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Bibrietal. Computational Urban Science (2022) 2:24
https://doi.org/10.1007/s43762-022-00051-0
ORIGINAL PAPER
The Metaverse asavirtual form
ofdata-driven smart urbanism: platformization
andits underlying processes, institutional
dimensions, anddisruptive impacts
Simon Elias Bibri1,2*, Zaheer Allam3,4 and John Krogstie1
Abstract
The emerging phenomenon of platformization has given rise to what has been termed "platform society,“ a digitally
connected world where platforms have penetrated the heart of urban societies—transforming social practices, dis-
rupting social interactions and market relations, and affecting democratic processes. One of the recent manifestations
of platformization is the Metaverse, a global platform whose data infrastructures, governance models, and economic
processes are predicted to penetrate different urban sectors and spheres of urban life. The Metaverse is an idea of a
hypothetical set of “parallel virtual worlds” that incarnate ways of living in believably virtual cities as an alternative to
future data-driven smart cities. However, this idea has already raised concerns over what constitutes the global archi-
tecture of computer mediation underlying the Metaverse with regard to different forms of social life as well as social
order. This study analyzes the core emerging trends enabling and driving data-driven smart cities and uses the out-
come to devise a novel framework for the digital and computing processes underlying the Metaverse as a virtual form
of data-driven smart cities. Further, it examines and discusses the risks and impacts of the Metaverse, paying particular
attention to: platformization; the COVID-19 crisis and the ensuing non-spontaneous "normality" of social order; cor-
porate-led technocratic governance; governmentality; privacy, security, and trust; and data governance. A thematic
analysis approach is adopted to cope with the vast body of literature of various disciplinarities. The analysis identifies
five digital and computing processes related to data-driven smart cities: digital instrumentation, digital hyper-con-
nectivity, datafication, algorithmization, and platformization. The novelty of the framework derived based on thematic
analysis lies in its essential processual digital and computing components and the way in which these are structured
and integrated given their clear synergies as to enabling the functioning of the Metaverse towards potentially virtual
cities. This study highlights how and why the identified digital and computing processes—as intricately interwoven
with the entirety of urban ways of living—arouse contentions and controversies pertaining to society’ public values.
As such, it provides new insights into understanding the complex interplay between the Metaverse as a form of sci-
ence and technology and the other dimensions of society. Accordingly, it contributes to the scholarly debates in the
field of Science, Technology, and Society (STS) by highlighting the societal and ethical implications of the platformiza-
tion of urban societies through the Metaverse.
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Open Access
Computational Urban
Science
*Correspondence: simoe@ntnu.no
2 Department of Architecture and Planning, Norwegian University
of Science and Technology, Alfred Getz vei 3, Sentralbygg 1, 5th floor,
NO–7491 Trondheim, Norway
Full list of author information is available at the end of the article
Page 2 of 22
Bibrietal. Computational Urban Science (2022) 2:24
1 Introduction
While the idea of the Metaverse has been around for
three decades as a speculative fiction narrative where
users are represented as avatars in unconnected virtual
spaces, it is until recently that it came to the public fore
with the rebranding of Facebook into “Meta” and other
platform providers. e Metaverse has been made pos-
sible by the rapid pace of progress in the development
of the core enabling technologies, notably Artificial
Intelligence (AI), Big Data, the Internet ings (IoT),
Edge Computing, Blockchain, Digital Twins (DT), Vir-
tual Reality (VR), Augmented Reality (AR), Mixed Real-
ity (MR), and high-speed 5G networks. While these
technologies are not of equal importance in terms of
enabling the Metaverse as a “sophisticated” computing
platform, their convergence has expedited the integra-
tion of the independent virtual spaces owned by many
different platform companies. Meta is one of the globally
operating platform companies. Platforms have become
crucial for understanding future-focused or envisioned
urbanism in emerging data-driven smart cities (Allam
et al. 2022a). Smart urbanism and platform urbanism
are interrelated as approaches to urban development in
that the latter originated in the multifaceted emergence
and rapid development of the former over the past two
decades. Generally, smart urbanism is understood as a
model of urban development focused on the use of big
data analytics, digital flows, and networked technolo-
gies (Bettencourt, 2014; Kitchin, 2014). ese aspects of
smart urbanism tend to capture the nature of platform
urbanism as a manifestation of the process and practice
of platformization. In short, platform urbanism is seen as
an evolution of smart urbanism (Han and Hawken 2018).
Caprotti et al. (2022) define platform urbanism as a
“novel set of digitally enabled socio-technological assem-
blages rooted in the urban, which enables the emergence
of new social and material relationships including inter-
mediations and transactions.”
Moreover, research and development of the Metaverse
has become a key trend in smart urbanism in terms of
the design of believably virtual cities based on large-
scale data-driven AI systems (Bibri, 2022). is relates
to what has been termed “virtual urbanism” or “aug-
mented urbanism” (Gordon and Manosevitch, 2011;
Sirc, 2001; Wilkins and Stiff, 2019) with respect to the
application of urban planning, urban design, and urban
geography to the design of virtual and augmented urban
spaces.Instudying the effects of the emergence of virtual
cities have on their perceptions compared to real-world
cities, Hemmati (2022) found that the Metaverse can
create more believable images than reality. As an envi-
sioned form of virtual urbanism, the Metaverse denotes
“a set of virtual spaces where you can create and explore
with other people who are not in the same physical
space as you. You will be able to hang out with friends,
work, play, learn, shop, create, and more” (Bosworth and
Nick,2021). e whole idea of the Metaverse as a form
of scientific and technological development relates to the
long-established debate on the role of science and tech-
nology in social progress (see, e.g., Cutcliffe, 2000; Cut-
cliffe, 2001; Jasanoff and Kim, 2009; Volti, 2001). In the
light of the negative impacts that the social media plat-
forms owned by Meta have had on urban society, cou-
pled with the plethora of thorny issues they have raised,
over the last two decades, the Metaverse will likely fail
to justify scientific and technological development and
investment in the sense of equating science and tech-
nology with societal progress (Bibri, 2022). at is, with
the advancement of the conditions of urban society and
how people live in it based on prevailing norms, values,
beliefs, and goals. Societal progress entails that the cur-
rent conditions of society are improved compared to the
past, and that these conditions are envisaged to be better
than those of the present (Noll, 2014).
e Metaverse depicts the peculiar characteristics
ofways of living in data-driven smart cities of the future.
Urbanism denotes “the distinctive features of the expe-
rience of everyday life in cities” (Bridge, 2009, p. 106),
which are being highly responsive to a form of data-
driven smart urbanism and platform urbanism based
on AI and analytics systems with regard to urban ser-
vices and urban governance. e radical expansion of
the granularity, range, and magnitude of urban big data
and data-intensive compute algorithms combined with
the onset of AI techniques has become compounded by
the COVID-19 pandemic. One implication of this is that
this crisis has induced big tech companies to look for new
ways to cater for the growing demand in speed, scale,
and extension of AI-software systems towards large-scale
data-driven AI systems given their potential for enabling
“sophisticated” forms of governance. Smart governance
has been criticized because it is strongly driven by the
interests and agenda of high-tech companies and large
corporations as well as the associated government poli-
cies (e.g., Grossi and Pianezzi, 2017; Hollands, 2015).
Keywords: Metaverse, Data-driven smart urbanism, Hyper-connectivity, Datafication, Algorithmizaton,
Platformization, COVID-19 pandemic, Surveillance, Governance, Privacy, Ethics
Page 3 of 22
Bibrietal. Computational Urban Science (2022) 2:24
Moreover, the “new normal” established in the after-
math of the COVID-19 pandemic has resulted in an
abrupt large-scale digital transformation of urban soci-
ety, a process of digitization and digitalization that is in
turn paving the way for merging virtual reality and physi-
cal reality inthe context of data-driven smart cities. is
merger requires the intensification of the datafication,
algorithmization, and platformization of both socializing,
working, learning, playing, travelling, shopping, and so
on, as well as the social organization resulting from these
interactions and activities (Bibri and Allam, 2022a). is
epitomizes the core of the Metaverse vision in terms of
its ultimate goal to virtualize ways of living and working
in urban society. isconcept refers to the social organi-
zation resulting from social interaction as an essential
aspect of social life, the ways in which people act with
other people and react to their ways of acting, as well as
the interaction of people with the physical environment
(Bibri, 2022). With reference to smart cities, however,
Calvo (2020) argues that the escalating digital and com-
puting trends are, either intentionally or unintentionally,
associated with highly corrosive consequences for urban
society. In addition, smart city systems “are often based
on technological orthodoxies which are conceptually
and empirically shallow” (Viitanen and Kingston, 2015).
In other words, smart cities are cast as “bounded, know-
able, and manageable…that can be steered and controlled
in mechanical, linear ways” (Kitchin, 2016, p. 11). Con-
sequently, numerous studies have addressed, from a vari-
ety of perspectives, the potential risks and other negative
implications of smart cities (e.g., technocratic reduc-
tionism, technocentricity, governance corporatization,
technological lock-ins, surveillance, privacy loss, mind
control, democratic decay backsliding) and the ramifica-
tions of the infiltration of socially disruptive technolo-
gies into the fabric of urban life andurban environment
(see Bibri, 2021a, 2021b for a detailed overview). In
view of the above, the downsides of the Metaverse as a
virtual form of data-driven smart urbanism or platform
urbanism remain unavoidable. is inescapable situa-
tion, especially the potential issues and risks that are not
immediately obvious but easily encountered, are most
likely to affect the social life and social order of urban
society in terms of social structures and institutions,
social relations, social interaction and behavior, and cul-
tural norms and values.
Against the preceding background, this study analyses
the emerging trends enabling and driving data-driven
smart cities and uses the outcome to devise a novel
framework for the digital and computing processes
underlying the Metaverse as a virtual form of data-driven
smart cities. Further, it examines and discusses the risks
and impacts of the Metaverse, paying particular attention
to: platformization; the COVID-19 crisis and the ensuing
non-spontaneous "normality" of social order; corporate-
led technocratic governance; governmentality; privacy,
security, and trust; and data governance.
is study is structured as follows: Section2 presents
a survey of related work in terms of the state–of–the–art
research. Section3 introduces, outlines, and justifies the
research methodology adopted in this study, Section is 4
presents the results of the thematic analysis. is study
ends, in Section5, with discussion and conclusion.
2 Related Work
e permeation of urban society by digital platforms in
regard to data infrastructures, economic processes, and
governance models has provided the globally operating
platform companies with the opportunity to leapfrog
their way of thinking and devising complex platforms
by courtesy of data-driven smart urbanism and platform
urbanism. One of such platforms is the Metaverse, a
gigantic ecosystem application that is fuelled by the most
innovative computing and immersive technologies. Given
the current development stage of the Metaverse as a
global platform being launched in 2021 by Meta, research
in this area tends to focus mainly on two strands, which
are typical tothe advent of new socially disruptive tech-
nologies. e first strand is concerned with the state-of-
the-art andtechnical aspects of the Metaversein terms
of computing technologies, immersive technologies,
ecosystems, developments, trends, applications, oppor-
tunities, grand challenges, open issues, research agenda,
roadmapping, and so on. One of the first studies that was
conducted in this regard after the announcement of the
Metaverse is the comprehensive state-of-the-art review
by Lee etal. (2021). is focuses on the technologies that
fuel the “Digital Big Bang” from the Internet and XR to
the Metaverse, which support its ecosystem as a gigan-
tic application. In addition to this detailed framework,
the authors cover a plethora of other topics, as well as
propose a research agenda and highlight the grand chal-
lenges associated with the development of the Metaverse.
While the architecture proposed by Lee et al. (2021)
consists of two key layers, Duan etal. (2021) propose a
three-layer architecture for the Metaverse from a macro
perspective: infrastructure, interaction, and ecosystem.
Dhelim et al. (2022) address the state-of-the-art archi-
tecture for the Metaverse applications. e authors
argue that it relies on a cloud-based approach for avatar
physics emulation and graphics rendering computation,
a centralized design that is unfavorable due to its sev-
eral drawbacks caused by the long latency required for
cloud access. To address this issue, they propose a Fog-
Edge hybrid computing architecture that leverage an
edge-enabled distributed computing paradigm. In such
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Bibrietal. Computational Urban Science (2022) 2:24
architecture, edge devices computing power is utilized
to fulfil the required computational cost for heavy tasks,
such as computation of 3D physics in virtual simulation
and collision detection in virtual universe.
Similar to Lee et al. (2021), but different to their
approach and with less detail, Mystakidis (2022) offers
a comprehensive analysis of the extant literature, iden-
tifying current gaps or problems. e author discusses
a number of topics of the Metaverse as a multiuser
environment merging physical reality with digital vir-
tuality, including XR and related concepts, multimodal
Metaverse interactions, limitations of 2D learning envi-
ronments, a brief history of virtual media and XR tech-
nologies, and Metaverse contemporary development. By
way of conclusion, the author states that the Metaverse
can enable world-wide participation on equal footing,
unbound by geographical restrictions. Expanding on
the history of virtual media, Duan etal. (2021) journey
toward a historical and novel Metaverse. As regards the
applications of the Metaverse, Taylor and Soneji (2022)
examine how visualization can leverage the Metaverse
in bioinformatics research and the advantages and dis-
advantages of this technology. Worth noting is that the
applications of the Metaverse span a plethora of domains
of urban society given its scope of use as a virtual form
of data-driven smart urbanism and platform urbanism.
Speaking of visualization, it is one of the several areas
that is united by the rapidly evolving field of immersive
analytics, in addition to immersive environments and
human-computer interaction. is is expressed by Ens
etal. (2021) who, in their study, present seventeen key
research challenges aiming to coordinate future work
by providing a systematic roadmap of current direc-
tions. e authors also provide impending hurdles in
this area to facilitate productive and effective applica-
tions for immersive analytics. However, most of the
aforementioned studies tend largely to focus on one or
a few aspects of the technical strand of the Metaverse,
lacking a more holistic perspective on the topic with
respect to the historical embeddedness of science and
technology, the socially constructed nature of science
and technology, the social conditions and institutional
structures shaping science and technology, and the soci-
etaland ethical implications of science and technology,
and so on. In an attempt to address this gap in his recent
study which is positioned within science of science,
(Bibri, 2022) analyzes the complex interplay between the
Metaverse as a form of science and technology and the
wider social context, focusing on the intertwined factors
underlying its materialization, expansion, success, and
evolution, as well as the key contentions, bottlenecks,
and uncertainties that have direct implications for its
realization and acceptance. is study shows that the
Metaverse raises seriousissues and concerns related to
social exclusion, marginalization, hive mentality, privacy
erosion, surveillance, control, democratic backsliding,
and dystopianism.
Given its focus, the above study relates to the second
strand of research within the area of the Metaverse,
which is concerned with its risks and other negative
implications, engaging critically with the underlying core
enabling technologies from a variety of perspectives.
It is important to note that some of these implications
have also been covered by some of the previous stud-
ies, but more or less from a computational perspective.
For example, Lee etal. (2021) discuss a number of influ-
encing design factors, including fairness, cyberbullying,
device acceptability, avatar acceptability, and privacy
threats, and argue that these will determine the sustaina-
bility of the Metaverse. Gurov and Konkova (2022) focus
on the Metaverse for human or human for Metaverses
by researching the grounds on which big tech companies
seek to transform the mankind way of life and the nature
of the “human,” based on the idea of the Metaverse.
Using a comparative analysis, the authors highlight the
opportunities and threats that the Metaverse pose for
humanity in the conditions ofthe uncontrolled techno-
logical development. ey conclude that forming and
disseminating a new socio-humanitarian rationality is
a necessary condition for the successful development
of the Metaverse, predicated on the assumption that
this approach will ensure control over the actions and
activities of big tech companies. In this line of think-
ing, Rosenberg (2022) discusses the regulation of the
Metaverse as a roadmap, outlining the dangers of the
Metaverse along with proposals for regulation. Bibri and
Allam (2022b) question and challenge the Metaverse
through the prism of the logic of surveillance capitalism,
focusing on how and why the practices of the govern-
ance of urban society are bound to be undemocratic and
unethical. However, none of these studies has addressed
the link between the Metaverse and data-driven smart
urbanism from a conceptual perspective, nor the disrup-
tive impacts of what underlie this relationship as an out-
come of the process and practice of platformization and
its institutional dimensions. e recent study conduced
by Hemmati (2022) rather deals with the Metaverse as
an urban revolution in regard to its effect on the per-
ceptions of urban audience. e author found that the
media seeks to create a purposeful image of reality in
the minds of the audience, and the Metaverse can create
more believable images than reality thanks to immersive
technologies.
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Bibrietal. Computational Urban Science (2022) 2:24
3 Methodology: Thematic Analysis
is study assumes that there are trends and processes
that repeat themselves and drive and underlie the digital
architecture of computer mediation associated with the
Metaverse as a virtual form of data-driven smart urban-
ism. erefore, it uses a qualitative method to identify
this architecture and its driving trends and underlying
processes as concepts and, eventually, to identify the
concepts behind them. In a broad sense, qualitative stud-
ies ultimately aim to describe and explain a pattern of
relationships, a process that requires a set of conceptu-
ally specified categories (Mishler, 1990). e qualitative
“tactics” to be used to generate meanings from a diverse
empirical and theoretical material (Miles and Michael
Huberman, 1994) relate to thematic analysis. Following
these tactics, a thematic analysis has been designed for
identifying the architecture and its digital and comput-
ing concepts and for conceptualizing the theoretical base
behind these components. ematic analysis is particu-
larly, albeit not exclusively, associated with the analysis
of textual material. In this respect, it emphasizes identi-
fying, analyzing, interpreting, and reporting themes, i.e.,
important patterns of meaning within qualitative data.
Worth pointing out is that, as suggested by Braun and
Clarke (2006), thematic analysis is flexible in terms of the-
oretical and research design given that it is not depend-
ent on any particular theory or epistemology: multiple
theories can be applied to this process across a variety
of epistemologies. However, this flexibility can lead to
inconsistency when developing themes derived from the
qualitative data (Holloway and Todres, 2003). Also, there
is no one accurate interpretation of these data, interpre-
tations reflect the positioning of the researcher.
As an inductive analytical technique, thematic analy-
sis involves discovering patterns, themes, and concepts
in thequalitative data that include interdisciplinary and
transdisciplinary literature. As such, it allows these data
to determine the set of themes to be identified, justified
in this context by the fact that the Metaverse is an emerg-
ing area of research that is still in its infancy. at is to
say, there is no established theoretical framework that
gives a strong idea of what kind of themes to expect to
find in the data, as with the deductive analytical tech-
nique. Accordingly, the intent is to develop a framework
based on what can be found as themes that are not pre-
determined. Moreover, thematic analysis ismore appro-
priate when analyzing and synthesizing a large body of
literature—in the form of empirical studies, exploratory
studies, conceptual frameworks, descriptive accounts,
reviews, and so on. It can be used to produce complex
conceptual cross–examinations of meanings in the quali-
tative data.
e main steps of this study’s methodology are as
follows:
1. Review of literature of various disciplinarities that
is related to data-driven smart urbanism. e aim
is to deconstruct (“take apart”) a multidisciplinary
text related to data-driven smart cities as a model of
urbanism. e outcomes of this process are numerous
themes, in this case “trends,” “processes,” “technolo-
gies,” “applications,” and “developments,” that areasso-
ciatedwiththis model of urbanism. It is important to
be familiarized with all the aspects of the qualitative
data collected. is step provides the foundation for
the subsequent conceptual and critical analysis.
2. Recognizing patterns in seemingly random infor-
mation (Boyatzis, 1998). e aim is to note major
patterns and concepts within the results of the first
step. e second step looks for similarities or pat-
terns within the sample and then codes the results
by concepts. Coding involves identifying passages
of text that are linked by a common theme, allowing
to index the text into categories and therefore estab-
lish a framework of thematic ideas about it. In this
step, the preliminary codes identified are the features
of data that appear meaningful and interesting, and
the relevant data extracts are sorted according to the
overarching themes. It is important to allude to the
relationship between codes and themes.
3. Revising themes is about combining, separating,
refining, or discarding initial themes. is relates to
the inductive approach to thematic analysis. Data
within the themes should cohere together meaning-
fully and be clear and identifiable as regards the dis-
tinction between these themes. A thematic map is
generated from this step.
4. Identifying the digital and computing processes
underlying the Metaverse as a virtual form of data-
driven smart urbanism in terms of recognizing the
specific and distinctive features of this model of
urbanism.
5. Finding the theoretical relationships among the iden-
tified concepts and the Metaverse as a virtual form of
data-driven smart urbanism—conceptualization.
6. Examining and discussing the risks and impacts of
theidentified digital and computing processes under-
lying the Metaverse as a virtual form of data-driven
smart urbanism in the wake of the COVID-19 pan-
demic.
7. Transforming the analysis into an interpretable piece
of writing by using vivid and compelling data extracts
that relate to the overarching themes and literature.
e outcome must go beyond a mere description
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Bibrietal. Computational Urban Science (2022) 2:24
of the preconceived themes and portray an analysis
supported with evidence.
4 Results
4.1 The Escalating Trends andProcesses Driving
andUnderlying theMetaverse asaVirtual Form
ofData‑Driven Smart Urbanism
e thematic analysis has identified five digital and com-
puting processes repeated, which result from the digital
transformation that both data-driven smart cities and the
Metaverse immerse in through the processes of digitiza-
tion and digitalization.
4.1.1 Digital Transformation: Digitization andDigitalization
We are moving into an era where digital instrumenta-
tion, digital hyper-connectivity, datafication, algorithmi-
zation, and platformization are routinely pervading the
very fabric of urban ways of living thanks to the minia-
turization of digital technologies as a set of machines,
systems, and devices. Urban society is currently under-
going large-scale digital transformation in the light of
both recent advances in science and technology and
drastic shifts in governance. is extensive process
of digitization and digitalization has been intensified,
accelerated, and normalized by the COVID-19 pan-
demic. Digitization refers to the process of converting
pieces of information, or encoding representations of
urban actions, into a digital format that can be read, pro-
cessed, transmitted, stored, re-used, and manipulated by
computational systems for various use cases in the form
of a series of zeroes and ones that describe a discrete
set of points. Digitization is foundational in terms of
making the connection between the physical world and
computer software. It is an enabler for all the computa-
tional processes that generate value because of the need
for manipulable and exploitable data. e process has
exponentiallyincreased the amount of data that could
be further processed, analyzed, and harnessed. Digi-
talization is about the ways in which urban processes
are organized through and around digital technologies.
Generally, it entails facilitating and enhancing processes
by leveraging digital technologies and digitized data
with respect to productivity, efficiency, and effectiveness
through taking a process from a human-driven series of
events to software-driven series of events. In short, it is
the use of digital technologies to advance processes and
provide value-generating and maximizing opportuni-
ties. Changes associated with digitalization are applied
to both data-driven smart cities as a social organiza-
tion and the Metaverse as a commercial organization.
ey include distributed and flexible operational and
functional processes and organizational arrangements,
the automation and autonomy of administrative task sys-
tems, the adoption of solutionist and knowledge man-
agement systems, and communication and horizontal
information platforms. In this context, digital transfor-
mation is urban transformation enabled by both digiti-
zation and digitalization, an integrated process which is
necessary to pursue and spur innovation.
Data-driven smart cities represent an immersion indig-
ital transformation, a process of digitization and digitali-
zationthatis enabled by the convergence of AI, the IoT,
and Big Data and its far-reaching consequences— digital
instrumentation, digital hyper-connectivity, datafication,
algorithmization, and platformization (Bibri and Allam,
2022a; Calvo, 2020). ese also pertain to the global
architecture of computer mediation pertaining to the
Metaverse as a virtual form of data-driven smart urban-
ism. Among the technological pillars of the Metaverse as
a giant ecosystem application are user interactivity, XR,
computer vision, AI/blockchain, robotics/IoT, edge cloud,
wireless networks, and hardware infrastructure (Lee etal.
2021). Data-driven smart cities (e.g., Kaluarachchi, 2022;
Sarker etal., 2020) are massively digitally instrumented
and hyperconnected, intensively datafied, and increas-
ingly algorithmized and platformized, and as such, they
enable data-intensive, distributed computing across
various urban domains based on innovative techniques,
models, and decision support systems in the shape of
large-scale data-driven AI systems. is is to enhance
and optimize urban operations, functions, designs, strat-
egies, and policies by means of generating “irreplaceable”
values in the form of applied intelligence from monitor-
ing, analyzing, and understanding citizens and places
across different spatial scales and over different temporal
scales. By the same token, at the heart of the Metaverse
is a computational understanding of human users’ cogni-
tion, emotion, motivation, and behavior that reduces the
experience of everyday life to logic and calculative rules
and procedures (Bibri and Allam, 2022a). is implies
that human users become more knowable and managea-
ble and their behavior more predictable and controllable,
thereby serving as passive data points feedingthe AI and
analytical systems that they have no interchange with or
influence on. is relates to—as with smart urbanism—to
quantitative universalism and reductionism, which refers
to the socio-technical configurations that reduce urban
phenomena into the purely quantitative (Bell, 2013; Hak-
lay, 2013). Accordingly, the rich complexity of urban life
is reduced to narrow quantitative and unitary languages,
manifested in a plethora of platforms—with long-term
implications for the wellness of citizens.
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Bibrietal. Computational Urban Science (2022) 2:24
4.1.2 Platformization
Platform companies are becoming increasingly central to
public and private life in urban society, transforming key
urban sectors and domains of urban life. Many different
types of platforms exist and vary across sectors, strate-
gies, and practices. ere has recently been a marked
intensification of platformization in terms of a radical
expansion and proliferation of platforms related to urban
governance, urban economy, and urban services as key
components of smart cities. As an ideological forma-
tion, platforms are associated with smart cities and shar-
ing economy (Barta and Neff, 2016; Sadowski, 2020). e
process of platformization deeply affects urban society—
with socio-cultural, socio-political, and politico-eco-
nomic consequences. e concept of “platformisation”
has been derived from the notion of “platform.” Plat-
forms combine digital technologies with organizational
forms. Poell, Nieborg and van Dijck (Poell etal., 2019, p.
1) define platforms as “(re-)programmable digital infra-
structures that facilitate and shape personalized interac-
tions among end-users and complementors, organized
through the systematic collection, algorithmic process-
ing, monetization, and circulation of data.” Accordingly,
they have been discussed in relation to the private, cor-
porate, technology, and public sectors. Platformization
refers to “the penetration of infrastructures, economic
processes, and governmental frameworks of digital plat-
forms in different economic sectors and spheres of life, as
well as the reorganization of cultural practices and imagi-
nations around these platforms” (Poell etal., 2019, p. 1).
It entails the construction, operation, and exploitation
of platforms and the alteration of existing organizational
forms to align them with the logic of platforms (Casilli
and Posada, 2019; Poell etal., 2019). In this network of
agents, information, products, services, resources, and
values are exchanged among companies, applications,
users, and devices. Helmond’s (2015) defines platformiza-
tion as the “penetration of platform extensions into the
web, and the process in which third parties make their
data platform-ready.” e computational infrastructures
and informational resources involved in this process
afford institutional relationships that are at the root of
a platform’s evolution and growth as platforms provide
a technological framework for otherentities to use as a
basis for further development(Helmond, 2015). Plantin
etal. (2018) observe a simultaneous “platformisation of
infrastructures” and “infrastructuralization of platforms”.
e authors argue that digital technologies have made
“possible lower cost, more dynamic, and more competi-
tive alternatives to governmental or quasi-governmental
monopoly infrastructures, in exchange for a transfer of
wealth and responsibility to private enterprises” (Plantin
etal., 2018, p. 306). Nieborg and Helmond (2019) analyse
the case of Meta, where social media platforms arecon-
ceived as a “data infrastructure” that hosts a set of var-
ied and constantly evolving “platform instances.” ese
instances are set to include many spheres of everyday
life with the development of the Metaverse as a 3D net-
work of numerous virtual worlds within the framework
of visual cities thanks to the process of algorithmization
and its key role in the dramatic shifts in the social organi-
zation resulting from social interactions and activities
made possible by pairing digital data with connectivity
to intensify datafication. ere are many sets of platform
instances pertaining to data-driven smart cities. One of
them is associated with social infrastructure, which ties
in well with the Metaverse in terms of its virtual services,
as it typically involves assets that accommodate the social
services provided by the public sector and related enti-
ties orthrough the financing of private provision of ser-
vices. New digital technologies, interactive platforms,
innovative solutions, and diverse forms of public-private
cooperation have become of critical importance to over-
come the social challenges and to bring about the needed
transformations in a number of social domains. is is at
the core of the assets of the social infrastructure of data-
driven smart cities of the future, particularly in relation
to citizen participation with respect to the following plat-
form instances (Bibri and Krogstie, 2021):
• Crowdsourcing platforms to address important city
issues related to different areas.
• Platform to enable citizens to influence their experi-
ence of the city by providing feedbacks and ratings.
• Platform where citizens can participate in the surveys
organized by the city administration which can use
the related data to adopt the resolutions in relation to
the different domains of city life.
• Platform to engage more citizens in dialogue so as to
gather input on their needs and demands, to evaluate
their suggestions, and to identify and solve important
issues.
• Platform to enable citizens to communicate as well
as track the status and control the execution of their
complaints related to city issues.
• Special portals to enable citizens to report the eco-
nomic problems existing in the city in response to
the adverse effects of pandemics and crises.
• Platforms to allow citizens to participate in urban
technologies and policies, including:
– Classrooms for learning about the uses and appli-
cations of and innovating in emerging digital tech-
nologies;
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Bibrietal. Computational Urban Science (2022) 2:24
– Entrepreneurial spaces for attracting startups and
skilled innovators to create and promote new tech-
nologies;
– Co-innovation centers for enabling close collabora-
tion among different city stakeholders;
– Participatory platforms for connecting city stake-
holders to support decision-making processes; and
Democracy platforms for enabling citizens to dis-
cuss government proposals as well as submit their
own.
4.1.3 Algorithmization
Algorithmization has created the propensity for
developing numerous platforms across various urban
domains for a variety of practices and purposes. e
ever-increasing trends towards the algorithmization
of social interactions and human activities and the
social organization resulting from these interactions
and activities epitomize the core of the Metaverse
vision. Algorithmization is the process of algorith-
mizing different urban activities and processes by
converting their informal description into a set of
well-defined instructions that can be used to perform
a large-scale computation using mathematical and logi-
cal models for calculating specific functions, such as
predicting a human user behavior, inferring a health
or social status, augmenting a cognitive process, read-
ing brain activities, and taking a decision on behalf of
a human user. e AI and Big Data technologies, as a
by-product of their normal operation, involve analyz-
ing and interpreting massive amounts of data on citi-
zens, places, and everyday objects to make decisions.
estrong tendency to algorithmize the different areas
of urban activity entails that AI algorithms take con-
trol of decision-making due to their perceived capac-
ity of analysing constantly generated data, predicting
the consequences of the decisions at play, and acting
according to value maximisation criteria (Calvo, 2020,
p. 1).Data-drivensmart cities use numerous algorith-
mic tools and techniques to process the data collected
from the monitoring of digital citizensand urban sys-
tems through extensive networks of data sources. is
approach reduces the rich complexity of urban life
and the unpredictability of urban systems to narrow
quantitive and unitary languages. e reduced aspects
embody cultural, ethical, social, and political values,
nevertheless. Marked by functionalist visions, the fic-
tional virtual cities depicted by the Metaversetend to
portray an algorithmic order, mirrored in the uniform
functionalism of data-driven smart cities, where the
fluidity,contingency, multidimensionality, complexity,
and relationality of their systems, as well as the crea-
tivity, spontaneity, and emotionality of their citizens,
are submitted to a techno-utopian fantasy of complete
logical and calculative ordering.
4.1.4 Datacation
Agorithmizaton and platformization have been made
possible by the marked intensification of the datafi-
cation of citizens and places in terms of the radical
changes in the volume, granularity, heterogeneity, veloc-
ity, and veracity of the data being generated about every
aspect of urban life thanks to digital hyper-connectivity
and digital instrumentation. In other words, the instru-
mentation, datafication, and hyper-connectivity of the
city have given rise to the process of algorithmizing and
platformizing the different activities in the city. Data-
fication refers to the practice of taking a social activ-
ity, behavior, or process and turning it into meaningful
data (Cukier and Mayer-Schöenberger, 2013), or to the
act of transforming something into a quantified format
(O’Neil and Schutt, 2013) so it can be structured, tabu-
lated, and analysed (Cukier and Mayer-Schöenberger,
2013). As argued by (Cresswell, 2014) it is the datafica-
tion of the people and the geocoding of everything that
are rendering data suddenly big. e processes of trans-
forming social action into quantified data allows compa-
nies and government agencies to carry out monitoring
and predictive analysis in real time of digital citizens
via AI algorithms (van Dijck, 2014, 2016)—algorithmi-
zation. is implies that datafication is associated with
data-driven AI analytics that permit more sophisticated
mathematical and logical analyses to identify non-
linear relationships among data for massive predictive
analyses.
Smart cities are dependent upon their data to oper-
ate properly—and even to function at all with regard
to almost all domains of urban life. In other words,
smart city services and operational governance
highly respond to a form of data-driven urbanism
that reduces urban life to algorithmic rules and pro-
cedures (Kitchin, 2016) thanks to datafication. Smart
cities are taking any possible quantifiable metric and
squeezing value out of it for enhanced decision–mak-
ing and deep insights pertaining to many domains of
urban life. We generate enormous amounts of data on
a daily basis, a binary trail of breadcrumbs that forms a
map of urban life in terms of citizens’ experiences and
urban dynamics, and the resulting disparate datasets
can, if harnessed properly, open up a unique window
of, and represent a goldmine, opportunity for making
cities smarter and in tune with citizens’ actual needs
and aspirations.
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Bibrietal. Computational Urban Science (2022) 2:24
4.1.5 Digital Hyper‑connectivity
Underlyingthe processes of algorthmization, platformi-
zation, and datafication is the digital hyper-connectivity
of multiple systems, devices, things, and people. Related
to the IoT, hyper-connectivity refers to the connectivity
and interaction of everything that exist in digital envi-
ronments, including systems, devices, objects, things,
processes, activities, people, and data. With reference to
smart cities, Calvo (2020, p. 141) describes digital hyper-
connectivity as a three-pronged concept being:
• e trend towards the digital connectivity of every-
thing
• e governance of the involved processes and the
connected things enabled by the application of AI
algorithms
• e AI algorithms fed through the data generation
and analysis processes with “the objective, relevant
information they need so they can make effective,
efficient decisions capable of optimizing processes
and making the behaviour of all the connected things
in the system more predictable.”
e widespread diffusion of multiple wireless tech-
nologies, especially various 5G networks, will opti-
mize and advance the sensing and collection of massive
repositories of spatiotemporal data that represent
society-wide proxies for human interactions and activi-
ties. e growing capabilities of 5G amounting to up to
10Gb (Lee etal., 2021) are providing new opportunities
to the Metaverse as a giant ecosystem application that
relies on the real-time transmission of colossal amounts
of data. e increasing connectivity hinged on current
5G speeds and anticipated 6G speeds is expected to
play a significant role in realizing the Metaverse vision
(Allam etal. 2022b). Especially, it is expected that the
Metaverse’s requirements will exceed 5G’s available
bandwidth (Braud etal., 2020). e centrality of digitally
enabled connectivity in understanding the consequences
of the digitization, digitalization, and datafication is a
product of two interrelated social constraints: (a) limited
information processing abilities; and (b) visibility of data
regardless of whether they are actively or willingly pro-
vided for decision making (Leonardi and Treem, 2020).
is is at the core of the Metaverse as an organization
whose reality is shaped and constrained by the finite
limits of its ability to experience connectivity, regard-
less of the way it perceives the processes of data capture,
storage, and representation as regards gaining detailed
insights into human users due to the lack of produc-
ing representations of large, complex data. However,
the global architecture of computer mediation under-
lying the Metaverse and its technical infrastructure
connecting users have grown more robust. With this
constant connectivity, the behaviors of people, organiza-
tions, and even technological devices and the real world
are, by association, expected to be able to be visible
(Flyverbom, 2019; Flyverbom etal., 2016).
4.1.6 Digital Instrumentation/Data Infrastructure
Digital instrumentation gives rise to hyper-connectivity
and is aimed at producing big data viadevices and data
infrastructure, which in turn feedthe collective tools,
mechanisms, and instruments that transform the city
into a data-driven enterprise. e latter is process of
datafication is manifest in a variety of forms and can
also be associated with the IoT and sensors as part of
data infrastructure. is in turn relates to the informa-
tion layer of data-driven smart cities, which involves
the whole complex of data sources, including numerous
types of sensors, cameras, transponders, meters, actua-
tors, GPS, and transduction loops monitoring various
phenomena, as well as a multitude of smartphone apps
and sharing economy platforms generating a range of
real-time location, movement, and activity data (Bibri
and Krogstie, 2020b). ese data are routinely gener-
ated about citizens and places by a range of private and
public organizations. Smart cities are instrumented
with digital devices and infrastructure that produce
large amounts of data that enable real-time analy-
sis of urban life and new modes of urban governance
(Kitchin, 2014). Digital instrumentation is opening up
dramatically different forms of the social organization
(Batty et al., 2012) resulting from social interactions
and activities, i.e., steering cities as well as control-
ling urban ways of living. It involves how data can be
collected and analyzed, services can be organized and
delivered, and operations can be streamlined. It is the
domain of ICT companies themselves that are provid-
ing the detailed hardware and software of the operating
system for emerging data-driven smart cities.
e data infrastructure and operating system for
the city form what is called the horizontal informa-
tion system for the city. e development of the data
infrastructure has been captured through the notion of
datafication: the ways in which digital platforms render
into data, practices, and processes that elude quantifica-
tion (Kitchin, 2014; Cukier and Mayer-Schöenberger,
2013; van Dijck, 2014; Mejias and Couldry, 2019). e
operating system involves the tools used for storing,
analyzing, and processing the data collected, as well as
for interpreting these data, making forecasts on their
basis, and identifying interconnection between different
data ranges (Nikitin et al., 2016). e horizontal infor-
mation system is one of the key components of the ICT
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Bibrietal. Computational Urban Science (2022) 2:24
infrastructure of data-driven smart cities with respect to
performing the main functions of large-scale computa-
tion based on AI and big data analytics (Table1) as well
as linking together diverse smart technologies and solu-
tions to coordinate city systems and domains.
e quintessence of the idea of data-driven smart cit-
ies revolves around the necessity to coordinate and inte-
grate technologies and their underlying processes that
have clear synergies in their operation so that many new
opportunities can be realized for strategic stakeholders
through large-scale computation and platformization.
4.2 A Conceptual Framework fortheDigital
andComputing Processes Underlying theMetaverse
e integrated framework illustrated in Fig.1 is derived
based on thematic analysis in terms of the identified
core dimensions of the global architecture of computer
mediation underlying the Metaverse as a virtual form of
data-driven smart cities. It attempts to capture in a struc-
tured manner the underlying components of the digital
and computing platform of the Metaverse. e basic idea
revolves around the integration and combination of the
same digital and computing processes enabling data-
driven smart cities to build the Metaverse as a free-form
design of virtually inhabitable cities. is is predicated on
the assumption that speculative fiction plays an impor-
tant role in shaping alternatives to the imaginaries of
data-driven smart cities (Bina etal., 2020). e Metaverse
seems to be edging closer to realitywhile paving the way
for the emergence of virtual cities (Bibri, 2022), which,
as found by Hemmati (2022), create more believable
images than reality compared to real-world cities. is
form of urban transformation has far-reaching implica-
tions for the way people will live in urban society—if the
Metaverse is realized and deployed.
Fig. 1 A conceptual framework for the digital and computing
processes underlying the Metaverse as a virtual form of data-driven
smart urbanism
Table 1 The key functions of the horizontal information system for data-driven smart cities
Source: Adapted from Bibri and Krogstie (2020a)
• Providing open platforms connecting the sensors installed and integrating the obtained sensed data
• Aggregating and standardizing the flows of functional and territorial data from municipal sources, the systems of state control (mobility, energy,
pollution level, etc.), business environment, and other state agencies (hospitals, cultural institutions, universities, schools, etc.), as well as from various
surveillance (e.g., geosurveillance) technologies, for their subsequent integrated analysis and visualization in 3D format
• Solving data disconnection problems through the open operating system that integrates and processes the information generated from urban
sources
• Reworking and repackaging the collected data for daily consumption by different stakeholders
• Allowing the city authorities and third party users to gain access to the received data in a more structured and convenient manner for software
development Integrating self-contained and unconnected solutions and the information systems used in the different functional departments of the
city
• Improving the efficiency and performance of applied technological solutions
• Allowing the city authorities to take decisions on the optimization of urban activities on the short, medium, and long term basis.
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Bibrietal. Computational Urban Science (2022) 2:24
4.3 The Risks andImpacts oftheMetaverse asaProcess
ofPlatformization
Based on thematic analysis, the process of platformiza-
tion—underpinned by digital instrumentation, digital
hyper-connectivity, datafication, and algorithmization—
is examined and discussed in terms of its risks to and
impacts on urban society in the post-pandemic era.
4.3.1 Platformization: Institutional Dimensions andSocial
Implications
e practice and process of platformization has brought
about a major digital transformation of the key sectors
of urban society. is implies that institutional changes,
cultural practices, digital technologies, and platforms
are inextricably interrelated. ere are many manifesta-
tions of this complex interplay, one of which is platform
urbanism which has become central to the governance,
economy, and experience of the city. Fields etal. (2020)
provide insights into understanding the politics of plat-
form urbanism. Caprotti etal. (2022) argue that platform
urbanism as an evolution of the smart city is consti-
tuted by novel digitally enabled socio-technical assem-
blages that enable new forms of social, economic, and
political intermediation and transaction. e increased
datafication and algorithmization of social action facili-
tate new opportunities for organizing diverse forms of
social organizations. Platforms constitute a key organi-
zational strategy and operational logic of platform capi-
talism (Pasquale, 2016; Srnicek, 2017), digital capitalism
(Faulkner-Gurstein and Wyatt, 2021, Wajcman 2015),
surveillance capitalism (Zuboff 2019), and platform soci-
ety (Van Dijck etal., 2018), all of which support domi-
nant system of global capitalism. From a critical political
economy perspective, platformization involves the pro-
cess of intensifying the power and governance of global
platform (Poell etal., 2019). Critical political economists
have drawn attention to issues of surveillance and impe-
rialism (Fuchs, 2017). Platforms are not politically neutral
(Gillespie, 2010) and amplify the power of big tech com-
panies that control them, creating new potentials for dis-
cipline and surveillance.
In light of the above, it is important to gain insights into
how changes in the key institutional dimensions of plat-
formization are intertwined in a complex interplay. ese
dimensions—data infrastructures, market relations, and
governance frameworks—are simultaneously shaped by
the (re-)organization of cultural practices around plat-
forms as a result of platformization (Poell etal., 2019).
Data infrastructures involve technologies and solutions
that allow the collection and transfer of data for their fur-
ther processing and analysis. Data handing as a resource
for urban management and economies is a key feature of
smart urbanism and platform urbanism. Both of these
rely on pervasive and ubiquitous sensing and comput-
ing across digital urban spaces, as well as sophisticated
analytics and advanced algorithms, thereby the central-
ity of data to the functioning of computing urban plat-
forms. Data capture and usage are linked to “platform
accumulation” in terms of deepening privatization, mar-
ketization, commodification, and consolidation forms
pertaining to neoliberal capitalism (Meier and Manze-
rolle, 2018). In this respect, behavioral data collection is
afforded by expanding platform infrastructures (Nieborg
and Helmond, 2019) and their integration with a grow-
ing number of devices across many spheres of urban life.
e myriad of the extensions pertaining to platformiza-
tion allows platform operators, e.g., Meta, Google, Apple,
and Microsoft, to transform virtually every instance of
human social (inter-)action into data—datafication. is
process is then algorithmized and haphazardly made
available to a wide variety of external actors (Bucher,
2018; Langlois and Elmer, 2013). is connects well with
the applications layer of data-driven smart cities that
serves for the exchange of data among all the interested
parties and the adoption of solutions based on the analy-
sis of the collected data (Bibri and Krogstie, 2020b). is
layer involves platforms with open data and tools of data
visualization used for control over management system,
automated systems of response to city-wide events, as
well as a plethora of applications developed by city gov-
ernments, state agencies, and other external developers.
Furthermore, market relations have significantly been
shaped by the re-organization of cultural practices
around platforms in terms of the means of society to
communicate values and ways of living through social
and behavioral interactions. is is due to the emer-
gence of surveillance capitalism, which works by moni-
toring people’s behaviors and movements online and in
the physical world to capture their data for monetiza-
tion, trading, and exploitation. Surveillance capitalism
is one-sided claiming of the free raw material of private
human experience for translation into behavioral data for
profit and control (Zuboff, 2019). As a global platform,
the Metaverse epitomizes the market-driven process of
surveillance capitalism in terms of trading user personal
information by translating it into behavioral data, relying
on the mass surveillance of the Internet and thus scru-
tinizing online interactions, communications, and activi-
ties (Bibri and Allam, 2022b). ese data are repackaged
as prediction products with respect to what people will
do now, soon, and in the future that are sold to behavioral
futures markets—and offered to government elites. is
repackaging is a multi-billion dollar industry consist-
ing of a diverse ecosystem of different types of specialist
companies as data brokers that are focused on specific
markets (Kitchin, 2016). ese companies offer services
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Bibrietal. Computational Urban Science (2022) 2:24
that are used to regulate, control, and govern end-users
as well as the various systems and platforms with which
they interact (Kitchin, 2014). Critical political economists
argue that platform operators “are fully in charge of a
platform’s techno-economic development” and therefore
the power relations among platform operators, third par-
ties, data brokers, and end-users are inherently asym-
metrical (Poell etal., 2019). In particular, expanding the
global market for new technological services and thus
platforms often ignores their wider impacts on users and
consumers. erefore, consuming the Metaverse tech-
nologies must be approached carefully because big tech
companies as centralized structures often have hidden,
and are driven by, economic and political motives. Like-
wise, as argued by Viitanen and Kingston (2015), in smart
city systems as a digital marketplace, citizen participation
tends to be involuntary while the hegemony of big tech
companies is inflated, resulting in a digital user experi-
ence with inherent biases and exclusionary issues.
In addition, platforms constitute increasingly complex
multi-sided markets, and their arrangements in terms of
aggregating transactions among a wide variety of end-
users and third parties affect the distribution of economic
power and wealth due to strong network effects (Poell
etal., 2019). is pertains to platform intermediation in
terms of how the relatively autonomous actors are con-
vened and coordinated, and thus to platform capitalism
as a process by which the intermediated network is seen
a profit-making and investment channelling (Faulkner-
Gurstein and Wyatt, 2021). As pointed out by Langley
and Leyshon (2017), the processes of capitalization and
the practices of intermediation are turned on by the
generative force of the platform in digital economic cir-
culation in a variety of ways. e Metaverse is attracting
considerable investment, funding, and public attention
and thereby giving rise to numerous R&D projects, pro-
grams, and consortia across a plethora of business and
industry domains. It is pushing the global market towards
unparalleled profitable paths. Meta and other platform
providers, as well as major corporations, have begun
investing billions of dollars to develop the Metaverse
given the rising prospect that it will greatly impact urban
society over the next decade. Bibri and Allam (2022b)
discuss the financial gains and economic implications
of the Metaverse in relation to immersive technologies.
Johnson (2022) provides recent statistics and facts on the
market capitalization of the Metaverse, Meta, and gam-
ing worldwide. Lee et al. (2021) discuss in more detail
the industry’s market structure of the Metaverse. In addi-
tion, as a digital twin of work in the physical world, the
Metaverse platform will promote all kinds of brands.
Given the rich diversity of technologies featured in the
Metaverse and the broad variety of potential products
and applications, it is believed that the economic pros-
pects of the Metaverse will eventually justify current and
future investments.
Platforms are becoming one of the key contemporary
political–economic formations (Just, 2018; Vallas and
Schor, 2020; Van Dijck etal., 2018) in terms of govern-
ance. Platforms steer both platform-based user inter-
actions and economic transactions (Poell et al., 2019),
which is associated with the governance dimension of
platformization (Gillespie, 2018; Gorwa, 2019). is form
of delegated governance represents a political approach
to keeping platforms on task, where a larger framework
of centralized power contains decentralized control and
autonomy (Faulkner-Gurstein and Wyatt, 2021). Delegat-
ing control among actors is about exercising power over
economic transactions by platforms, as apposed to hier-
archies in terms of centralized power, markets as regards
dispersed power, or networks as to parcelling power out
to trusted collaborators (Vallas and Schor (2020). In this
respect, structuring how end-users can interact with each
other and other actors in the form of platform govern-
ance materializes through algorithmic sorting, thereby
shaping what types of services become prominently
visible and what remains largely out of sight (Bucher,
2018; Pasquale, 2015). Platforms govern through poli-
cies, which have to be agreed with when accessing plat-
form’s services (van Dijck, 2013). On the basis of these
terms and guidelines, platforms moderate what end-
users can share and how they interact with each other
(Gillespie, 2018), thereby conducting the actions and
affairs of people with authority. is broadly relates to
the exercise of “platform power” (Cohen, 2016). Within
the Metaverse as aglobal platform, there are economic
and political actors who exercise domination over others,
which is associated with the politics of delegation and
the politics of domination in terms of governance and
government within platforms. However, there are often
disputes and disagreements with local rules, regulatory
frameworks, and social norms because platforms tend to
use algorithms, interfaces, and policies as different gov-
erning instruments—without considering political and
cultural traditions (Poell etal., 2019). Still, platformiza-
tion is increasingly marked by strong state support and
oversight (De Kloet etal., 2019) with respect to how this
process is steered and managed by platform providers,
such as Meta, Google, Apple, Microsoft, and Cisco, in
collaboration with governments. is has become vis-
ible in the aftermath of the COVID-19 pandemic. As a
way to help combat this pandemic, a number of compa-
nies are actively repurposing their platforms and data.
Google and Apple are developing solutions to aid con-
tact tracing via smartphones (Brandom and Robertson,
2020); Google is monitoring the effects of interventionist
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Bibrietal. Computational Urban Science (2022) 2:24
measures globally; and Meta, Apple, Google, and Micro-
soft are generating and storing real-time location and
movement data while legitimating surveillance capitalism
as well as invasively harvesting and exploiting personal
(behavioral) data for profit-making (Kitchin, 2020).
4.3.2 The COVID‑19 Crisis andtheEnsuing Non‑Spontaneous
"Normality" ofSocial Order
e COVID-19 pandemic has forced new ways of living
digitally in the urban world, drastically changing urban
landscape in terms of the evolving urban patterns and
the shifting nature of urban life. e abrupt digital trans-
formation that has swept through the urban world in the
aftermath of the COVID-19 pandemic, coupled with its
disruptive impacts on people’s everyday life, seems to be
in tandem with the envisioning process of the Metaverse
in terms of its ultimate goal to datafy, algorithmize, and
platformize urban ways of living towards virtual alterna-
tives to the imaginaries of data-driven smart cities. Urban
scholars have long explored fictional and imaginary rep-
resentations of the city and urban life and their roles in
shaping and framing urban change (Abbott, 2016; Bassett
and Steinmueller, 2013; Dunn etal., 2014). However, the
Metaverse was launched amid the COVID-19 pandemic,
a crisis purported to be a rare opportunity that should be
seized to reset and reimagine the urban world—though
mainly in regard to its digital incarnation. Consequently,
the “new normal” enforced by this crisis was nothing
near to what—is termed in complex systems theory—
“spontaneous order” or “self-organization.” e latter
relates to the evolutionary resilience in the urban context
(Davoudi etal., 2012), which denotes the ability of a com-
plex system to not only bounce back from events caus-
ing a shock through robust behavior, but also to adapt
and learn from the past behaviors to surpass the previous
states by extending its capacity (Gunderson and Holling,
2002). As self–organizing social networks embedded
in space and enabled by infrastructures, activities, and
services (Bettencourt, 2014), cities are quintessential
complex systems that exhibit unplanned order or self-
organized behavior out of seemingly perceived chaos.
Self–organization is created and controlled by no one. It
results from human actions—not from human designs
(Hayek, 1978) as the case of the “new normal” that is
rather exhibited out of a chaos of another kind. Central
to self–organization is that the actions of a group of indi-
vidual constituents of a complex system are coordinated
without centralized planning. is dynamical property of
complex systems seems to be not characteristic of how
urban society is bouncing back from the COVID-19 pan-
demic and adapting and learning from the past behaviors
to surpass the previous pandemics. Historically, pandem-
ics have been deeply impactful on the way cities have
evolved, thereby forcing the agendas aligning with the
prevalent narrative around the reset of urban society. In
his work on the great reset and its impact on ways of liv-
ing and working in cities as a result of the financial cri-
sis of 2007-2008. Florida (2010) discusses how the past
resets have shaped urban development, as well as what
technological trends will emerge from the great reset.
is work, which describes the future of cities, has been
criticised for taking an overly elitist viewpoint by over-
stating the potential impact of the elite class and over-
looking many socio-economic realities related to ways
and choices of living.
e COVID-19 pandemic has served governments as a
window of opportunity (Kingdon, 1984) to accelerate the
development and adoption of big data technologies and
thus digital transformation. Indeed, during this crisis,
the world has braced for the “new normal,” where the use
of advanced technologies have become mainstream and
more embedded into almost every realm of urban society.
As argued by Kitchin, 2020), the utility of the solutionist
technologies deployed has been oversold, and this cri-
sis served as an opportunity for governments to expand
the roll-out and normalization of surveillance technolo-
gies, with no intention of rolling them back after the
pandemic, and the “new normal” will include spatial sort-
ing as to entering to public and private spaces. e sys-
tems deployed to combat the COVID-19 pandemic will
become part of the “new normal” in monitoring and gov-
erning societies—and hence will not be turned off after
the crisis (Sadowski, 2020; Stanley and Granick, 2020). In
this respect, the state surveillance tends to “stick” when it
is justified by pandemic or crisis events. e same tech-
nologies that have demanded fine-grained knowledge
about movement, social networks, contact tracing, social
distancing, and health status during the COVID-19 pan-
demic (Angwin 2020; Schwartz and Crocker, 2020; Stan-
ley and Granick, 2020) will be utilized in the Metaverse
as part of the global architecture of computer mediation
upon which the implicit logic of surveillance capitalism
depends.
e COVID-19 crisis seems to be laying the ground-
work for shifting from data-driven smart urbanism to
virtual platform urbanism. As concluded by Caprotti
etal. (2022), there is a need to critically engage with plat-
form urbanism in regard to its development in response
to the COVID-19 pandemic, as well as how it may shape
visions of the current and future reality in the city. As the
imaginaries of smart cities have shown, the ways futures
are imagined can frame and shape how urban societies
and settlements evolve in their names (Bina etal., 2020).
Fictional representations convey both “ future possi-
bilities” and “warning signals” (Miles, 1990, 1993; Pop-
per, 2009). With respect to the latter, the kind of digital
Page 14 of 22
Bibrietal. Computational Urban Science (2022) 2:24
transformation that is—accelerated by the COVID-19
pandemic—and reflected in the core vision of the
Metaverse has been argued to be not for the bettergiven
the ethical, social, and political issues and risks it has
raised. Since the onset of this crisis and its multifarious
consequences have made it clear that its impact will not
fade any time soon, and it will have a long-lasting impact
on urban society and ways of living in it. erefore, it has
become of crucial importance to understand and find
ways to address the risks and impacts of the rapid roll-
out of technologies across every sphere of urban society
as regards technocracy, technocentricity, personal auton-
omy, freedom, privacy, cybersecurity, discrimination, and
social exclusion, but to name a few (e.g., Allam, 2019,
2020, Aouragh et al. 2020; Calvo, 2020; Kitchin, 2020;
Lee etal., 2020; McDonald, 2020; Stanley and Granick,
2020; Taeihagh, 2021; Taeihagh et al., 2021; Tan etal.,
2021), ese concerns are expected to exacerbate with
the Metaverse (e.g., Bibri and Allam, 2022a, b; Gurov
and Konkova, 2022; Falchuk etal., 2018; Lee etal., 2021;
Rosenberg, 2022). is is predicated on the assumption
that the magnitude of the data to be generated by the
Metaverse will be far greater than that being collected
from the Internet today due to the technical operational
features of immersive technologies.
4.3.3 Data‑Driven Corporate‑led Technocratic Governance
e recent large-scale digital transformation of urban
society has raised serious concerns and provoked dis-
turbing questions about the core values of urban society
being undermined or eroded. is situation has exacer-
bated the risks and other negative implications of smart
urbanism and smart governance (Table 2). Emerging
research within smart urbanism is increasingly inves-
tigating the associated empirical realities as they move
from slick sales pitched by corporations to become new
urban realities (Cowley et al., 2018; Cugurullo, 2017;
Datta, 2015; Vanolo, 2016). In smart urbanism, citizens
are managed and manipulated as a function of datasets
in order to control urban governance and urban ways
of living (Marvin et al., 2016). e data-driven smart
urbanism model for sustainable development (Bibri,
2021c, 2021d) entrenches the idea that there are “no
alternatives” to techno-managerialist governance of cit-
ies (Vanolo, 2014) by being promoted as optimizing and
enhancing urban management through “standardized
decision-making” (Joss 2016) that prioritizes efficiency
over political action (Vanolo, 2014), which is seen as
impediment (Bina etal., 2020). In data-driven govern-
ance, citizens play a “subaltern role” (Vanolo, 2016) and
there is no real democratic participation (Hollands,
2015; Kitchin, 2014).
Data-driven smart city systems “become a digital
marketplace where citizen-consumers’ participation
is increasingly involuntary and…are defined through a
digital consumer experience that has inherent biases and
leaves parts of the city and its population unaccounted
for. is renders the city less resilient in the face of future
social…risks” (Viitanen & Kingston, 2015). Paradoxically,
ubiquitous citizen sensing and computing across digital
Table 2 The key issues and risks of smart urbanism and smart governance
Smart Urbanism Smart Governance
(e.g., Bina et al., 2020; Cardullo & Kitchin, 2018; Kitchin, 2014, 2016; Marvin
et al., 2016; Luque-Ayala & Marvin, 2015; Söderström & Paasche, 2014;
Sadowski, 2016; Verrest & Pfeffer, 2019)
(e.g., Barns, 2018; Grossi et al., 2020; Grossi & Pianezzi, 2017; Grossi et al.,
2020; Hollands, 2015; Kitchin, 2014; Sadowski & Pasquale, 2015; León &
Rosen, 2020; McFarlane & Söderström, 2017; Pereira et al., 2018).
• Ignoring social, ethical political, cultural, economic, and historical
contexts shaping urban life
• Curtailing the opportunities for wider perspectives beyond technical
systems and scientific processes
• Lacking the acknowledgement that the urban is not confined to the
administrative boundaries of the city
• Overlooking local social-economic, cultural-political, and environmen-
tal contingencies in analyzing the development, implementation, and
effects of urban policies
• Marginalizing certain groups and creating multiple divides between
those who have access to smart applications and those who do not
• Reinforcing neoliberal economic growth, focusing on more affluent
populations, and disempowering citizens
• Breaking urban systems into pieces and reducing urban life to
algorithmic processes to make the city knowable, manageable, and
controllable
• Pledging for sustainability as marketing strategy and overlooking
sustainability concerns
• Concealing those urban issues, conflicts, and controversies that cannot be
represented by digital models and embedded in data analytics techniques
• Emphasizing the government as the prime initiator of innovative solutions
and the private sector as their provider
• Treating urban governance merely as a management problem that can be
dealt with by making use of the power of big data analytics
• Perceiving urban problems as being solvable primarily through the appli-
cation of technologically derived knowledge
• Neglecting the role of contextualization and place-based knowledge in
shaping the process of governance
• Focusing too much on the technical, engineering, and economic dimen-
sions of urban governance while missing on the role of social processes in
configuring its meaning in practice
• Developing policies that are largely featured with the corporatization of
urban governance
• Resulting in highly unequal urban societies, characterized by unequal
power relations, social exclusion, and unbalanced distributions of costs and
benefits
• Cementing surveillance practices and submitting spontaneity of choices
to complete logical ordering.
Page 15 of 22
Bibrietal. Computational Urban Science (2022) 2:24
urban spaces are often represented as a way of enabling
progressive citizen empowerment as part of e-govern-
ment or smart governance. Focusing on the relationships
between ICT-enabled citizen-government collaboration
and social sustainability and how contextual circum-
stances influence these related elements, Tomor et al.
(2019) found that empirical evidence for the alleged ben-
efits in this regard is sparse, and the emerging picture is
ambiguous as it reports both positive and negative effects
regarding the achievements of smart governance. One of
the conclusions drawn by the authors is that smart gov-
ernance, in the sense of ICT-enabled government-citizen
collaboration, is still rare. Despite the increasing variety
of collaboration-based digital instruments, a one-way
information supply in citizen–government interactions
tends to dominate. Although governments promote
online citizen engagement and civic empowerment, they
do not encourage deliberation or any broad-based pub-
lic–civil interactions in practice. Urban affairs are framed
in socio-political configurations of technocratic regimes
and constituted in social constructions of big data sys-
tems as an apolitical or neutral matter, respectively, an
illusion of political neutrality and objective view of smart
technologies (Bibri, 2022; Söderström etal., 2014).
e Metaverse as a techno-urban utopia is built on the
monitoring of citizens and places through extensive net-
works of data collection, processed and analyzed via AI
algorithms and mathematical models. Mathematics pre-
sents an answer to a set of pre-defined variables, which is
why it appears “rational,” the algorithmic rules are made
up to get a certain outcome. Algorithmic governance
involves unevenness and inequity which reproduce data
justice issues (Dencik etal., 2016; Taylor, 2017) across dif-
ferent demographics (Benjamin, 2019; Noble, 2018) with
potentially harmful consequences. e quantified, digi-
tally stored, manipulatable, and shared information on
users and consumers is seen as a key source of exploit-
able, investable value (Faulkner-Gurstein & Wyatt, 2021).
Regardless, technocratic governance replaces democratic
policy-making and politics and data-driven AI systems
replace wider urban knowledge and expertise (Chandler,
2015; Söderström etal., 2014). Urban life is far more than
digital imprisonment. Democracy is subordinated to the
governmental and corporate elites who control smart
technologies and govern “by code” (Söderström et al.,
2014, p. 315). Outsourcing democratic resilience increase
the power of the powerful elites, raising further con-
cerns over accountability, representation, and transpar-
ency (Bibri & Allam, 2022b). Regardless, the Metaverse
will be a digital marketplace where the supremacy and
dominance of big tech companies will be further inflated,
and its numerous virtual worlds will be defined through
the experience of human users that will reinforce control
and deepen inequality and social exclusion. is renders
the Metaverse way less equitable, inclusive, and safethan
data-driven smart cities in the face of future vulnerabili-
ties and risks.Overemphasising advanced computing and
immersive technologies in the context of data-driven
smart cities is more likelyto undermine social and ethi-
cal values (Allam 2020, Allam and Dhunny 2019, Allam
etal. 2022). As argued by Bina etal.(2020, p. 8) “fictional
representations powerfully explore the dystopian conse-
quences of the dream of dominium over natureand the
resultant production of extreme, oppressive, and unstable
environments, animating and extending a range of warn-
ing thatsocial scientific critique often touches upon”.
4.3.4 Governmentality
e consequences of massively deploying surveillance
technologies during the COVID-19 pandemic have been
argued to have significant downstream effects that are to
be suffered by citizens. is event has already affected
urban ways of living and the way citizens self-govern
themselves drastically. Kitchin (2020) questions the
technical and practical efficacy of surveillance technolo-
gies and examines their implications for governmental-
ity. is concept denotes how people govern themselves
(Foucault, 1991) or exercise government “beyond the
state” (Rose & Miller, 1992). As a term combining gov-
ernment and rationality, govenmentality represents the
tactics of government that allow it to define and redefine
what competencies it entails, or the calculated means
that allow it to shape, guide, or affect the conduct of peo-
ple. Accordingly, the state designs systems for defining
populations, including management and administration
mechanisms and ways of classifying individuals or groups
based on certain norms, which make them known and
visible by means of their identification, categorization,
and control (Foucault, 1977). Kitchin (2020) outlines an
agenda for documenting how surveillance technologies
unfold in practice and impact on governmentality. e
promotion and use of invasive technologies in the age of
surveillance capitalism trump concerns over civil liveries.
Routinizing new forms of social and spatial sorting as a
result of the new type of management enabled by surveil-
lance technologies has “the potential to permanently shift
the nature of governmentality and to also act as a path-
way towards authoritarian forms of governance where
technology is used to actively impose the will of the state
onto citizens” (Kitchin, 2020).
Numerous investigations have demonstrated that
states have a poor record when it comes to practicing
dataveillance (Lyon, 2015) and geosurveillance, which
lend a legitimacy to authoritarianism concerns. Data-
veillance entails the systematic surveillance of people’s
activities and behaviors on the Internet. Monitoring
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Bibrietal. Computational Urban Science (2022) 2:24
and investigating the digital data pertaining to personal
details and online and virtual interactions, actions, and
communications will be the primary purpose of the crea-
tion and use of data in the Metaverse. Geosurveillance
is the tracking and tracing of location and movement of
people, vehicles, goods, objects, products, and services
and the monitoring of interactions and relationships
across space and time. With the event of the COVID-19
crisis, technologies beyond smartphone infrastructure,
such as the IoT, AI systems, Big Data ecosystems, Edge
Computing, XR, Blockchain are being subject to control
creep, i.e., their original purpose is being extended to per-
form mass surveillance in order to normalize and cement
the new biopolitical architecture of urban society. Cen-
tral to the biopolitics (Foucault, 1977) of the COVID-19
pandemic is to control bodies and their movement and
to trace their contact. Being thoroughly spatial in regard
to its articulation, it regulates public and private spaces
and spatial access and behavior, as well as generates par-
ticular forms of spatiality (Kitchin, 2020). With reference
to the practice of governmental surveillance, Crampton
(2003) argues that surveillance and security operate by
establishing norms that assess risks and threats, which
entails deploying geosurveillance in response to danger-
ousness and subjecting people to management as at-risk
resources. is practice relates to what Foucault (1977)
calls a “governmental society,” which operates at the
level of populations and their distribution across terri-
tory. e technocratic, algorithmic, automated nature of
technologies can shift the governmental logic from sur-
veillance and discipline to capture and control (Deleuze
1992). Given the long-lasting impact of the COVID-19
pandemic, urban ways of living will be intimately and
permanently interwoven with data-driven governmen-
tality. erefore, it is important to critically engage with
how this form of platformization may create alternatives
to the imaginary of data-driven smart cities based on the
fictional representations of future urban worlds imagined
by the Metaverse.
4.3.5 Privacy, Security, andTrust
e concern about privacy is part of a larger concern
about control, about people having control over their
own lives. is contradicts the logic of surveillance capi-
talism, which underpins platform society—where plat-
forms have penetrated the core of urban society, affecting
civic and public practices and democratic and ethical
values. e responsibility of “anchoring public values
and the common good in a platform society,” including
privacy, security, and safety, as well as fairness, control,
and accountability (Van Dijck et al., 2018) is increas-
ingly being outsourced to the global technology sector.
Platformized surveillance is at the heart of data-driven
smart cities and thus the Metaverse. With respect to the
former, Calvo (2020) addresses the moral implications of
the hyper-connectivity, datafication, and algorithmiza-
tion of urban society within the ethical realm of smart
cities. Kitchin (2016) examines the ethics of smart cit-
ies, focusing on privacy, datafication, dataveillance, and
geosurveillance. e author argues that smart city initia-
tives need to be re-cast in ways that adopt ethical princi-
ples designed to realize the benefits of smart cities while
reducing pernicious effects. Drawing on this study, Bibri
and Allam (2022a) examine the forms, practices, and
ethics of the Metaverse as a virtual form of data-driven
smart cities, paying particular attention to: privacy,
dataveillance, and geosurveillance, among others. e
authors highlight the ethical implications the Metaverse
will have on the experience of everyday life in post-
pandemic urban society. ey argue that the Metaverse
will do more harm than good to human users due to
themassive misuse of the hyper-connectivity, datafica-
tion, algorithmization, and platformization underly-
ing the global architecture of computer mediation upon
which surveillance capitalism depends. However, privacy
threats are worrying most of the users and consumers of
the Metaverse, as the privacy–enhancing mechanisms
proposed thus far remain inadequate to solve this ethi-
cal conundrum. In reality, technology can only safeguard
privacy, and even this potential is associated with inher-
ent limitations and embedded flaws. us, privacy is a
real challenge and quandary facing the Metaverse (e.g.,
Acquisti et al., 2011, Acquisti et al., 2014; Dick, 2020;
Falchuk etal., 2018; Lee etal., 2021; Leenes, 2007), espe-
cially in relation to face recognition and edge computing.
Not only the issue of privacy but also the issues of
security, trust, and accountability have long been, and
continue to be, a subject of much debate and an area of
intensive research (e.g., Alqubaisi et al., 2020; Mollah
et al., 2017; Boddington., 2021; Cuzzocrea, 2014; Lee
et al., 2021; Liu et al., 2015; Haber, 2020; Ouda etal.,
2010; Ryan2011). Based on recent statistics published
by Johnson (2022), among the concerns posed by the
Metaverse are, in addition to privacy, hacking, trust, data
abuse, and identity protection. Lee etal. (2021) provide
a detailed discussion on security, trust, and accountabil-
ity, as well as privacy, in the context of the Metaverse.
While much of ongoing debate revolve around accept-
able practices in regard to accessing and disclosing per-
sonal and sensitive information about people, the era of
Artificial Intelligence of ings (AIoT) marks the end of
privacy. What is risky to the users of the Metaverse is
the idea that this platform will be steered and controlled
by big data companies—considering the aggressive tac-
tics and engagement strategies that are currently being
used in social media platforms for malicious purposes.
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Bibrietal. Computational Urban Science (2022) 2:24
e risks to the users of the Metaverse can notbe miti-
gatedor solved by changing the business models of plat-
form providers or by establishing strong industry norms
among them (Rosenberg, 2022). e idea of platforms
being seen as black boxes emanates from the proprietary
nature of AI algorithms, corporate ownership, and con-
trol and thus the concerns about scrutiny, accountability
and transparency, and addressing them reflects a lim-
ited and limiting horizon and potential of socio-politi-
cal solutions. It follows that the Metaverse will most
likely employ new deceptive methods based on opaque
and largely invisible algorithms to impede the ability of
people to grasp their ethical and societal implications,
as well as to keep them unaware at best and ignorant at
worst of the kind of arrangements that are intricately
interwoven with governmental apparatuses and their
techniques (Bibri & Allam, 2022b). Technologies that
are designed to deliver specific services are enrolled into
policing and security apparatuses (Kitchin, 2020). With
reference to social media platforms, Fuchs (2017) found
that the surveillance capitalism fuses with the surveil-
lance state. is issue is further complicated by hidden
collaborative arrangements with state security appara-
tuses (Zuboff, 2019). It follows that the regulatory frame-
works that control dataveillance and geosurveillance as
main reasons for privacy harms are most likely not to
be enacted by or enforced on big tech companies due to
their vested interests with other large corporations and
government agencies.
4.3.6 Data Governance
Data governance is a complex and slippery concept,
especially when it comes to its implementation as a set of
decisions, and in different settings. It relates to the politi-
cal dimension of Internet governance and international
relations, that is, the governing of cross-border data flows
based on a whole system of policies, practices, and insti-
tutions managing various types of data. Policies are a set
of laws, rules, regulations, norms, and actions adopted
by governments and mediated by civic and public insti-
tutions. is entails the formation and utilization of net-
works for linking data between civic institutions across
urban society. Data governance refers to the institutional
systems that manage the processes of storing, processing,
analyzing, using, sharing, transacting, and trading data
by or in the name of the government (Bonina & Eaton,
2020). In data governance, efficiencies tend to be prior-
itized over regulatory requirements and user and con-
sumer services. In the wake of the COVID-19 pandemic,
many countries have enacted policies to govern data pro-
cesses to serve efficiency and effectiveness at the expense
of privacy, equity, and safety due to the accelerated roll-
out of digital technologies to combat it. Li et al. (2022)
explore the extent to which the COVID-19 pandemic has
led to policy change in data governance and the impli-
cations of such change for the post-COVID-19 era. e
resulting extensive use and accelerated development of
digital technologies associated with the collection and
utilization of personal and sensitive data have raised and
intensify concerns regarding data governance, data pri-
vacy, and data security as a whole (Parker etal., 2020).
is is due to the fact that big data companies determine
the current research in the field of data governance, and
this has implications for developing and implementing
regulatory frameworks for data governance across many
domains of urban society. e development and use of
data tools for containing and controlling the COVID-19
pandemic have proven to have a long-lasting detrimental
impact on urban society and data governance, enacting
new and changing existing policies to achieve the “new
goals” of data-driven smart cities. To put it differently,
this crisis has exacerbated the issues of the increasing
involvement of big data companies in data policy and
data privacy through the accelerated adoption of big data
technologies (Li etal., 2022). erefore, there is a need to
critically investigate new power geometries of corporate,
legal, and regulatory alignments with respect to platform
urbanism (Caprotti etal., 2022), virtual platform urban-
ism (Bibri, 2022), and platform society (Van Dijck etal.,
2018) with respect to data governance and data privacy.
Data privacy measures and mechanisms have been a
subject of much debate since the early 1990s, as well as of
a great deal of activity in legislatures. is has resulted in
“data protection oversight agencies and a modest level of
jurisprudence” in many countries, while provisions that
enable dataveillance and geosurveillance are voluminous
(Clarke and Greenleaf 2017). Oversight decisions are
largely influenced by the lobbying of big tech companies
while insisting their evolving technology is too complex
and fast-moving to be regulated. Regardless, personal
data cannot be defined based on privacy regulations
alone, as these tend to lag behind technological innova-
tions due to their rapid pace, thereby the need to develop
a principled framework that keeps up with them as
regards what personal data mean. In this respect, Rosen-
berg (2022) propose some of the regulatory solutions to
mitigate the risks of the Metaverse,including restricting
monitoring, emotional analysis, virtual product place-
ments, and simulated personas. e author argues that
government and industry actors must consider aggressive
regulations promptly, predicated on the assumption that
it would become difficult to unwind them if the problems
are embedded in the business models and digital infra-
structure of the Metaverse. However, it is unfeasible to
enact these regulatory solutions— considering the cur-
rent reality of social media platforms where AI-based
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Bibrietal. Computational Urban Science (2022) 2:24
algorithms are designed to serve devious purposes,
although there are possibilities to implement the privacy-
by-design approach and its principles.
In addition, many governments have repealed privacy
protection to enable the widespread use of personal
data as a means to tackle the COVID-19 pandemic, and
this will become part of the “new normal” and hence
will not be revoked again after this crisis. is is just
like all the systems deployed to combat the COVID-19
pandemic (Sadowski, 2020). us, the COVID-19 pan-
demic will have profound impacts on the future of data
governance, the new measures will have lasting effects
on data privacy, and personal and sensitive data will be
shared across the different platforms of urban society.
e core values of society are globally the very stakes in
the struggle over its platformization, including disputes
and disagreements over regulation between platform
providers and city councils and power clashes between
global markets and (supra-)national governments (Van
Dijck etal., 2018).
5 Discussion andConclusion
is study analysed the emerging trends enabling and
driving data-driven smart cities based on a thematic
analysis approach in order to derive a conceptual frame-
work for the digital and computing processes underly-
ing the Metaverse as a virtual form of data-driven smart
urbanism. ese processes are: digital instrumentation,
digital hyper-connectivity, datafication, algorithmization,
and platformization. ey are inextricably interrelated in
that they shape and build on one another at the technical
and operational levels towards enabling the functioning
of the Metaverse and the future urban world it imagines.
e proposed framework represents a conceptual struc-
ture intended to serve as a guide for building a model
of virtual urbanism that can expand the structure into
something useful on the basis of further in-depth quali-
tative analyses, empirical investigations, and practical
implementations.
Further, this study examined and discussed the risks
and impacts of the digital and computing processes
underlying the Metaverse as a virtual form of data-driven
smart urbanism, paying particular attention to: plat-
formization; the COVID-19 crisis and the ensuing non-
spontaneous "normality" of social order; data-driven
corporate-led technocratic governance; governmental-
ity; privacy, security, and trust; and data governance.
is study argues that the digital and computing pro-
cesses—as intricately interwoven with the entirety of
urban ways of living—arouse contention and controversy
due to their negative effects on civic and public practices
and participatory and democratic processes. Due to
the inherent ethical and societal implications of science
and technology, more explicit democratic processes are
needed for enhancing civic participation in the shaping
of the Metaverse as a form of scientific and technologi-
cal development. e ultimate goal is to structure such
development in ways that are collectively the most dem-
ocratically beneficial for urban society. e concerns
over long-term data privacy and data governance as a
result of the wide deployment of big data technologies
may remain unabated, but it is necessary to devise con-
crete institutional measures and practices pertaining to
platformization in order to address and overcome these
concerns. Otherwise, they may lead to the deteriora-
tion of the quality of the governance of urban society
as a whole in the post-pandemic COVID-19 era, which
would hinder future government efforts to gain citizen
trust and encourage citizen cooperation. In other words,
citizen distrust in government could be exacerbated if
the development and use of big data technologies are
not carefully implemented to respond to citizen needs.
e Metaverse raises critical concerns about the gov-
ernance of urban society due to the logic of surveillance
capitalism and what constitutes the global architecture
of computer mediation it depends on with regard to the
underlying mechanisms that are designed to increase the
power of the powerful (corporate and government elites)
and undermine the public values of urban society. As
summarized by Zuboff (2019), surveillance capitalism is
best described "as a coup from above, not an overthrow
of the state but rather an overthrow of the people’s sov-
ereignty and a prominent force in the perilous drift
towards democratic de-consolidation that now threat-
ens Western liberal democracies” (Gray, 2019). Surveil-
lance capitalism leads to democratic backsliding, privacy
loss, and freedom erosion. Large corporations have
often been at the forefront of debates over such prac-
tices (Rikap & Lundvall, 2020), often criticised for not
re-assessing the process and practice of platformization.
erefore, governments in democracies must employ
new approaches when regulating long-lasting big data
technologies and their escalating rate and scale of use
based on deep analysis to avoid unexpected and poten-
tially disastrous or lethal consequences in the long run.
By looking closely at the institutional dimensions of
platformisation, it becomes clear how this multifac-
eted process and practice brings about a large-scale
digital transformation of the spheres of urban soci-
ety, why it raises serious concerns over the underly-
ing mechanisms, and what challenges it presents
for strategic actors. It is crucial to gain insights into
how changes in the dimensions of platformization
may shape one another—but rather in a mutual pro-
cess. In this regard, future endeavors need to focus
on finding ways to regulate the Metaverse as a global
Page 19 of 22
Bibrietal. Computational Urban Science (2022) 2:24
process and practice of platformization democrati-
cally, ethically, and effectively through relevant social
structures and institutions while understanding the
key underlying mechanisms at work. One of the key
challenges to address in this regard is to integrate
platforms in urban society without undermining cul-
tural features, such as norms, beliefs, and values, and
without increasing disparities in the distribution of
benefits and costs and of wealth and power. This is
a worthy scholarly endeavor in itself, so is the extent
to which a deeper understanding of the mechanisms
at play will bring concrete changes to the functioning
of the Metaverse. In addition, a holistic philosophical
and analytical framework needs to be developed and
applied to enhance the understanding of how politi-
cal and institutional changes are entangled with shift-
ing socio-cultural practices as a result of the emerging
socially, politically, and economically oriented plat-
forms and vice versa. The framework of Science,
Technology, and Society (STS) can bring new insights
into the ever-evolving dynamics and increasing com-
plexity of platformization. At the core of this frame-
work is a systemic exploration of the ways in which
different forms of science and technology emerge
and evolve and become institutionalized and socially
anchored—interwoven with policy and politics and
thus globally disseminated, as well as of the risks and
impacts of science and technology (Bibri, 2022). This
framework is essentially applied to investigate science
and technology in its wider social context (e.g., Bia-
gioli, 1999; Hess, 1997; Jasanoff etal., 1995; Sismondo,
2004). Indeed, a systemic inquiry into the relation-
ships between the institutional and social dimen-
sions of platformization as a form of scientific and
technological development is of crucial importance
because it will bring into view the tensions between
the Metaverse and institutional practices and govern-
ance frameworks.
Acknowledgements
Not applicable
Code availability
Not applicable
Authors’ contributions
Conceptualisation: SEB; Methodology: SEB; writing—original draft preparation:
SEB, ZA and JK; writing—review and editing, SEB, ZA and JK. The authors have
read and approved the published version of the manuscript.
Funding
The authors received no financial support for the research, authorship and/or
publication of this article.
Availability of data and materials
Not applicable
Declarations
Competing interests
The authors declared no potential conflicts of interest with respect to the
research, authorship and/or publication of this article.
Author details
1 Department of Computer Science, Norwegian University of Science
and Technology, Sem Saelands veie 9, NO–7491 Trondheim, Norway. 2 Depart-
ment of Architecture and Planning, Norwegian University of Science and Tech-
nology, Alfred Getz vei 3, Sentralbygg 1, 5th floor, NO–7491 Trondheim, Nor-
way. 3 Chaire Entrepreneuriat Territoire Innovation (ETI), IAE Paris—Sorbonne
Business School, Université Paris Panthéon-Sorbonne, 75013 Paris, France.
4 Live+Smart Research Lab, School of Architecture and Built Environment,
Deakin University, Geelong, VIC 3220, Australia.
Received: 26 April 2022 Accepted: 1 July 2022
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