face of increased use of surveillance technology, such as CCTV cameras and facial recognition technology. This public trust factor is connected to what some refer to as ‘obtaining a social license’ by ensuring that data re-use initiatives are transparent and create public good.
There are also significant data challenges in maintaining a digital representation of a physical urban space. Large amounts of data are collected from a range of different digital technologies. It comes in different formats, including both geospatial and real-time data. And the data needs to be securely stored and properly organised so it can be analysed and shared between multiple stakeholders.
Success stories of Digital Twin with CKAN
Let’s focus on two examples where CKAN has successfully been used to meet the data challenges of smart city digital twin projects.
The city of Munich
The first is the digital twin for the city of Munich. A 2022 paper by researchers at the School of Engineering and Design at the Technical University of Munich, notes one traffic-related project alone generates a wide range of digital resources, in different data formats and maintained by different municipal departments.
‘This includes city models (.citygml), topographic data (.gdb, .shp), site plans (.pdf, .dwg), meshes (.obj), point clouds (.las), visualization formats (.czml, .udatasmith) but also simulation results (.csv, .xml). Realtime data is provided by sensor Web services (implementations of the OGC SensorThings API). In addition, with applications using this data as well as a number of different stakeholders, this results in a complex and distributed UDT [urban digital twin].’
CKAN is used as the foundation for the extensive catalogue that securely manages the data. These resources are shared on the City of Munich open data portal, also powered by CKAN, with the aim of improving public participation. As part of the Connected Urban Twins project, the city of Munich is also collaborating on digital twin technology with Hamburg and Leipzig, an initiative which also uses CKAN.
The province of Bavaria
The second example is an initiative overseen by the State Ministry for Digital Affairs in the southeast German province of Bavaria, to support 17 municipalities to create digital twins. The project seeks to enable municipalities that lack the skills and resources, to develop and maintain their own smart city digital twin infrastructure and utilise it to plan and implement urban projects faster and at reduced cost.
Municipalities were given the option of setting up their own cloud storage catalogue or using a central data infrastructure developed by the Technical University of Munich and powered by CKAN. All 17 chose to go with the centrally provided instance. The central catalogue uses a version of the CKAN software prepared for deployment in a Kubernetes cluster – an open source platform for managing containerised workloads and services. The abilities of the main CKAN instance are further enhanced through extensions, including for managing geospatial datasets and metadata. The instance is also configured to enable the setting of different access rights for users.
CKAN can support smart city digital twin initiatives in many ways.
- Interoperability and seamless communication between the various digital twin technology components. In Madrid, for instance, CKAN has been used to share a large quantity of parking data from different sources in a machine readable format as Linked Open Data, which the urban twin then utilises for traffic management and real time parking updates.
- Metadata management and interoperability. CKAN can be used to create and manage a centralised and comprehensive descriptive record of data that goes far beyond collecting basic metadata, making data more discoverable. CKAN can also be used to catalogue diverse datasets and provide a centralised platform for stakeholders to analyse shared datasets, crucial for collaboration across diverse digital twin systems and stakeholders.
- Real-time data integration can ensure that planners, policy makers and other stakeholders access up to date digital twin data for faster, improved decision making.
- Enhanced security, including the ability to limit access to selected datasets by setting group and individual permissions. It also allows for version control and audit trails, so any changes can be tracked, and data accountability and traceability are enabled.
The fact that CKAN is open source and easily used means organisations can avoid license fees and vendor lock-in. This makes it more accessible and cost-effective, especially for small to medium-sized smart city digital twin projects. This is an important consideration, given that globally municipal city and national government funding remain the primary sources of funding for smart city digital twin projects.
Conclusion
Increasing urbanisation and the challenges of making cities more liveable, coupled with advances in digital technologies, mean smart city digital twin initiatives will continue to grow in popularity across the globe. CKAN is already supporting governments in this area by ensuring the standardised management of complex and diverse datasets from non-traditional sources, seamless information sharing and real time interoperability across the various stakeholders involved and the collaborative data ecosystems necessary for them to function.
