City Digital Twins: their maturity level and differentiation from 3D city models

Figures & data

Table 1. Comparing digital twins’ definitions proposed in various perspectives and scopes.

Title	Scope	Definition
“Real-time traffic quantization using a mini edge artificial intelligence platform” (Broekman et al., 2021)	Freeway/smart cities/traffic	“An integrated multi-physics, multi-scale, and probabilistic simulation of a complex product and uses the best available physical models, sensor updates, etc., to mirror the life of its corresponding twin.”
“Smart cities in the time of climate change and Covid-19 need digital twins” (Dignan, 2020)	Climate change	“A digital model of a physical asset that continuously collects information (via sensors, drones or other IoT and IoT data collection tools) applies advanced analytics, machine learning, and artificial intelligence (AI) to gain valuable real-time insights about the physical asset’s performance, operation or profitability.”
“Digital twins’ conceptual model within the context of internet of things” (Al-Ali et al., 2020)	Transportation	“A simulation of a system or product mirroring the physical model into a virtual model”
“A Novel HDF-Based Data Compression and Integration Approach to Support BIM-GIS Practical Applications” (Pan et al., 2020)	Data management	“Dynamic digital reflection of the real urban environment, which should be capable of (1) containing as many cities features as possible, (2) synchronizing with its host dynamically through multiple urban data resources, and (3) forming a real-time and integrated response to the events.”
“Smart City 4.0 from the perspective of open innovation” (Yun & Lee, 2019)	Business	“The digital twins’ strategy where the real problems of the real city are solved in the virtual city through the process of smart transformation”
“Smart-guided pedestrian emergency evacuation in slender-shape infrastructure with digital twin simulations” (Yun & Lee, 2019)	Crisis management	“A Digital Twin is a high-fidelity representation of the operational dynamics of its physical counterpart, enabled by near real-time synchronization between cyberspace and physical space”
“An animated spatial time machine in co-creation: Reconstructing history using gamification integrated into 3d city modelling, 4d web and transmedia storytelling” (Matthys et al., 2021)	Animation	“A digital twin is, in origin, a digital copy of a physical object, and for some years, 3D models of cities have also been called Digital Twins.”

Figure 1. The flowchart of the research approach and process including paper selection, search string, inclusion and exclusion criteria, and records’ number.

Table 2. Number of publication records obtained based on analysis search strings and keywords in the database.

Database	Search String	Output numbers
Scopus	TITLE-ABS-KEY (“digital twins” AND city OR urban OR “built environment”)	360
Web of Science	“digital twins” (Topic) and city OR urban OR “built environment” (Topic)	148
Total articles	After applying filters and removing the duplicates	72
Total review articles	After selection of city-scale articles	41

Table 3. The sum of case studies per year related to the subject of study classified based on countries that the research had been done.

	Country	Frequencies
1	China	88
2	USA	57
3	UK	54
4	Spain	36
5	South Korea	28
6	Italy	14
7	Germany	13
8	Australia	9
9	India	9
10	Belgium	8

Figure 2. The published scientific literature on City Digital Twins by year until January 2022.

Figure 3. Results of co- authorship network analysis of the 131 documents reviewed related to City Digital Twins.

Figure 4. Results of mapping co-occurrence network analysis (plus-keywords) of the terms in the 131 documents reviewed on the City Digital Twins’ studies, identified in the developed bibliography.

Figure 5. Results of mapping the co-authorship network analysis among countries. The minimum number of co-occurrences of keywords was two.

Table 4. The results of classifying data types used in the existing City Digital Twins literature.

Type of data	Details of data	Description	References
Structured	Sensors data	Including different types of sensors varying from traditional sensors to wearable or personal devices	Park et al. (2019), Chaves et al. (2021), Laamarti et al. (2020), Conejos Fuertes et al. (2020), Lu et al. (2020), Lenfers et al. (2021), Sun et al. (2020), Dembski et al. (2020), Schrotter and Hurzeler (2020)
	Land administration data	Including land features and related attributes such as geographical features, cadastral maps, land-use type, land cover, ownership, and others.	Park et al. (2019), Conejos Fuertes et al. (2020), Beil et al. (2020), Buckley et al. (2021), Lu et al. (2020), Al-Ali et al. (2020), Huo et al. (2021), Simonsson et al. (2021), Park and Yang (2020a), Lenfers et al. (2021), Truu et al. (2021), Orozco-Messana et al. (2021), Bass et al. (2021), Dembski et al. (2020), Schrotter and Hurzeler (2020)
	Building information	Including information and data about buildings such as surface type, area, height, numbers of windows, and others.	Buckley et al. (2021), Lu et al. (2020), Al-Ali et al. (2020), Park and Yang (2020a), O’Dwyer et al. (2020), Orozco-Messana et al. (2021), Bass et al. (2021), Han et al. (2020), Schrotter and Hurzeler (2020)
	Socio-demographic data	Including socio-demographic attributes such as population, density, age, and others, and behavioral data	Gutierrez-Franco et al. (2021), Buckley et al. (2021), Han et al. (2020)
	Historical and time-series data	Including time-series data and previous documents data	Dembski et al. (2020), Matthys et al. (2021), Gutierrez-Franco et al. (2021), Lu et al. (2020), Al-Ali et al. (2020), Simonsson et al. (2021), Orozco-Messana et al. (2021), O’Dwyer et al. (2020), Major et al. (2021)
	3D data	Including 3D features, Point clouds, OSGB files and others.	Dembski et al. (2020), Pan et al. (2020), Matthys et al. (2021), Beil et al. (2020), Al-Ali et al. (2020), Park and Yang (2020a), Visconti et al. (2021), Lu et al. (2020), Schrotter and Hurzeler (2020)
	Traffic and Transportation	Including roads networks and data and data of vehicles	Dembski et al. (2020), Gutierrez-Franco et al. (2021), Beil et al. (2020), Park and Yang (2020a)
	Climate and weather	Including data related to weather, such as temperature, pollution, and others.	Dembski et al. (2020), Truu et al. (2021)
	Energy consumption	Including data related to the energy consumption of buildings or cities.	Park et al. (2019), Gutierrez-Franco et al. (2021), Buckley et al. (2021), Lu et al. (2020), Park and Yang (2020a), Bass et al. (2021)
	Infrastructure	Including infrastructure network and its related data.	Chaves et al. (2021), Conejos Fuertes et al. (2020), Gutierrez-Franco et al. (2021), Simonsson et al. (2021), O’Dwyer et al. (2020), Sun et al. (2020), Dembski et al. (2020)
Unstructured	Text	Including interviews, workshops, official plans, official documents	Matthys et al. (2021), Schrotter and Hurzeler (2020), Hämäläinen (2021), Çetin et al. (2021)
	Image	Including satellite images, aerial images, and documented images from the past.	Dembski et al. (2020), Pan et al. (2020), Matthys et al. (2021), Lu et al. (2020), Al-Ali et al. (2020), Huo et al. (2021), Broekman et al. (2021), Schrotter and Hurzeler (2020)
	Video	Including video traffics.	Broekman et al. (2021)

Table 5. Distribution of reviewed studies in the subject of research based on data sources used in their research.

Data sources	Description	References
Official documents	Including official plans, historical records (images and related documents), urban infrastructure documents, and an official geographical database.	Dembski et al. (2020), Park et al. (2019), Chaves et al. (2021), Matthys et al. (2021), Conejos Fuertes et al. (2020), Gutierrez-Franco et al. (2021), Buckley et al. (2021), Lu et al. (2020), Simonsson et al. (2021), Park and Yang (2020a), Truu et al. (2021), Orozco-Messana et al. (2021), Bass et al. (2021), Sun et al. (2020), Schrotter and Hurzeler (2020)
Satellites and aerial images	Including different satellite images, aerial images, and airborne imagery.	Dembski et al. (2020), Huo et al. (2021), Lenfers et al. (2021), Bass et al. (2021)
Sensors	Including different types of sensors, such as traditional sensors for physical phenomena and personal devices.	Park et al. (2019), Chaves et al. (2021), Laamarti et al. (2020)¸ Conejos Fuertes et al. (2020), Lu et al. (2020), Al-Ali et al. (2020), Lenfers et al. (2021), Sun et al. (2020), Ford and Wolf (2020), Major et al. (2021), Han et al. (2020)
Open sources	Including DWG open data and open-source websites (for geographical information, weather data, and others).	Matthys et al. (2021), Pang et al. (2021), Beil et al. (2020), Buckley et al. (2021), Al-Ali et al. (2020), Park and Yang (2020a), Orozco-Messana et al. (2021)
Aerial vehicles	Including devices such as drones and unmanned aircraft.	Lu et al. (2020), Al-Ali et al. (2020), Broekman et al. (2021), Anda, Medina, et al. (2021)
3D scanners	Including 3D scanner and Lidar.	Dembski et al. (2020), Schrotter and Hurzeler (2020)
Census and survey	Including official census and surveys	Dembski et al. (2020), Buckley et al. (2021), Truu et al. (2021)
Interviews and work shops	Including structured and semi-structured interviews.	Dembski et al. (2020), Matthys et al. (2021), Hämäläinen (2021)

Table 6. The results of analyzing case studies mentioned in the existing City Digital Twins literature.

Case study	Purpose	Technologies	Data type	Further discussion
Cambridge, England (Lu et al., 2020)	Developing digital twins at building and city levels with a focus on the operation and maintenance field	BIM Cloud Computing	Sensors data Land administration data Building information data Historical and time-series 3D data Energy consumption data	Making practical digital twins on a large scale Working on organizational productivity Focusing on social and economic results such as optimizing city services.
Zurich, Switzerland (Schrotter & Hurzeler, 2020)	Increasing public awareness through realizing 3D spatial data Developing and discussing scenarios through visualization and analysis	GIS	Sensors data Building information data Land administration data 3D data LIDAR images	Generating better inter-connected 3D data Updating of existing data Systematic recording of urban furniture Better integration of the BIM and GIS Detection and maintenance of the infrastructure Building a 3D cadastre database.
Helsinki, Finland (Hämäläinen, 2021)	The applicability of digital twins in urban planning and innovative developments.	BIM	Text Land administration data	Transforming digital twins perspectives through socio-technical aspects
Herrenberg, Germany (Dembski et al., 2020)	Aiding in participatory and collaborative processes in digital twins.	Big Data analytics	Sensors data Land administration data Historical and time-series 3D data Traffic and Transportation Climate and weather data Infrastructure data Images	Developing an integrated toolbox and method that focuses on advancing methods concerning socio-economic data.
Barcelona, Spain (Sun et al., 2020)	This paper has analyzed the operation of the urban water cycle under the cyber-physical framework.	Real-time analysis	Sensors data Infrastructure data	Deep research in detailed interconnecting digital twins Interfacing with real systems to provide real-time control
Remscheid, Germany/Flat Iron USA/Vienna, Austria (Visconti et al., 2021)	To keep the digital twins of the case study synchronized (both directions) with the actual city’s data and information.	CityGML Data-Driven and Model	3D data	Integrating Interfaces and toolchain to support the design cycle Generalizing the synchronizing to a many-to-one model
Haapsalu, Estonia/Söderhamn, Sweden (Truu et al., 2021)	Presenting an innovative concept in pluvial flood risks reduction Creating dynamic interlinkages between land developments, the urban drainage system, and other flood-related factors	GIS	Land administration data Climate and weather	Mitigating the flood risks faced buildings Analyzing the best design of the building envelope in floods.
Changsha, China (Huo et al., 2021)	Improving previous methods of large-scale oblique photogrammetry models visualization.	GIS	Land administration data Images	Linking synchronous loading method (based on the parent-child relationship) and data structure of quadtrees and octrees Visual content and video streaming integration
Dublin (Buckley et al., 2021)	Exploring the use of an urban building energy model to examine the potential for creating a near-zero carbon neighbourhood	UMI (urban modelling interface)	Land administration data Building information data Socio-demographic data Energy consumption data	Linking archetypes to their geography using building footprints
Bogota (Gutierrez-Franco et al., 2021)	Flexible decision making, achieving social welfare, increasing management productivity, and reducing environmental impacts and costs.	GIS Artificial intelligence	Socio-demographic data Historical and time-series Traffic and Transportation Energy consumption Infrastructure data	Incorporation of a circular economy in City Digital Twins framework.

Table 7. Classification of applied technologies in the 41 reviewed City Digital Twins literature.

Technology	References
GIS	Pan et al. (2020), Matthys et al. (2021), Conejos Fuertes et al. (2020), Gutierrez-Franco et al. (2021), Buckley et al. (2021), Lu et al. (2020), Huo et al. (2021), Simonsson et al. (2021), Park and Yang (2020a), Lenfers et al. (2021), Truu et al. (2021), Orozco-Messana et al. (2021), Major et al. (2021)
BIM	Pan et al. (2020), Lu et al. (2020), Major et al. (2021), Hämäläinen (2021)
UMI (urban modelling interface)	Buckley et al. (2021)
Cloud Computing	Lu et al. (2020)
Internet of Things	Park et al. (2019), Al-Ali et al. (2020), O’Dwyer et al. (2020), Broekman et al. (2021), Major et al. (2021)
Artificial Intelligence*	Matthys et al. (2021), Gutierrez-Franco et al. (2021), Park and Yang (2020a), Lenfers et al. (2021), Park et al. (2019), Al-Ali et al. (2020), Broekman et al. (2021), Pang et al. (2021), Laamarti et al. (2020), Han et al. (2020), O’Dwyer et al. (2020), Anda, Medina, et al. (2021)
Edge and Mini Edge Computing	Park et al. (2019), Al-Ali et al. (2020), Broekman et al. (2021)

*This category consists of AI and its related technologies that are used in selected research. Amongst which are Machine Learning, Federated Learning, and Markov Model.

Figure 6. Distribution of applied methods in 41 reviewed City Digital Twins studies at a glance.

Table 8. Digital twins’ maturity models and classifications that proposed in different sources.

Papers	Bojr et al. (2020)	Madni et al. (2019)	Evans et al. (2019)	Chen et al. (2021)	Shemyakina et al. (2022)
Proposed levels of digital twins Maturity	Generation #1 – monitoring platforms	1 Pre-Digital Twin	Element 0: Reality capture (for existing physical assets)	Unaware	Vertical
	Generation #2 – intelligent semantic platforms	2 Digital Twin	Element 1: 2D map/systems or 3D model (object-based only)	Identifiable	Horizontal
	Generation #3 – agent-driven socio-technical platforms	3 Adaptive Digital Twin	Element 2: Connected to persistent (static) data, metadata and BIM Stage 2	Aware	Connective
		4 Intelligent Digital Twin	Element 3: Enrich with real-time (dynamic) data	Communicative	Intellectual
			Element 4: Two-way integration and interaction	Interactive
			Element 5: Autonomous operations and maintenance	Instructive and Intelligent

Figure 7. Digital twins maturity levels and descriptions proposed by Evans et al. (2019).

Figure 8. Results of classification of 41 reviewed City Digital Twins studies based on their maturity elements proposed by Evans et al. (2019).

Figure 9. Results of the content analysis on the papers of City Digital Twins. The inner circle shows the main fields of studies in City Digital Twins literature. The next circle illustrates the corresponding sub-fields, and the outer circle reflects the corresponding applications in each subfield. Different colours show separate fields. More frequently explored research areas are reflected with more intense colours and larger sections.

Figure 10. Current state, challenges, and limitations of City Digital Twins.

Table 1S. A detailed content analysis of 41 reviewed studies on the subject of research.

Topic	country	methods	data	Theory/ practice /review			Case study	Theme		Application
				T	P	R		Main field	Sub field
“A digital twin uses classification system for urban planning & city infrastructure management” (Al-Sehrawy et al., 2021)	United Kingdom	Systematic literature review	Previous research			*	-	Built environment	Urban planning	-
“A Novel HDF-Based Data Compression and Integration Approach to Support BIM-GIS Practical Applications” (Pan et al., 2020)	China	Data reorganization Data transformation Data aggregation	CityGML, pictures, IFC, analytical data		*		Zhong Chun	IT and Technology	Data management	Interoperability, data fusion,
“A Socio-Technical Perspective on Urban Analytics: The Case of City-Scale Digital Twins” (Nochta et al., 2021)	United Kingdom	Workshops and interviews	Data from interviews	*			Cambridge	Built environment	Urban planning	Policy making, evaluation, citizen’s collaboration
“A systematic review of a digital twin city: A new pattern of urban governance toward smart cities” (Deng et al., 2021)	China	Systematic literature review	Previous research			*	-	Institutional	Urban governance	-
“AI-based physical and virtual platform with 5-layered architecture for sustainable smart energy city development” (Park et al., 2019)	South Korea	5-layer architecture	Land-use data, real-time data		*		Korea	Infrastructure	Electricity	Visual platform
“An animated spatial time machine in co-creation: Reconstructing history using gamification integrated into 3d city modelling, 4d web and transmedia storytelling” (Matthys et al., 2021)	Belgium	Workshops Co-Creation Analyzing the Lost Historical Architecture and City Environment Modelling Storytelling	Text (from workshops), documents, Open 3D Data		*		Ghent	IT and Technology	Gaming and animation	Visualization, interoperability
“Application Study in the Field of Solutions for the Monitoring Distribution Transformers of the Overhead Power Grid” (Chaves et al., 2021)	Brazil	Monitoring system for overhead transformers	Numeric data		*		Sao Paulo, Brazil	Energy management	Energy consumption and efficiency	Monitoring, real-time analysis, network dynamic
“An ISO/IEEE 11073 Standardized Digital Twin Framework for Health and Well-Being in Smart Cities” (Laamarti et al., 2020)	Canada	Data analytics on the cloud	Sensor data		*		-	Public health	Health and well-being	Monitoring and analysis
“Building and exploiting a Digital Twin for the management of drinking water distribution networks” (Conejos Fuertes et al., 2020)	Spain	Hydraulic model Machine Learning and A.I. algorithms Simulate hypothetical scenarios and optimization tools	Sensor data, geographical data		*		Valencia, Spain	Infrastructure	Water system	Operation, management, network dynamics
“Cybersecurity for digital twins in the built environment: Current research and future directions” (Alshammari et al., 2021)	United Kingdom	Systematic literature review	Previous research			*	-	I.T. and Technology	Cybersecurity	-
“Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution” (J. J. Pang et al., 2021)	China	Federated Learning	Spatial and real-time data		*		-	Public health	Crisis management	Data processing, citizen’s collaboration
“Circular digital built environment: An emerging framework” (Çetin et al., 2021)	Netherlands	Developing Framework Workshop with Expert Literature review Map digital technologies framework.	Previous research, interview		*		-	Business and economy	Economy	Participation of stakeholders, decision-making
“Data-driven methodology to support long-lasting logistics and decision making for urban last-mile operations” (Gutierrez-Franco et al., 2021)	United States	Forecasting techniques, clustering, data mining, probability distributions Quantitative Modeling Simulation and Experiments Deep reinforcement learning	Historical data, spatial data		*		Bogota	Infrastructure	Urban transportation system	Decision-making, integration, monitoring
“Detailed Streetspace Modelling for Multiple Applications: Discussions on the Proposed CityGML 3.0 Transportation Model” (Beil et al., 2020)	Germany	CityGML 3.0 Transportation Model	Open data, geospatial data		*		New York	Infrastructure	Urban transportation system	Software, data fusion, integration
“Designing an Energy-Resilient Neighbourhood Using an Urban Building Energy Model” (Buckley et al., 2021)	Ireland	Urban Building energy models (UBEMs) Urban Modelling Interface (UMI)	Archetypes, GIS database		*		Dublin	Energy management	Energy consumption and efficiency	Simulation, integration, data fusion
“Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus” (Q. Lu et al., 2020)	United Kingdom	In-depth analysis RVT model	BIM object, historical data, environment and energy data		*		West Cambridge Campus	Built environment	Urban planning	Monitoring, data fusion
“Digital Twin Conceptual Model within the Context of Internet of Things” (Al-Ali et al., 2020)	United Arab Emirates	End-to-end conceptual model	Sensor data, Historical data, real-time data		*		-	Infrastructure	Urban transportation system	Integration, analyzing, interoperability, designing a software
“Digital Twins From Smart Manufacturing to Smart Cities: A Survey” (Mylonas et al., 2021)	Greece	Systematic literature review	Previous research			*	-	Built environment	Urban planning	-
“Digital Twins: A Critical Discussion on Their Potential for Supporting Policy-Making and Planning in Urban Logistics” (Marcucci et al., 2020)	Italy		Previous research	*			-	Infrastructure	Urban transportation system	Decision-making, Behavior modeling
“Efficient visualization of large-scale oblique photogrammetry models in unreal engine” (Huo et al., 2021)	China	Data organization method	Geometric attributes, OSGB file		*		Changsha, China	Built environment	Photogrammetry and remote sensing	Visualization, data management, interoperability, real-time analysis
“Experiences from city-scale simulation of thermal grids” (Simonsson et al., 2021)	Sweden	Co-Simulation Physical and Data-Driven Models	Geographical data, piping and instrumentation, historical data		*		Sweden	Energy management	Energy consumption and efficiency	Simulation, monitoring, interoperability
“Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: Digital twins, immersive realities and new urban economies” (Allam & Jones, 2021)	France	Critique paper	-	*			-	Business and economy	Economy	Integration
“GIS-enabled digital twin system for sustainable evaluation of carbon emissions: A case study of Jeonju city, south Korea” (Park & Yang, 2020a)	South Korea	Mapping Machine learning algorithms Spatial statistics basis function (RBF) kernel algorithm Backpropagation neural network (BPNN) Hot spot analysis	3D building data, spatial data, energy, waste and vehicle data		*		Jeonju City, South Korea	Energy management	Energy consumption and efficiency	Integration, monitoring, interoperability
“Improving Model Predictions-Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model” (Lenfers et al., 2021)	Germany	Real-time data correction through agent-based methods and modeling Research and Simulation with Multi-Agent	Real-time data, sensors data, R.S. data		*		Hamburg, Germany	Infrastructure	Urban transportation system	Simulation and decision-making, real-time analysis
“Integrated decision support system for pluvial flood-resilient spatial planning in urban areas” (Truu et al., 2021)	Estonia	A dynamic combination of 1D hydraulic modelling Climate scenario making	Geographical data, cadastral, land cover and digital elevation maps, weather data, city plans		*		Haapsalu (Estonia) and Söderhamn (Sweden)	Environment	Natural disaster management	Data management, integration, designing a software
“Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems” (O’Dwyer et al., 2020)	United Kingdom	Integration of the sustainable energy management and system simulation (framework/model/simulation)	Temperature data, solar radiation, building simulation metrics, historical data		*		Greenwich, London	Energy management	Energy consumption and efficiency	Simulation, integration,
“Neighbourhood modelling for urban sustainability assessment” (Orozco-Messana et al., 2021)	Spain	LCA analysis (life cycle)	Historical data, cadastral information, orthophoto, ownership data, height, buildings characteristics		*		Benicalap in Valencia (Spain).	Environment	Natural disaster management	Policy evaluation, simulation
“Model-driven engineering city spaces via bidirectional model transformations” (Visconti et al., 2021)	Austria	Bidirectional transformations Support model-based development Synchronizing among city spatial models and graph-based analyzable models	CityGML data		*		Remscheid (Germany), Flat Iron (N.Y., USA), Vienna (Austria)	I.T. and Technology	Computer science	Interoperability
“Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area” (Bass et al., 2021)	United States	Automatic Building detection Energy Model creation (AutoBEM) Simulation	Satellite, LiDAR, tax-assessor data, energy consumption, land-use layer		*		Chattanooga, Tennessee	Energy management	Energy consumption and efficiency	Simulation, integration, decision-making
“Real-time control of urban water cycle under cyber-physical systems framework. Water” (Sun et al., 2020)	Spain	Simulating Virtual reality through MATLAB Supervising Management Framework of Multilayer CPS-based Optimizing algorithm of Integrated MPC-Based	Sensor data		*		Barcelona	Infrastructure	Water system	Virtual reality, integration of different fields, analyzing and monitoring, Network dynamics
“Real-time traffic quantization using a mini edge artificial intelligence platform” (Broekman et al., 2021)	South Africa	Quantitative proof of concept Real-time traffic analysis platform	Video data		*		Pretoria, South Africa	Infrastructure	Urban transportation system	Real-time analysis and platform, Interoperability
“Smart City 4.0 from the perspective of open innovation” (Yun & Lee, 2019)	South Korea	Systematic literature review	Previous research			*	-	Business and economy	Business	-
“Smart city for sustainable urban freight logistics” (Pan et al., 2021)	France	Systematic literature review Bibliometric analysis	Previous research			*	-	Infrastructure	Urban transportation system	-
“Smart cities in the time of climate change and Covid-19 need digital twins” (Dignan, 2020)	United Kingdom	-	-	*			-	Environment	Natural disaster management	Behavior modeling, simulation,
“Smart Cities with Digital Twin Systems for Disaster Management” (Ford & Wolf, 2020)	United States	-	ICT sensor		*		-	Environment	Natural disaster management	Integration, simulation, monitoring, decision making
“The Digital Twin of the City of Zurich for Urban Planning” (Schrotter & Hurzeler, 2020)	Switzerland	Report GIS	Official documents, Sensors, Building information, Land administration, 3D data, LIDAR images		*		Zurich	Built environment	Urban planning	Visualization, citizen engagement, policy evaluation, integration
“Smart-guided pedestrian emergency evacuation in slender-shape infrastructure with digital twin simulations” (Han et al., 2020)	China	CA-based model data-driven approaches through machine learning	Behavioral dynamics, sensor data		*		-	Population movements	Crisis management	Behavior modeling, simulation, monitoring
“Synthesizing digital twin travellers: Individual travel demand from aggregated mobile phone data” (Anda, Medina, et al., 2021)	Singapore	Markov models framework designing clustering strategy	Mobile phone data		*		-	Infrastructure	Urban transportation system	Data fusion, tracking, integration
“The Use of a Data-Driven Digital Twin of a Smart City: A Case Study of angstrom Alesund, Norway” (Major et al., 2021)	Norway	Framework of data-driven digital twin	GIS data, BIM, demographic data, and (IoT) data		*		Ålesund, Norway	I.T. and Technology	Data management	Citizen engagement, localized information systems, interoperability, visualization
“Urban development with dynamic digital twins in Helsinki city” (Hämäläinen, 2021)	Finland	A qualitative exploratory research strategy Semi-structured interviews content analysis	Interviews from smart city practitioners		*		Helsinki and Pietarsaari	Built environment	Urban planning	Integration, interoperability, and participation of stakeholders
“Urban Digital Twins for Smart Cities and Citizens: The Case Study of Herrenberg, Germany” (Dembski et al., 2020)	Netherlands	Mixed method approach 3D modeling Mathematical models of street network Urban mobility simulation Simulation of Wind flow Simulation patterns of people’s movement	Geographic data, geo-referenced space syntax data, emission data, traffic simulation, weather and climate, historic empirical, real-life data, sensor data, social data, images		*		Herrenberg, Germany	Built environment	Urban planning	Simulation, data fusion, Behavior modeling, integration, citizens’ engagement

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Data availability statement

Derived data supporting the findings of this study are available from the corresponding author [Sara Shirowzhan] on request.