In the last several years, the convergence of geospatial technology, building information modeling (BIM), and interactive 3D has driven a conversation about digital twins and how they may be used to simulate single facilities, entire cities, and even large natural systems. Digital twins are virtual representations of the real world including physical objects, processes, relationships, and behaviors.
GIS can be used to create digital twins of the natural and built environment. It can also be used to integrate many different digital representations of the real world, virtual models of real-world assets, or natural systems along with information models, data, reports, analyses, and user experiences intended to capture the state of a digital twin, monitor its performance, and predict future outcomes using it.
The concept of a digital twin originated in the product manufacturing industry. Precise digital models of complex objects, such as airplanes or cars, can be captured in a database for purposes of reporting, analysis, and eventually simulating and testing an object’s performance. The original digital twin concept helped move the use of data about an asset from using it strictly for finance and cost-accounting purposes to evaluating performance and analyzing operations. This enhanced data could then be used to inform into financial aspects of an asset’s manufacture and sales.
The digital twin concept is being applied to real-world infrastructure, buildings, and even for systems at scales as large as whole cities and natural environments. The idea that a model of fixed physical assets can be used to help understand performance, costs, and construction is compelling. Extending the digital twin concept to inhabited systems, is even more interesting and more complex.
GIS has been used to model real-world systems with high accuracy for years. Utility networks, legal parcel boundaries, and traffic networks have been represented in GIS for purposes of asset management, maintenance, and planning. The digital twin concept builds on older techniques and technologies for data warehousing, federated systems, and object-based data models by adding realism, an interactive user experience, and high-resolution 3D and 4D models of assets and systems. Ultimately, the digital twin must help the owner or operator of the real-world asset or system to solve business or mission-critical applications to justify the investment and process changes that will inform and maintain the twin.
Problems That Digital Twins Help Solve
Digital twins can address problems associated with the historical record of an asset, operational performance monitoring, and testing or predicting the future performance of an asset.
A digital twin can be used to archive a state of an asset or system for a specific time. Parcel boundaries and utility network models are good examples of geospatial twins of real-world systems that can be captured for legal and operational purposes. Reality capture, such as the point cloud and textured mesh output of SURE for ArcGIS, can serve as a high resolution, 3D snapshot of the real world. From these snapshots, some vendors can even extract BIM data that can be used as either historical record or as a starting point for future renovation or operations.
Operational Performance Monitoring
The 3D experiences furnished by a 3D virtual model of an asset can provide a consumer-level interaction and exploration experiences. Users can look at the model and see the real world. For that reason, many digital twin projects use the twin as a dashboard showing current performance of the actual system, often with live feeds and dynamically updated attributes. These systems are often linked to other, federated enterprise systems. One of the biggest benefits that GIS brings to the digital twin is that GIS can store, stream, and provide dynamic experiences to explore 3D assets as small as a building or as large as a city, all in the context of the built and natural world around the assets.
Testing or Predicting Future Performance
Some of the highest expectations surrounding the use of digital twins are based on the use of high-precision versions of an asset that are used to simulate and predict future performance or behavior. In the automotive industry, digital twins can be used to explore aerodynamics of cars. In the Architecture, Engineering and Construction (AEC) industry, aggregated BIM information is used in the practice of virtual design and construction. A digital twin of planned construction is used for detecting possible safety issues or construction conflicts. At the scale of a city, planners want to simulate future change, such as the shape of a new building, changes in a planned highway, or improvements to seawalls. At the scale of a city, the hope with digital twins is that simulation and analysis of future changes can be used to understand and optimize the impact of changes while minimizing the costs of implementation.
Is GIS Necessary for a Digital Twin?
Any digital twin of a fixed asset or real-world system benefits directly from the inclusion of GIS data about the asset as well as the GIS context of the asset. Not only can GIS be used to create digital twins of the natural and built environment, but it can also be used to integrate many different digital representations of the real world.
For single objects in the mechanical world, an entire object can be represented in a self-contained data schema that can be used for observation and hypothesis testing. For fixed physical assets or natural systems, data about soils, asset materials, weather, traffic, maintenance, and utilities can be stored in different data models with diverse data quality, resolution, and ownership. GIS is the only technology that provides a simple key—location—that allows complex analysis of such diverse data models and datasets.
In the last few years, Esri has invested in ArcGIS to dramatically improve its capabilities, turning GIS into the ideal technology for bringing together federated datasets behind dashboards, ArcGIS StoryMaps stories, and 2D, 3D, and 4D maps for facilitating communication, observation, and analysis of complex systems. The more data that is added to GIS, the more benefits that GIS provides.
GIS data also powers many simulations of real-world dynamics and behaviors. For simple analyses, such as investigating the shadow impact of a planned structure, 3D GIS provides dynamic, easy-to-use experiences in a web browser. For complex simulations, advanced geoprocessing workflows may be used to simulate changes in large utility networks and then to see those changes in a simple dashboard.
Partner solutions extend ArcGIS capabilities. Simulating airflow through a major urban center or modeling the effects of a power outage in a major utility are just two examples of these partner solutions. The demand for the use of GIS content in game engines led Esri to release the ArcGIS Maps SDKs for Unity and Unreal Engine plug-ins in 2020.
What Determines the Scope of a Digital Twin Project?
Like any complex system design, designing a digital twin project requires identifying criteria that will help limit scope while also supporting longer-term maintainability. Given the diverse uses for digital twins, some of the considerations that will help identify the scope and requirements include:
- Time scale
- Stakeholder diversity
- Systems complexity
- Data ownership
- Data security
- Time Scale
Will the digital twin used for a single project, or will it be used throughout the life of an asset? For example, in virtual design and construction (VDC) practice, the digital twin is typically used only for the life of a project, and it is not guaranteed that the twin will be usable for operations of assets.
Will the people working on or dependent on a digital twin be working on a single project team or be drawn from diverse ecosystem of participants from multiple domains?
The complexity of a digital twin will increase—probably exponentially—with the number of systems that are modeled. This is true for building systems, such as plumbing, electrical, mechanical, security, and telecommunications, and even more true for campuses and cities. To help limit complexity, the number of systems modeled in a digital twin should likely be focused on the problems the twin will be used to solve.
Data ownership drives both access rights to data and opportunity for connectivity between datasets. Owners of a facility, campus, or portfolio of commercial buildings may find that they have good control over available data and the ability to set requirements for incoming datasets. Operators at the scale of a college campus or city may struggle to assemble a digital twin that addresses their planned applications if not all utilities, facilities, or other systems that impact their operations are under their control.
The security of a system, such as a digital twin, will be determined by the requirements of the most secure dataset in the system. Just because a highly secure dataset could be included in a digital twin doesn’t mean that it should be included if access restrictions will end up limiting stakeholder requirements or other capabilities of the digital twin.
A digital twin is a virtual model. In a scientific context, a model is used to either test hypotheses or predict future states of systems. Scientific models are typically tailored to test specific elements of the system and often can’t be stretched to test or predict scenarios that weren’t considered when building the model. When building a digital twin project, it’s worth considering that success may depend on limiting its scope to achievable outcomes, not expanding its scope to solve every future problem.
Elements That Make a Digital Twin Successful
Many of the elements that go into a successful digital twin project are the same elements that drive successful IT implementation projects in many industries. Fundamentally, a digital twin is deeply dependent on the ability to connect data and create user experiences that help solve problems and increase understanding.
Problematic domains and requirements for reporting and analysis directly drive the data requirements and experiences that will be built for the digital twin. Specifying a clear set of desired outcomes, reports, and analyses up front will help make a digital twin more achievable. No technology implementation is static. Future iterations of a digital twin can incorporate more requirements.
A Design Thinking Approach
A deep understanding of how a digital twin will be used to support existing business practices helps inform and design experiences so that the digital twin can be used to get work done. Using a digital twin as a dashboard view into real-time systems will provide a different user experience than an extended reality (XR)-based planning and simulation tool to explore the effects of future changes. Experiences can only be understood by working with engaged stakeholders throughout the planning and design of the twin.
Clear Requirements for Information Architecture
The owner of the digital twin needs to understand—deeply—how the digital twin will be used and ask that data and services be delivered according to specifications that meet those needs. This may require extra up-front investment, but it is the only way to reliably obtain standardized, quality data to feed the digital twin.
Open Data Standards and APIs
Because challenges with data and systems integration make up a large portion of the problem set associated with creating a digital twin, customers need to specify open standards that will allow them to connect to data from many different applications reliably and sustainably. This is true for BIM models delivered according to an industry-standard file type, and it’s also true for real-time data feeds that may be accessed through an open REST API.
Standardized Authentication and Licensing
If a digital twin will be used by a small team, access and authentication may not be a huge issue. If a digital twin will be used by many collaborators and stakeholders across an organization, such as a city, requirements must specify how federated datasets will be accessed using standardized authentication and identity patterns. If a common access card (CAC) card will be needed, make sure everyone knows that at the beginning of the project. While ArcGIS Enterprise standard licensing is great for sharing within an organization, ArcGIS Hub Premium licensing lets an organization share content with people outside the organization.
Geospatial Digital Twins in Action
Right now, ArcGIS customers can combine reality capture; 3D, 2D, and planimetric data; and real-time feeds in dynamic, interactive experiences that describe individual facilities, large systems, or entire cities. Geospatial digital twins, built with ArcGIS, can be explored on mobile devices, in a web browser, or through rich desktop applications from Esri and its partners. The geodatabase portion of a digital twin, stored in ArcGIS Online or ArcGIS Enterprise, is accessible through a wide variety of open standard APIs, services, and data formats that facilitate exchange and integration with other enterprise systems such as IBM Maximo or Autodesk BIM 360.
Many Esri customers have benefited from the current capabilities of geospatial digital twins.
Amsterdam Airport Schiphol has built a dynamic, digital twin of its airport premises that incorporates open standard BIM data through Safe Software’s FME integration of ArcGIS with live data feeds for aircraft and ground vehicle locations.
HNTB Corporation, an American infrastructure design firm and Esri partner, is using innovative web-based 3D ArcGIS apps to present time and cost visualizations for construction projects. Its Long Island Railroad project allows teams to visualize project progress and even run future work scenarios that explore the impacts of schedule and budget changes.
Hartsfield-Jackson Atlanta International Airport built a digital twin using CAD, BIM data, GIS, and information about the surrounding environment that informs wayfinding and equipment placement, hazard analysis, and space management.
Hazen and Sawyer, an environmental engineering firm, helps customers deliver safe drinking water and limit water pollution. It uses GIS as the aggregator for BIM data, reality capture, and systems information for entire water treatment plants through a dashboard view into the operation and maintenance status of facilities.
The Future of Digital Twins
GIS has played a central role in helping customers model, analyze, and observe their assets and systems for decades. New technologies, such as game engines and real-time data feeds, have added exciting possibilities to create richer interactive, experiences for users to explore, analyze, and experience their assets and the world around them.
Esri is investing heavily in reality capture, BIM integration, buildings system integration, the analysis of the Internet of Things (IoT) data, and related technologies and tools that will be used for creating the next great digital twins with ArcGIS.
Visit the Esri Digital Twin website to keep up with developments in the use of GIS for creating digital twins.