Urban digital twins are rapidly becoming integrated into infrastructure management, urban policy, mobility, climate adaptation, and municipal public service delivery, yet contemporary cities cannot be treated as stable objects that are capable of being fully captured within static computerized models. In a perspective article published in Frontiers in Sustainable Cities, urban planning researchers Abdulaziz I. Almulhim of Imam Abdulrahman Bin Faisal University in Saudi Arabia and Michael Batty of the Centre for Advanced Spatial Analysis at University College London argue that municipal decision-makers must conceptualize urban digital twinning as an ongoing institutional planning practice rather than merely a configured piece of technology. As metropolitan governments increasingly invest in advanced computational platforms to manage complex urban systems, the rapid operational adoption of these technologies has generated widespread enthusiasm across research and professional practice. However, this implementation has simultaneously exposed a fundamental conceptual question that extends far beyond computational capabilities, requiring urban authorities to recognize that digital models must continually evolve alongside the dynamic, unpredictable environments they are constructed to support.
To understand the structural limitations of urban models, the authors trace the origin of the digital twin concept back to its historical emergence within engineering and manufacturing environments, referencing foundational work by Michael Grieves and earlier analyses by Batty. In industrial and manufacturing domains, digital twins were originally developed to represent clearly defined physical assets that possessed explicit physical boundaries, predictable operational functions, and tightly controlled engineering objectives. While the technical definition continues to evolve rapidly across various computational disciplines, these engineering origins continue to exert a strong influence on how digital twins are conceptualized in urban domains. However, Almulhim and Batty emphasize that municipal territories differ fundamentally from manufactured machinery or engineered physical assets. Cities operate as open sociotechnical systems where physical environments interact continuously with formal institutions, complex governance arrangements, shifting economic activities, wider ecological processes, long-term policymaking processes, and the daily activities of diverse human communities, meaning they cannot be reduced to closed engineering systems.
Because cities are constantly reshaped through political priorities, municipal planning decisions, public participation, and unforeseen social or economic disruptions, digital representations face distinct operational constraints. Drawing on prior analyses of urban modeling, the researchers explain that a city digital twin cannot function as an exact mirror or complete replica of urban reality. Every digital model necessarily reflects deliberate choices made by its creators regarding system boundaries, selected datasets, underlying assumptions, institutional priorities, and intended analytical purposes. Consequently, the primary value of an urban digital twin lies not in an impossible attempt to reproduce physical reality perfectly, but in providing structured analytical support for urban planning and public decision-making under conditions of pervasive uncertainty. The central challenge confronting urban planners is therefore not deciding whether a digital twin provides an absolute, perfect representation of a city at a single moment in time, but understanding how that representation is continually produced, interpreted, revised, and utilized across evolving municipal planning processes.
The perspective paper outlines how scholarship surrounding digital twins has matured beyond treating computational platforms as static representations. More recent academic work, such as analysis by Wagg and colleagues, describes digital twins as time-evolving computational systems that maintain a dynamic two-way coupling between physical assets and their digital counterparts. Similarly, research led by Goodchild emphasizes that urban digital twins are designed to support forecasting and decision-making while explicitly recognizing the uncertainty inherent in complex urban systems. These developments have also challenged the conventional assumption that a single, universal definition of a digital twin can be applied across all scientific and operational domains. Citing conceptual frameworks developed by Agrawal and Fischer, the authors note that the practical meaning of both the digital model and the twinning process depends directly on the specific use case, operational context, boundary definitions, analytical capabilities, and physical entities being twinned, requiring practitioners to address the core questions of what is digital and what is being twinned.
Building upon recent urban research, Almulhim and Batty highlight that digital twinning has increasingly been conceptualized as a broader sociotechnical phenomenon rather than an isolated software system. Citing work by Calzati and van Loenen, as well as contributions by Solman and colleagues, the researchers note that urban digital twinning functions as a contextual, iterative, and participatory process that links technical systems with everyday urban development. Furthermore, recent conceptual scholarship has emphasized that digital twinning is never a purely technical activity; it fundamentally involves subjective interpretation, shifting operational assumptions, and human judgment. As digital models become more deeply integrated into administrative governance, understanding the boundaries of digital knowledge, transparency, and model limitations becomes essential, as highlighted in studies by Borbach and colleagues. Correspondingly, research by van der Meer and co-authors suggests that mature urban digital twins must look beyond computational sophistication and automated decision-making by placing professional human judgment and civic participation at the center of planning practice.
To formalize this conceptual shift, Almulhim and Batty propose a structured framework that differentiates the digital twin from the continuing practice of digital twinning. In their framework, illustrated conceptually in the study, the digital twin does not disappear; rather, it remains positioned as one specific representational and analytical component embedded within a broader administrative workflow. The digital twin itself is defined as a configured computational system that represents selected aspects of the urban environment and maintains a two-way coupling with physical systems to support monitoring, simulation, and predictive analytics. In contrast, urban digital twinning is conceptualized as the continuing institutional planning practice through which those digital representations are continuously interpreted, combined with professional expertise and institutional memory, and translated into concrete urban interventions. Through this sustained institutional engagement across repeated planning cycles, computerized digital models move beyond being passive visual repositories and acquire authentic public value and administrative relevance.
From this operational perspective, urban digital twinning encompasses an extensive range of administrative and professional tasks that extend far beyond building and maintaining software models. These ongoing responsibilities include defining and continually revising system boundaries, integrating heterogeneous datasets from diverse public and private sources, maintaining technical interoperability across legacy information systems, and updating digital representations as urban landscapes shift. Furthermore, the practice requires planners to interpret analytical outputs critically, incorporate specialized professional expertise, integrate local experiential knowledge from resident communities, negotiate competing municipal priorities, and inform strategic policy decisions. Planners must then monitor the real-world implementation of those policies and iteratively revise both the underlying digital models and subsequent urban interventions over successive planning cycles. The authors assert that these organizational tasks are not auxiliary or secondary activities, but rather constitute the core planning labor required to keep digital models meaningful as urban conditions evolve over time.
The necessity of treating digital twinning as an institutional practice stems directly from the intrinsic limitations of digital representation. Drawing on findings by Borbach and colleagues, along with work by Das and co-authors, the authors explain that metropolitan environments can never be entirely digitized because they are continually transformed by social relations, institutional frameworks, shifting political priorities, and locally situated community knowledge that naturally exceed complete computational capture. More specifically, urban evolution emerges from a continuous interplay between emergent, often unintended patterns generated by complex social interactions—as detailed in urban complexity studies by Bettencourt—and deliberate institutional rules established by public authorities, as described by Moroni. Although public rules establish the legal and administrative framework within which daily urban activities unfold, they do not fully dictate or determine the resulting urban order, making precise long-term trajectories difficult to forecast and reinforcing the perpetual necessity of human interpretation.
Because the scope of what can be digitally captured continues to change as new sensor and computing technologies emerge, the authors stress that urban digital twinning must focus on ongoing institutional adaptation rather than the futile pursuit of total computational control or perfect reality replication. Digital models remain inherently partial, selective, and purpose-specific, requiring regular reassessment as municipal priorities and community conditions evolve. Within this framework, uncertainty is recognized as an enduring, unavoidable characteristic of urban planning that cannot simply be engineered away through larger data volumes or more complicated algorithms. Drawing on planning theory from Moroni and Chiffi, the authors explain that uncertainty manifests differently across municipal sectors: infrastructure planning involves uncertainties regarding public facility demands, system capacity, and engineering performance, whereas regulatory planning governing private land and buildings involves open-ended uncertainties stemming from unpredictable private property owners and market actors.
Consequently, digital twinning must fulfill distinct operational roles across different municipal planning contexts while explicitly resisting the impulse to automate public governance entirely. In infrastructure planning, digital twinning facilitates the iterative testing of demand assumptions, performance simulations, and investment scenarios; in regulatory planning, its role centers on monitoring how legal rules operate in practice, interpreting emerging physical patterns, and supporting institutional revisions of zoning and regulatory frameworks. Across both spheres, digital twinning functions to augment planning judgment and institutional learning rather than displacing human discretion. Almulhim and Batty emphasize that while routine administrative data streams may be automated, strategic civic decisions regarding land use, capital allocation, housing developments, climate resilience, and transit networks require professional accountability, democratic deliberation, and political judgment, ensuring that modeling choices, boundary selections, and planning assumptions remain transparent, contestable, and open to continuous public scrutiny.
Concluding their perspective, the authors articulate a comprehensive research, policy, and practice agenda intended to reorient future urban digital initiatives. For urban researchers, they recommend shifting academic inquiry away from solely evaluating the computational accuracy, processing speed, and real-time connectivity of digital models, urging scholars instead to investigate how digital twinning influences institutional learning, administrative transparency, civic participation, and adaptive policy adjustments over time. For urban policymakers, they advise municipal leaders to direct capital investments beyond proprietary software platforms and server infrastructure toward the institutional capabilities needed to sustain digital twinning across administrative departments. Finally, for urban practitioners, Almulhim and Batty urge public agencies and planning organizations to treat digital twinning not as an isolated, one-off IT project with fixed technical specifications, but as an ongoing organizational capability co-created alongside infrastructure operators, community stakeholders, and citizens to sustain responsive public governance.
Subject of Research: Conceptualizing urban digital twinning as an ongoing institutional planning practice
Article Title: Urban digital twins and digital twinning as a continuing planning practice
Article References: Almulhim, A. I., & Batty, M. (2026). Urban digital twins and digital twinning as a continuing planning practice. Frontiers in Sustainable Cities, 8, Article 2011125. https://doi.org/10.3389/frsc.2026.2011125
Image Credits: AI Generated
DOI: 10.3389/frsc.2026.2011125
Keywords: urban digital twins, digital twinning, urban planning, urban governance, institutional learning, adaptive planning, sociotechnical systems, infrastructure planning, Urban, digital, twins, twinning
News Source: Mallory Mcbride. (November 7, 2026). Urban Digital Twins Require Ongoing Institutional Practice Rather Than Pure Automation. Scienmag.
