City Digital Twinning: A Step-by-Step Process

• December 8, 2022
• Infrastructure

A full-scale city digital twin (CDT) still seems to be a distant goal. Advancing toward this as-yet-unfulfilled objective requires us to be aware of the efforts and time demanded by the twinning process and to accept the discrepancies produced from a more realistic approach to twinning. Essentially, a CDT is a systematic solution that requires step-by-step experiments, updates, and optimizations in crucial components such as local and remote sensing, data repository, analytical algorithms, and data transmission. There are likely to be divergent approaches and applications at an early stage of enabling a full-scale CDT, a few of which will be covered in this review.

CDT not as a product, but a networked solution

CDT is a convenient concept. The fundamental emphasis on replicating the physical and creating an identical virtual twin makes it an easily understandable idea among different audiences. In the meantime, that convenience also yields a false understanding that there is a finalized, defined representation of a CDT: an operating system with user-friendly interfaces, visualized data information of charts, and a full-scale 3D model of the urban environment. That impression is evident in some commercial reports highlighting CDT as if it is a mature product and in scientific-fiction entertainment works that implicitly render the possibility of managing real cities through SimCity-like gaming programs.

The misconception of CDT is probably due to its dual identity. While CDT is undoubtedly a tool that enhances city planning, management, and governance, it is essentially a bundled system contingent upon a variety of sub-level applications, data repositories, and data transmission infrastructure. In that sense, there are three critical implications of CDT. Firstly, CDT is a representative term that pins down a tech-enabled “solution” but not a replicable “product” manufactured from a streamline. As a solution, the twinning process is a long-term effort that requires collaborative contributions from different stakeholders. Optimized data transmission and integration, accessible IoT devices and robust back-end systems, publicly accepted policies on security and privacy, and so forth are all structural pieces for a complete version of CDT.

Secondly, CDT may not necessarily look alike across different cities. Suppose CDT is ultimately a technological upgrading process that addresses local interests. In that case, the twinning process is when a city begins to select and incorporate technological innovations and policies tailored to local problems or priorities. For instance, shadow simulation through high-resolution building and environmental modeling were one of the early CDT attempts in Boston. Yet in dense cities like Singapore, where street-level greenery is considered a critical urban asset, the CDT agenda included projects to document all the trees digitally through lidar scanning.  Creating digital twins for trees is also done in smaller cities; Leoben, Austria also deployed private services to twin the trees in its city center. For a compact, historic city like Breda, the Netherlands, a CDT project may prioritize visualizing data related to public spaces, such as visitor flow, traffic, air quality, and noise levels.

Finally, the last implication is that many works contributing to the process of enabling CDT solutions may not match existing notions of CDT. As the previous two points have suggested, a CDT solution may consist of multiple parts. In fact, a great variety of tech-enabled projects that aim at public interests - be it experimental or infrastructural - may pave the road for a functional CDT solution in the future, so practitioners need to be open to building a CDT from multiple different blocks. For instance, Philadelphia’s SmartCityPHL team has launched projects such as SmartBlockPHL and Smart Loading Zones. The former deploys 14 sensor-equipped streetlights to collect real-time data related to pedestrian traffic, street activity, and weather in Midtown Village, and the latter regulates the use of street curbs through designated loading zones and a smartphone app. Indeed, neither of the two projects stands alone as a CDT example, but the two pilot projects are feasible solutions for local sensing and are collecting and processing real-time data. As the SmartCityPHL team is carrying out additional CDT components, these projects may represent early-stage infrastructural elements that possess the potential to be integrated into a more comprehensive CDT system.

Paving the road for the future

A similar situation occurred in Boulder, Colorado, where planners and GIS specialists worked together to test the capacities of 3D GIS tools in urban planning. The city of Boulder began its experiments with ArcGIS Urban (the "Urban") in 2019, but during the COVID-19 pandemic, Boulder’s planning team utilized Urban for several different projects. This web-based GIS tool facilitates scenario planning, impact assessment, and visualization, for the sub-community planning project in East Boulder. In conversations with Boulder’s planning department members, Kalani Pahoa, an urban designer from Boulder’s Comprehensive Planning division, and Jennifer Immich, a GIS analyst from Planning and Development Services, commended ArcGIS Urban’s improved capacities in scenario planning. Like other geo-mapping tools, ArcGIS Urban imports standard data, such as demographics, business analytics, carbon emission estimates, tree canopy estimates, and flooding risk area. With these data, the Urban could perform specific simulations in a 3D Boulder model. For instance, by joining 3D models, shadow analysis, and tree canopy, planners and analysts could project urban heat island effects and gain insights into locations that need more or fewer trees, parking spaces, or sidewalks. Besides those, the web-based tool may import local planning codes and visualize planning restrictions that may affect proposed changes in the city. Highlighting planning-related information enables the planning team to generate quickly simulated scenarios to demonstrate proposed urban interventions and potential impacts on the environment and local communities. All these characteristics have made ArcGIS Urban an impactful instrument for scenario planning and community engagement today. In the meantime, it would not be surprising to see the tool incorporate additional datasets and become exchangeable with information handled by other departments, such as transportation planning, urban pipelines, telecom facilities, topography, and natural resources. More data from local and remote sensing may potentially feed into the Urban and other departments’ software.

Rather than adopting emerging tools, San Jose takes different strategies. The city has an Information Technology division with comprehensive scopes and responsibilities and the Mayor’s Office of Technology and Innovation (MOTI) which leads public-private partnerships and pilot programs that address challenges to urban innovation. Home to many leading tech companies such as Cisco, PayPal, eBay, IBM, and so forth, San Jose has launched a series of collaborative initiatives with private actors to enhance its public services through data and emerging information and communication technology. Among the listed projects, two noticeable projects may contribute to the future efforts of digital twinning. The first project is San Jose’s 311 mobile platform. In 2016, San Jose hired AST Corporation to build the San Jose 311 app using the Oracle Service Cloud platform, which routes service requests to the corresponding city departments. The app has received both criticism and appreciation since its launch. While some praised the convenience of filing complaints and service requests, many left negative comments due to poor user experience of the product or slow responses from the city government. However, it is essential to point out that, at least, San Jose 311 was efficient at crowdsourcing citizen requests and complaints with precise geolocation information. Through continuous updates, the city-AST team may optimize user experiences by fixing app freeze, enhancing interface design, and improving app accessibility. These changes are likely to attract new users to the app. In the long run, this app still represents a straightforward and low-cost strategy to collect data related to 311 requests. More importantly, the collected data will become part of San Jose’s Spatial Data Repository and may be a component of other digital tools in the future.

Besides strengthening the city’s in-house tech capacity, San Jose also invested in empowering its residents’ capacity with IT and communication technology by closing the digital divide. By setting up an $18 million fund for the Digital Inclusion Partnership, San Jose is making significant progress on improving households’ Internet connectivity, device accessibility, and digital literacy. For instance, MOTI, in partnership with Revivn, a device refurbishment platform, and the California Emerging Technology Fund (CETF), created a new initiative that purchased new devices for low-income residents through profits gained from refurbishing donated devices. At the same time, the Digital Inclusion Fund has a long-term training program to improve digital literacy for San Jose’s residents (the city has also provided impact reports for the first round and the second round). The program provides training and technical support to improve residents’ ability to successfully utilize digital technologies to create, access, analyze and manage communication information. Building up public digital capacity is crucial for gradually preparing citizens to take full advantage of an urban environment that is increasingly connected with digital technology. It is impossible to justify the eventual success of a CDT if it leaves some citizens behind. And for work that inevitably takes time and effort, securing funding and planning long-term strategies are undoubtedly smart moves.

Building it with an agenda

Unlike the previously mentioned cities, Miami is a city where residents look at CDT with explicit expectations. Coral Gables, a city in Miami-Dade County and seven miles southwest of Downtown Miami, is one of a few US cities that launched an online platform following the CDT concept. Through Amazon Web Services (AWS) and online GIS tools, the city generated an interactive satellite map with different layers of public data - 3D building models, land management, city asset management, critical public work facilities, and more. One of the impressive characteristics of the web-based platform is that it performs as a readily accessible viewer of important spatial information for citizens who do not necessarily have experiences with GIS applications. The attention to non-professional users sets a sharp contrast to some complex CDT visions. In addition, it is important to note that the web-based CDT platform is not a stand-alone application, but part of the city’s Smart City Hub. This portal straightforwardly presents most subjects relevant to smart cities, such as open data, 3D geospatial analytics, IoT sensing, and e-governance.

However, some challenging work lies ahead. Horizontally integrating other functional components in the Smart City Hub with the web-based platform will be critical to avoid siloed datasets, and that integration process would require a continuous investment of time and effort. Further, given that the web-based platform is relatively slow in loading spatial data, incorporating and visualizing massive real-time data efficiently in a web-based environment would be a technical challenge. Conducting local sensing through cameras and other environment sensors while carefully ensuring cybersecurity and privacy protection would undoubtedly require efforts from different city departments. Indeed, these challenges are not unique to Coral Gables alone, but to any municipalities that strive to make cities smarter.

A long journey ahead

City-scale twinning takes work. The popularity of this concept also creates prominent roadblocks to building a CDT. On the one hand, there are technical challenges related to building digital infrastructure and applications - data collection (reality capturing), data storage, efficient data transmission and analytics, and more. On the other hand, deploying trustworthy, secure, and more budget-friendly methods to achieve the technical goals and to ensure for-public outcomes are new governance questions policymakers must address. Back-and-forth communications, negotiations, and even confrontations among relevant actors might be inevitable in this long journey of building an operating CDT for different urban contexts. For those reasons, cities may adopt divergent measures when working with emerging technologies.

Nevertheless, all of the abovementioned cities have demonstrated some common characteristics. First, all the cities have established cross-disciplinary departments or teams to oversee their smart cities projects. The dedicated institutional arrangements are one of the essential bedrocks for an evolving CDT solution. Second, even if specific projects might not look directly relevant to the CDT concept, most projects are equipped with secured and transparent data collection and storage approaches. That still creates opportunities for integration with other applications for digital governance.

In the long run, it is only when data between the tools and other departments’ operating systems become readily accessible to the authorized users that a city can enable a twinning process that keeps bringing together the physical and the digital and provides tangible benefits for residents.