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This essay is an excerpt of a paper published in the journal Dialogues in Urban Research in 2024. Read the full article here.

References:

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Visualising human life in volumetric cities

Gillian Rose

The volumetric regime and the city as an object

According to Gaboury (2021: 12), computer graphics assume that ‘the world is understood as a relational system of objects capable of discrete forms of interaction’. Moreover, it is assumed in both academic and commercial descriptions of digital twins that the city that is to be twinned is itself a physical object. Terms frequently used in the literature to refer to the twinned city include the real (physical) world, existing urban structures, the physical fabric, the built physical realm, a real/physical system, a real-world physical counterpart, the physical city or, simply, the real world. The city to be twinned is ‘a real world (physical space)’ (Kikuchi et al., 2022: 838). It is not surprising then that CDTs convert city environments into volumetrically-defined objects, since this aligns with both software affordances and imaginary conceptualisations: both concur that geometric objects can accurately represent a city.

The mode of conversion speaks to Rocha and Snelting’s emphasis on computational optimisation as part of what enables volumetric models to circulate. They point to ‘a particular way to arrange volumetrics in the interest of optimized computation, including drawing hyper-real surfaces on top of extremely simplified structures’ (Rocha and Snelting, 2022a: 221). This describes exactly the basic process of generating three-dimensional onscreen images, including CDTs, as also described by Gaboury and detailed in CDT scholarship (and also visualised in the thousands of breakdown videos showing the creation of digital visual effects in movies as a series of layers appearing over a massified version of the scene). A CDT is built from a 2D mesh of points, lines and polygons, generated from satellite data or aerial photography, or from existing datasets such as Open Streetmap or national land surveys. Elevation data is added (generated from aerial photography too, usually, or Lidar scans), this becomes a 3D wireframe model. Many CDTs do then drape ‘hyper-real surfaces’ over such simplified structures (see for example Helsinki City and kira-digi, 2019). Other objects are then added: buildings, road, infrastructure and so on. Digital twins assemble cities such that each ‘is an aggregate of smaller-scale objects like buildings, streets, trains, buses, cars, each of which could feasibly have their own Digital Twin, each itself a complex aggregate of further sub-components’ (Dawkins et al., 2018: 2). Various techniques establish the CDTs thus composed as accurate representations of the real urban environment.

These volumes are tethered to the material world using latitude and longitude or a location identified by a Global Positioning System, which is also understood as making them locationally accurate. According to a user guide to CityGML (an open standardised format for CDTs), ‘coordinates always have to be given with respect to a coordinate reference system that relates them unambiguously with a specific position on the Earth’ (Heazel, 2021). This process is ‘the construction of a reliable algorithm for translating between representation and reality’ (Kurgan, 2013: 13), and it depends on and reiterates long histories of hegemonic cartographic practice: ‘geographic information has largely been defined and implemented in a manner that constitutes independent entities with intensive properties, contextually indexed by location in an absolute space, and known objectively through a geographic gaze’ (Bergmann, 2016: 973).

A consequence of the assumption of absolute space is that the space of the twin must be unified and singular: it must be complete. There are no ruptures, gaps or folds: a CDT shows the city as ‘a single fabric’ (Yossef Ravid and Aharon-Gutman, 2023: 1455). Much of the technical literature on twins is precisely about fixing gaps or discrepancies in spatial organisation of a twin. Conflicts between different data sources must be resolved, either by manual checks or by automated edits (Argota Sánchez-Vaquerizo, 2022). All of the data must be consistently georeferenced, using the same system of co-ordinates. Photographic or Lidar data about surface textures and qualities must be correctly located, often by using ground control points which must themselves be carefully calculated for locational accuracy. Any inconsistencies in geometries must be fixed: for example, inaccurate occlusions among objects must be mended (Kikuchi et al., 2022), gaps and overlaps between buildings removed (Scalas et al., 2022), conflicting descriptions of road junctions resolved (Argota Sánchez-Vaquerizo, 2022). Distinct objects must be correctly identified from LiDAR point clouds (Xue et al., 2020). This spatial organisation enables a complete view.

The human affiliates that are pictured as the inhabitants of a CDT are also constituted as digital objects located in the twin’s digital space. They can thus be described as one ‘subasset’ among others: ‘a city can be thus considered as an asset that integrates different subassets such as buildings, utilities, transportation infrastructure, and people’ (Lu et al., 2020: 4). The CityGML user guide has a list of ‘prototypic objects’ which includes street furniture, traffic signals, vegetation objects, cars and ‘people and animals’ (Heazel, 2021). The human inhabitants of CDTs can be procedurally generated as visual content that follows rules related to 3D volumetric objects, so that for example feet have to stay on roads and pavements, and bodies can’t pass through each other; an Esri CityEngine demo video shows a yellow line added to a road in order to generate images of cars, and another yellow line added to a pavement to generate pedestrians (CityEngine User Meeting, 2022). Some CDTs also host agent-based models, especially of the movement of crowds. In both cases, humans are ‘individual mobility objects’ (Lee et al., 2022: 2), and can be substituted by various modes of transport: ‘Although topography, buildings, and roads are the main components of urban spaces, the most important factor therein is people. Vehicles, bicycles, and motorcycles that people use daily for transportation are the main elements comprising a city’ (Lee et al., 2022: 3; Papyshev and Yarime, 2021). Humans pictured in CDTs are thus computationally-generated objects equivalent to all the other moving objects in a twin (Figure 4).
Figure 4. Two screenshots showing the generation of cars and pedestrians in CityEngine (CityEngine User Meeting, 2022).
CDTs are constituted as objective in a double sense, then. They turn cities and their inhabitants into objects, and their imaginary assumes that this is appropriate because cities are indeed objects. Attention is then focussed on the techniques understood as accurately converting those material objects into digital objects. Such claims to know the world by converting it into Cartesian coordinates in two or three dimensions have long been associated with the power of the modern Western state and capital; there are extensive accounts of the complex imbrication of cartographic geometric abstraction with colonialism. ‘Economy and state have been intricately intertwined historically in the production of abstract spaces (of commodities, of private property, of state administration, of judicial power, among others)’ (Pickles, 2003: 153; see also Bier, 2022). The next section turns to the visuality of CDTs to unpack their particular patriarchal power in more detail.

City digital twins and seeing like a god

Volumetric CDTs render the twinned city fully transparent: visually as well as spatially unitary and coherent. There are no visual blanks or ruptures in twins. This transparency is argued to offer a more comprehensive view of city locations that enables better decision-making: ‘digital reality allows the user to navigate the city and toggle between different perspectives (e.g. birds’-eye view, first/third-person view) as well as change their scale. The possibility of interacting with the digital twin city increases situational awareness and can lead to better data-driven discoveries’ (Ketzler et al., 2020: 562–563; and see Mohammadi and Taylor, 2017). ‘A 2D display is sometimes convenient, but does not often provide a complete picture of the observed area’ (Komadina and Mihajlović, 2022: 140). A similar claim is made by the outreach lead of the UK’s National Digital Twin programme: ‘when you start to connect the datasets from these digital twins, you can build a bird’s eye view of a city, which gives you better information about the consequences of your decisions’ (Frearson, 2021). If a twin risks being ‘a blurry mess’ (Pimentel, 2020), it is simplified. If draping terrains and buildings with photographic-like imagery is too computationally demanding to achieve – or perhaps deemed superfluous to the aim of a particular CDT – it is abandoned, the level of detail is reduced, and instead buildings are textured as simple volumetric blocks with little or no surface detail.

The user of a CDT is affiliated visually with this accurate way of seeing by being aligned with a highly mobile point of view. The point of view of the user in promotional animations is the same as the position of what the software terms a ‘virtual camera’ in the twin’s geometric space, and the camera is itself situated in, and able to move through, the twin’s three-dimensional computed space. The camera’s position generates the viewpoint, and what is seen is rendered visible according to norms of geometric perspective. Almost without exception, city digital twins have a mobile aerial view as default. Proposals for new buildings can be seen from ground level but users can always then fly. Indeed, promotional materials for digital twins display relentless visual mobility; their point of view is constantly on the move. The virtual camera software that provides the point of view can pan across a model. Or the camera may remain steady and the user of a digital twin can rotate the twin-object around onscreen to look at from different angles. Unlike the CDT inhabitants, chained to the twin’s terrain, the CDT user is configured ‘doing away with horizons, suspending vanishing points, seamlessly varying distance, unchaining the camera and transporting the observer’ (Elsaesser, 2013: 237) (which description was written as part of a discussion of 3D cinema).

That spatially mobile gaze can also zoom, and the zoom enables visual mobility through things that would be impossible in the material world. This zoom gives users of the CDT a visual perception that can penetrate an object’s surface. A CDT is seen as ‘a holographic, high-definition, high-precision urban digital space, covering ground and underground, indoor and outdoor, and two-dimensional and three-dimensional structured entities’ (Deng et al., 2021: 127). Some city zooms pass through the exterior walls of buildings to picture interior rooms, or the infrastructure embedded within walls, ducts or tunnels; subterranean infrastructure is also rendered visible (particularly in CDTs that include Building Information Models). In some animations, the camera can look underneath digital twins which float in space, to be inspected from any direction. As a report by the consultancy Arup says, ‘we can now render our geometric designs virtually, in breathtaking fidelity, including previously unfeasible perspectival changes. We can move from a bird’s-eye view of the entire building and its surroundings to zoom in on the smallest detail of a room’ (Arup, 2019: 26). This entails ‘the virtualization of the eye into a metastatic virtual camera able to view an object from any point of view whatsoever’ (Galloway, 2014: 66); it renders urban space entirely transparent to the CDT user’s gaze.

This highly mobile and all-seeing gaze evokes Haraway’s (1988) discussion of ‘the god-trick’. While it is important to neither over-generalise its prevalence nor exaggerate its power (Bier, 2022Kurgan, Brawley and Kirkham-Lewitt, 2019Pickles, 2003), it does appear that the CDT imaginary is precisely premised on ‘a conquering gaze from nowhere’:

This is the gaze that mythically inscribes all the marked bodies, that makes the unmarked category claim the power to see and not be seen, to represent while escaping representation. This gaze signifies the unmarked positions of Man and White, one of the many nasty tones of the word ‘objectivity’ to feminist ears in scientific and technological, late-industrial, militarized, racist, and male-dominant societies. (Haraway, 1988: 581)

This penetrating gaze gives urban knowledge from a CDT to its users, offering a totally comprehensive view of the city as an object. The power of this gaze in the context of CDTs needs further elaboration, however.

One aspect of this power, in the context of urban management, is the ability to act on the city. This move from transparency to action is interrogated in Söderström’s (1996) short history of the forms of visibility embedded in professional planning practice as it emerged in the late nineteenth century:

This new space of representation simultaneously opened up a new space of action: urban space. Situated within the same simulated space, scaled down so as to be readily assimilable at a glance, forms that had hitherto belonged to incommensurable categories could now be apprehended by the mind and could therefore be manipulated as parts of a whole. (Söderström, 1996: 258)

Both the spatial and visual organisation of a CDT make the twinned city ‘a whole’, and Söderström’s association between seeing the city at a glance and acting on the city is a core dynamic of the CDT and the professional vision of planners in which it is operationalised (Carlsson, 2022). The promotional materials that picture CDTs in use often show the twin as a glowing holographic model sitting on a table, with its users gathered at its edges managing the city by moving objects with a gesture, or by interacting with it via a tablet interface or a VR headset, or just looking at it benevolently. That is, the bodies of users are pictured as outside of the twin looking in, and therefore in a position to transform it from its outside. After all, ‘it is widely accepted that to improve the quality of life and sustainability of cities, urban planning involves manipulating their physical form’ (Batty, 2024: 195). The visual integrity and inspectability of objects are imperative if action is to follow from seeing a CDT (Figure 5).
Figure 5. This Shutterstock stock image is frequently reproduced on websites discussing city digital twins.

The association of transparent visibility and absolute space with being able to take action on what is thus visualised must be understood as part of powerful masculine whiteness of the gaze mobilised with CDTs. CDTs are based on ‘the white spatial imaginary [which] idealizes “pure” and homogeneous spaces, controlled environments, and predictable patterns of design and behaviour’ (Lipsitz, 2011: 29). McKittrick argues:

If we imagine that traditional geographies are upheld by their three-dimensionality, as well as a corresponding language of insides and outsides, borders and belongings, and inclusions and exclusions, we can expose domination as a visible spatial project that organizes, names, and sees social differences (such as black femininity) and determines where social order happens. (McKittrick, 2006: xiv)

As McKittrick remarks, volumetric spaces are constituted both from, and reiterate, a racial, sexual and economically dominant vantage point which allows other forms of life to be managed.

Further, the CDT imaginary universalises this way of seeing when it is claimed that CDTs simply offer ‘a very real, detailed, specific, and impactful visual experience’ (Deng et al., 2021: 128) which is seen in the same way by all CDT users. ‘An open and transparent visual experience helps everyone clearly see a scheme in context and understand how it will work’, according to VU.CITY’s website page for local authorities (VU.CITY, 2023). For example, it is argued that a CDT enables effective urban management also because it shows the same data to all its users. (There is no critical discussion of the production of near-real-time urban data in the CDT literature.) Tkacz (2022) has observed that the multiple screens in the smart city control room actually generate considerable uncertainty for urban managers, who have to make sense of constantly changing data, distributed across different screens, in different formats. In contrast, the CDT shows its data in one interface – the 3D model – and it is argued by proponents of CDTs that this eliminates what Tzack describes as the ‘doubt’ generated by control centre dashboards. Instead, a twin ‘orchestrates’ urban management by showing the same information to everyone:

Orchestration is the harmonious organization of activities (good planning) that enables informed decisions and helps to avoid costly ad-hoc problem solving. Digitization helps planning of activities by keeping track of essentials, and by facilitating the inclusion of stakeholders, because everyone can be updated to have the same and the latest information. (Lehtola et al., 2022: 1)

This is claimed to offer both resource efficiency and democratic inclusion. Displaying the same data is claimed to generate ‘a universal experience’ (Buildmedia, 2021) or a ‘common referential’ (Dassault Systèmes, 2017). The effect is ‘to uniformize the vocabulary used among the very different profiles involved in the urban sciences, be they planners, architects, policymakers, citizens, lawyers, computer scientists, and so on’ (Meta et al., 2021: 13).

3D city modelling enables enrichment of digital city models with external data and presentation of more knowledge-based city scenarios… Consequently, more informative and realistic city scenarios enable elimination of emotional statements and opinions during the decision-making process. (Hämäläinen, 2021: 6)

In short, ‘3D city models and digital twin technology assist urban developers in objectifying and forming a shared vision and understanding of the city design subject matter’ (Hämäläinen, 2021: 6; see also Dembski et al., 2020Kikuchi et al., 2022). These claims assume that removing differences among ‘very different profiles’ is an advantage of CDTs (as per Rocha and Snelting’s [2022b] description of the volumetric regime).

Moreover, not only is it assumed that seeing the city in the same way results in better decisions about city management but it is also assumed that this specific mode of seeing is the only way to manage cities. This extends to a key claim made by the CDT imaginary, which is its efficacy as a tool for community participation. This is mentioned repeatedly in discussions of city digital twins, by researchers, commercial software vendors and local city authorities, which suggests that criticism of the lack of democracy in many versions of the smart city is acknowledged (see for example Cardullo and Kitchin, 2019Greenfield, 2013Kitchin, 2015Sadowski and Pasquale, 2015). However, the example of such participation that is repeatedly used in the literature is the ability to show planning proposals to citizens and communities.

3D model allows for the easy removal and addition of newly proposed buildings… Any proposed building plans can easily be added to the digital twin using the BIM model. This model would then allow citizens and public officials to walk around the digital twin and see the effect that the new building would have on the skyline from a number of different locations. (White et al., 2021: 5)

Computer-generated, photo-realist images of planned new developments can be inserted into existing models so that everyone can see what they will look like when they are built; with GIS functionality, the sightlines and shadows cast by proposed buildings can also be calculated and visualised (Ketzler et al., 2020Scalas et al., 2022Schrotter and Hürzeler, 2020; and see Carlsson, 2022). In this further erasure of differences among the various users of CDTs, both professionals and ‘citizens’ are assumed to engage with the CDT’s image of the city entirely visually. According to the World Economic Forum (2022: 14), CDTs allow residents to ‘visualize urban congestion in real time, allowing them to adjust their travel plans. They can also “go sightseeing” to any of the world’s tourist destinations regardless of where they are in reality’. This persistent emphasis on the urban as what can be seen is underlined by the fact that when, very occasionally, urban humans are invited to contribute to a twin’s database, this is usually a request for photographs in order to deepen the visual realism of a twin or to include more objects (Belcher, 2019Dembski et al., 2020Ham and Kim, 2020Lu et al., 2020). Citizen feedback is sought only on the appearance of the city; managers act only on the basis of its various data displays, both numeric and visual. Thus, all the viewers of a CDT are assumed to see the twin in the same way, ‘sustained by the idea that vision is stable and shared, and that visual experiences are largely determined by physical form and objects, including buildings, trees and hills, that can be mapped, measured and compared using numerical coordinates’ (Carlsson, 2022: 228).

The assumptions that seeing the city as a spatially coherent and visually transparent animated object is the most effective way to manage it, and that everyone will see the city in that same rational way, constitute one version of the production of similarity that Rocha and Snelting (2022a) identify as central to the volumetric regime. The emphasis in the CDT imaginary on entirely rational decision-making is masculinist, as is its technocratic insistence that it offers the single, truthful, data-derived vision of the city (according to Siemens (2020), its Mindsphere CDT offers ‘a single source of truth for all’). This managerial affiliate of the CDT is given the power to act on the city by seeing it and manipulating it. The world of the CDT is therefore surely a contemporary version of that produced by colonial and capitalist visual techniques which also envisioned the world as abstract, transparent and actionable (Pickles, 2003).

urbanNext (January 6, 2026) Visualising human life in volumetric cities. Retrieved from https://urbannext.net/visualising-human-life-in-volumetric-cities/.
Visualising human life in volumetric cities.” urbanNext – January 6, 2026, https://urbannext.net/visualising-human-life-in-volumetric-cities/
urbanNext December 17, 2025 Visualising human life in volumetric cities., viewed January 6, 2026,<https://urbannext.net/visualising-human-life-in-volumetric-cities/>
urbanNext – Visualising human life in volumetric cities. [Internet]. [Accessed January 6, 2026]. Available from: https://urbannext.net/visualising-human-life-in-volumetric-cities/
Visualising human life in volumetric cities.” urbanNext – Accessed January 6, 2026. https://urbannext.net/visualising-human-life-in-volumetric-cities/
Visualising human life in volumetric cities.” urbanNext [Online]. Available: https://urbannext.net/visualising-human-life-in-volumetric-cities/. [Accessed: January 6, 2026]

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