Methodical-topical literature review methodology
Interdisciplinary and transdisciplinary approach
Hierarchical search strategy and scholarly sources
Searching databases of reviewed high quality literature;
Searching evidence based journals for review articles; and,
Routine searches and other search engines.
Selection criteria: inclusion and exclusion
Combining three organisational approaches
To examine and discuss the underlying foundational constructs and their integration and fusion from an interdisciplinary and transdisciplinary perspective, respectively.
To analyse, evaluate, and synthesise the existing knowledge in line with such constructs as set for this study.
To highlight the strengths, weaknesses, omissions, and contradictions of the existing knowledge, thereby providing a critique of the research that has been done within the field and related subfields.
To discuss the identified strengths and weaknesses, with an emphasis on the performance of smart and smarter cities with respect to sustainability and the untapped potential of big data applications for its advancement in the future.
To identify and discuss the knowledge gaps and opportunities within the field with regard to sustainability and related big data applications.
To identify the key relationships between the research findings by comparing various studies addressing the different topics of the study, with a particular focus on sustainability and related big data applications.
Conceptual, theoretical, and discursive foundations and assumptions
Different foci and orientations of smart city definitions
‘A smart city is…a city which invests in ICT enhanced governance and participatory processes to define appropriate public service and transportation investments that can ensure sustainable socio-economic development, enhanced quality-of-life, and intelligent management of natural resources’ 
‘A smart city is a very broad concept, which includes not only physical infrastructure but also human and social factor’ 
‘Connecting the physical infrastructure, the IT infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city… A city striving to make itself “smarter” (more efficient, sustainable, equitable, and livable’ 
‘Smart cities is a term…that describe cities that, on the one hand, are increasingly composed of and monitored by pervasive and ubiquitous computing and, on the other, whose economy and governance is being driven by innovation, creativity and entrepreneurship, enacted by smart people’ [79, p. 1]
A smart city is ‘a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies’ 
‘As presently understood, a smart city is one that strategically uses networked infrastructure and associated big data and data analytics to produce a: smart economy…; smart government…; smart mobility…; smart environments…; smart living…; and smart people…’ [80, p. 8]
Smarter cities and other faces of cities
Big data computing
Big data: concept and characteristics
Huge in volume, consisting of terabytes or petabytes of data;
High in velocity, being created in or near real-time;
Diverse in variety, being structured and unstructured in nature, and often temporally and spatially referenced;
Exhaustive in scope, striving to capture entire populations or systems (n = all), or at least much larger sample sizes than would be employed in traditional, small data studies;
Fine-grained in resolution, aiming to be as detailed as possible, and uniquely indexical in identification;
Relational in nature, containing common fields that enable the conjoining of different data sets;
Flexible, holding the traits of extensionality (can add new fields easily) and scaleability (can expand in size rapidly).
Big data analytics: concept and characteristics
Big data processing platforms
Advanced techniques based on data science fundamental concepts and computer science methods.
Data mining models.
Computational mechanisms involving such sophisticated and dedicated software applications and database management systems.
Advanced data mining tasks and algorithms.
Modeling and simulation approaches and prediction and optimization methods.
Data processing platforms.
Cloud and fog computing models.
Big data application
A detailed survey of relevant work: issues, debates, gaps, benefits, challenges, opportunities, and prospects
Smart and smarter cities
Research strands from a general perspective
Theory and practice
Conceptual and theoretical work
Advanced ICT impacts
Deficiencies and misunderstandings pertaining to sustainability
Potential risks of ICT to sustainability
Frameworks, models, and infrastructures
Research strands of particular relevance to the topic of the study
The inadequate contribution of smart cities of today to the goals of sustainable development and thus their poor sustainability performance
There is no general consensus about whether there needs to be any substance behind the claim of smartness for, or how it is linked to, sustainability.
Smart technologies are less focused on providing solutions for the challenges and pressing issues related to sustainability and more focused on optimising the efficiency of solutions.
There is a discrepancy between smart solutions and sustainability problems.
There particularly is a weak connection between smart solutions and environmental problems.
There is a mismatch between smart targets and sustainability goals.
There are gaps between theory and practice and visions and their realisation with regard to the sustainability dimension.
Current ICT investments and technological innovation orientations fall short in considering or embracing the goals of sustainable development.
The field is unable to proceed in anything like a cumulative fashion and to contribute systematically and constructively to the development of innovative technologies for sustainability.
Smart technologies mostly provide pre-configured/pre-formatted solutions for yet-to-find urban problems, rather than the needed solutions for tackling the challenge of sustainability.
ICT research, development, and application are directed mainly towards economic development.
There are divergences in terms of the current and future use of big data applications, as well as in terms of related innovation.
The existing assessment performance frameworks lack environmental indicators and tend to overemphasise economic aspects.
ICT poses great risks to and negative implications for environmental and social sustainability.
Smart cities focus mostly on ICT advancement and the efficiency of solutions and fall short in considering, if not ignoring, design concepts and principles and planning practices of urban sustainability and their effects and benefits.
Smart cities continue to strive for smart targets rather than integrating them with sustainability goals.
Sustainability goals and smartness targets are misunderstood as to their interconnection.
The two landscapes of the smart city and sustainable city are extremely fragmented on the technical and policy levels.
Smart cities need to leverage their informational landscape together with their physical landscape in line with the vision of sustainability.
Smart technologies are still being developed for building and enabling smart cities without any orientation towards, or any consideration of, improving the contribution to the goals of sustainable development.
The existing smart city performance assessment frameworks need to be redeveloped in ways that incorporate the design concepts and principles and planning practices of sustainability as well as environmental indicators.
Realising the tremendous potential of smart cities of the future for advancing sustainability
Smarter cities: characteristic features, social shaping aspects, and current issues of and future potentials for sustainability
Big data analytics and its application in smart and smarter cities
Research status and data growth projection
Research issues and future prospects
Urban data deluge
Urban data potentials and sources
Core enabling technologies
Strands and permutations
A survey of related work
Pervasive sensing for urban sustainability in terms of collecting and measuring urban big data; the IoT and related RFID tags; sensor-based urban reality mining; and sensor technologies, types, and areas in big data computing.
Wireless communication network technologies and smart network infrastructures.
Data processing platforms.
Cloud and fog/edge computing in terms of characteristics, benefits, commonalities, and differences.
Advanced techniques and algorithms.
Privacy mechanisms and security measures.
Conceptual and analytical frameworks with a focus on the process of data mining.
Big data applications and their sustainability effects and benefits
A critical evaluation of topical studies
The key practical and analytical applications of big data technology for multiple urban domains
Transport and traffic.
Academic and scientific research.
Towards data-driven sustainable smart cities
The main scientific and intellectual challenges and common open issues
Computational, analytical, technical, and logistic challenges
Design science and engineering constraints
Data processing and analysis
Data management in dynamic and volatile environments
Data sources and characteristics
Database integration across urban domains
Data sharing between city stakeholders
Data uncertainty and incompleteness
Data accuracy and veracity (quality)
Data protection and technical integration
Fault tolerance and scalability
Urban growth and data growth
Cost and large-scale deployment
Evolving urban intelligence functions and related simulation models and optimization and prediction methods as part of exploring the notion of smart cities as innovation labs
Building and maintaining data-driven city operations centres or citywide instrumented system
Relating the urban infrastructure to its operational functioning and planning through control, automation, management, optimization, enhancement, and prediction
Creating technologies that ensure fairness, equity, inclusion, and participation
Balancing the efficiency of solutions and the quality of life against environmental and equity considerations
Privacy and security
Consisting of exabytes or terabytes of data;
Being structured and unstructured in nature;
Being often tagged with spatial and temporal labels; being commonly streamed from a large number and variety of sources;
Being mostly generated automatically and routinely; being created in, or near, real-time;
Being exhaustive in scope and scale by striving to capture entire populations or systems;
Dramatically exceeding sample sizes commonly in use for small data studies;
Being relational in database systems by containing common fields that enable the conjoining and combination of different datasets;
Being fine-grained in resolution by aiming to be very detailed and uniquely indexical in identification; and holding the traits of extensionality (can add new fields easily), evolvability (can change dynamically), and scaleability (can expand in size rapidly).
Identity privacy (to protect personal and confidential data);
Bodily privacy (to protect the integrity of the physical person);
Territorial privacy (to protect personal space, objects and property);
Locational and movement privacy (to protect against the tracking of spatial behaviour);
Communications privacy (to protect against the surveillance of conversations and correspondence); and,
Transactions privacy (to protect against monitoring of queries/searches, purchases, and other exchanges).
Watching, listening to, or recording of an individual’s activities
Various forms of questioning or probing for information
The combination of various pieces of data about a person
Linking information to particular individuals
Carelessness in protecting stored information from leaks and improper access
Use of information collected for one purpose for a different purpose without the data subject’s consent
Failure to allow the data subject to know about the data that others have about her and participate in its handling and use, including being barred from being able to access and correct errors in that data
Breach of confidentiality
Breaking a promise to keep a person’s information confidential
Revelation of information about a person that impacts the way others judge her character
Revealing another’s nudity, grief, or bodily functions
Amplifying the accessibility of information
Threat to disclose personal information
The use of the data subject’s identity to serve the aims and interests of another
Dissemination of false or misleading information about individuals
Invasive acts that disturb one’s tranquillity or solitude
Incursion into the data subject’s decisions regarding her private affairs
Data ownership, data control, data coverage and access.
Data security and data integrity.
Data protection and privacy, dataveillance, and data uses such as social sorting and anticipatory governance.
Technical data issues such as data quality, veracity of data models and data analytics, and data integration and interoperability.