Next Article in Journal
Building a Sustainable Society: Construction, Public Procurement Policy and ‘Best Practice’ in the European Union
Next Article in Special Issue
An Empirical Approach to Differences in Flexible Electricity Consumption Behaviour of Urban and Rural Populations—Lessons Learned in Germany
Previous Article in Journal
The Functional Controller for Sustainable and Value Chain Management: Fashion or Need? A Sample of Job Advertisements in the COVID-19 Period
Previous Article in Special Issue
Scenario Analysis for Incremental Community Planning in an African Context
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends

Graduate School of Humanities and Social Sciences & Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, Hiroshima 739-8511, Japan
Chaire Entrepreneuriat Territoire Innovation (ETI), Groupe de Recherche en Gestion des Organisations (GREGOR), IAE Paris—Sorbonne Business School, Université Paris 1 Panthéon-Sorbonne, 75013 Paris, France
Department of Remote Sensing and GIS, University of Tabriz, Tabriz 51368, Iran
Interior Design Program, Department of Family and Consumer Sciences, College of Health and Human Development, California State University Northridge, Northridge, CA 91330, USA
Authors to whom correspondence should be addressed.
Sustainability 2021, 13(13), 7140;
Received: 19 May 2021 / Revised: 20 June 2021 / Accepted: 23 June 2021 / Published: 25 June 2021


The concept of smart cities has gained significant momentum in science and policy circles over the past decade. This study aims to provide an overview of the structure and trends in the literature on smart cities. Bibliometric analysis and science mapping techniques using VOSviewer and CiteSpace are used to identify the thematic focus of over 5000 articles indexed in the Web of Science since 1991. In addition to providing insights into the thematic evolution of the field, the three-decade study period is divided into two sub-periods (1991–2015 and 2016–2021). While splitting the dataset into more sub-periods would have been desirable, we decided to only examine two sub-periods as only very few papers have been published until 2010. The annual number of publications has progressively increased since then, with a surge in the annual number of publications observable from 2015 onwards. The thematic analysis showed that the intellectual base of the field has been very limited during the first period, but has expanded significantly since 2015. Over time, some thematic evolutions, such as further attention to linkages to climate change and resilience, and more emphasis on security and privacy issues, have been made. The thematic analysis shows that existing research on smart cities is dominated by either conceptual issues or underlying technical aspects. It is, therefore, essential to do more research on the implementation of smart cities and actual and/or potential contributions of smart cities to solving societal issues. In addition to elaborating on thematic focus, the study also highlights major authors, journals, references, countries, and institutions that have contributed to the development of the smart cities literature.

1. Introduction

There are indications that the concept of smart cities emerged as early as 1974, when the city of Los Angeles attempted to create the world’s first urban big data project [1]. However, it is after 20 years (1994) that a major milestone in the smart sities pursuit occurred in Amsterdam, when a virtual ‘digital city’ was created with the purpose of promoting internet usage among local populations [2]. Since then, there have been extensive research and attempts to create smart city digital infrastructures, with large ICT corporations, such as Cisco and IBM, taking the lead [3,4], especially in research and development. For instance, in 2008, IBM launched the ‘IBM Smarter Planet’, with an aim to investigate and test how applying sensors, networks, and analytics to different urban fabrics can render more performance and, as a result, identify business opportunities. The success of this program led to the corporation unveiling the USD 50 million smart cities campaign in 2009, aimed at helping cities reach efficiency and performance, while overcoming some contextualized urban challenges [5]. After the widespread application of technology in diverse urban spheres, this concept, in 2011, gained ground in acceptability with different cities in developed economies, with the city of Barcelona leading the way [6]. While the smart cities program today (2021) is still gaining a rapid adoption rate, there is no universally agreed definition as to what constitutes a smart city [4,7,8]. However, one agreed commonality is the ability to assist planners and municipal authorities in taking more effective actions towards achieving higher efficiencies and performance in different frontiers, with the expected outcome being improved liveability status, increased inclusivity, and fostering of sustainability agendas, amongst others [9,10]. It is, therefore, obvious that smartness is a multi-dimensional concept and a combination of both hard infrastructure (i.e., technology-based) and soft infrastructure (i.e., regulations, knowledge economy, citizen participation, social and institutional innovation, data management, etc.) is essential for building smart cities.
The increasing acknowledgement of the multi-dimensional nature of the smart city concept has led to a paradigm shift from a technology-based approach to more holistic approaches that recognize the central role of social, economic, and institutional forces. Indeed, the impacts of the smart city concept in different urban dimensions have been mainly viewed positively, especially in prompting transformative changes in urban infrastructures such as institutional, physical, social, and economic infrastructures. The groundbreaking outcomes of the smart city concept have led to the emergence of other concepts such as the ‘Safe City’ concept [11], and the latest one being the ‘15-Minute City’ concept [12], which are seen to utilize the foundation of smart city concept in varying ways. The emergence of these technology-based planning concepts has in turn led to a boom in software and hardware products that aim to tap into an emerging digitally inspired market estimated to be between USD 83.9 billion [13] and USD 410.8 billion [14], according to different market research firms. However, due to increasing demand for different digital products, the market is expected to expand to reach over $297.7 billion [13] and USD 820.7 billion by 2026 [14]. Other market research firms, such as Mordor Intelligence, value the smart cities market even higher at USD 739.78 billion as of 2020 and expect it to experience a Compound Annual Growth Rate (CAGR) of approximately 18.22%, to reach a market value of approximately USD 2.0361 trillion by 2026 [15]. This showcases that, even though there are discrepancies on the expected value of smart cities, there are commonalities on their substantial worth and shared trust on their growth potential.
As with the smart cities market increase, the market for technology products, especially relating to the Internet of Things (IoT) based devices has also been seen to increase. It is reported to have reached approximately USD 250.7 billion by 2019, but due to market developments, the market size is projected to expand to above USD 1.46319 trillion by 2027; a 24.9% CARG during the projected period [16]. While these market figures are encouraging, especially from an economic point of view, they also raise alarms, especially on the observable and perceived trends where the urban areas are morphing into cold mechanical elements, thus undermining humanistic and liveability aspects in cities [9]. Such urban characteristics have been argued to being driven by profit-oriented agendas by different companies: both small and big, eyeing the expanding market. The mechanical nature of how cities are turning has not gone unnoticed, as already, there is an increasing pool of literature that is focusing on the need to have cities include ‘soft’ approaches as a core dimension of the urban planning concept. Such calls even emanate from UN-Habitat, that, despite supporting new urban planning models, advocates for ‘People-Centered Smart Cities’ [17], which would ultimately lead to a win-win situation where both companies and corporations are able to benefit, just as well as urban dwellers- from improvements in different urban fabrics.
Such benefits are already being enjoyed by residents and companies that have invested in cities such as Barcelona [6], London [18], Amsterdam [19], Songdo [20], Hangzhou [21], and numerous others in varying geographies, which have already embraced and implemented some aspects of smart cities concept. Coupled with the development of new technologies supporting the concept, it is expected that more smart cities will continue to emerge in the coming years. In the academic sphere, both existing and emerging smart cities are spurring a surge in literature touching on the global discussion on smart cities across a range of themes including governance [22,23], liveability [24,25], safety [26,27,28], economic performance [29], mobility [30,31], health [32], culture [4], education [33], communication infrastructures [34], energy [35], and others.
Along with the increasing interest in smart city development, the number of academic articles published on smart cities has also grown rapidly over the past two decades. In the past few years, several review articles have been published that have improved our understanding of the state of development of the smart cities field and have highlighted key successes and challenges. According to these studies, while the smart city concept is hailed for its transformative prospects on the urban planning sphere, there are notable issues that must be streamlined. Camero and Alba [36] note that one such issue is the lack of a universally agreed definition and the scope of the same. They argue that the lack of unanimity in the definition has led to research on the concept being built in a wide array of silos depending on the understanding of those conducting the research. This argument is affirmed by Cocchia [37] who highlights that a number of terminologies such as the Intelligent City and Digital City have been used to depict the technological foundations of urban concepts, without properly linking them to the broader smart city concept. The point by Camero and Alba [36], on creation of application silos, is seen to align with the proposition by Ruhlandt [38], supporting that research on smart cities is hampered by lack of understanding of various components such as smart governance, technological application, and the metrics adopted as yardsticks for those components. Without any explicit understanding of the components and metrics, it becomes problematic in determining the expected outcomes, and, as is expressed by Pereira et al. [39], this leads to researchers concentrating on specific components rather than generally focusing on the entirety of the smart city concept. To overcome these challenges, several efforts have been made to provide metrics for assessment of different smart city aspects and dimensions [40,41]. On their end, Talari et al. [42] note that concentrating on specific aspects of the smart city concept would have far reaching implications in respect to philosophical approaches and perspectives about the concept’s implementation, especially in different regions and in different sectors. Focusing on specifics in the smart city concept would also be impacted by time as already technological advancements are emerging fast and in diverse sectors. This, then, has a bearing on the research in the present paper, especially in relation to increasing publications, covering a diverse set smart city dimensions, raising the challenge and need for regular reviews.
While traditional reviews are essential for detailed understanding of research fields, they are not always suitable to keep up with the rapid pace of scientific publishing, especially in popular fields such as sustainability or smart city development. This issue can be partially solved by using science mapping and bibliometric analysis techniques that allow the collection of overall understanding of knowledge structure and trends using advanced text-mining methods [43]. In this way, bibliometric analysis studies can complement traditional systematic reviews. There are several generic bibliometric studies on smart cities research [44,45,46,47,48,49,50,51]. These studies have improved our understanding of the overall landscape of the field, and interestingly point out similarities between some concepts, particularly regarding the ‘intelligent city’, and the ‘smart city’; where the former focusses on systems design, without necessarily engaging in heavy utilization of technology. Smart cities, on the other hand, approach systemic design by supporting a technological foundation. In addition, smart cities follow a more comprehensive approach that, in addition to technological focus, acknowledges the significance of people, economy, and institutions. There are also several bibliometric studies focused on specific issues such as governance of smart cities [52], smart city applications in the building and construction sectors [53], relationship between smart cities and migration [54], and smart city indicators [55].
Despite contributions of the systematic review and bibliometric analyses mentioned earlier, and while this field has been booming, there is a lack of literature examining the thematic evolution of the smart cities research over the past three decades. In addition, as will be discussed in Section 3.1, a large number of articles has been published over the past two years that warrants an updated analysis. Indeed, considering the rapid pace of scientific publishing on smart cities, regular scientific mapping and bibliometric analyses are necessary to keep up with the recent developments, identify emergent areas, and highlight gaps. Accordingly, the main objective of this study is to provide an updated understanding of the knowledge structure of smart cities research published over the past three decades. Other objectives are to identify major thematic areas and discuss their transition over time; to identify influential authors, sources, institutions, and references that have made relatively more contributions to the development of the field; and to highlight understudied themes that warrant further research. Overall, this bibliometric analysis builds on those mentioned earlier by providing period-based thematic analysis of the evolution of the field, and also by taking into account the large number of papers published recently. As this is a fast-growing area of research, results of this study can be used as a point of reference for researchers new to the field to gain a rather quick understanding of the intellectual base of smart cities research, its evolution, and gaps and emergent topics. This will allow them to develop future research ideas in a more effective way. Further, interested readers and those new to the field can refer to the influential sources and references highlighted in this study to gain further knowledge on specific topics.
Materials and methods for bibliometric analysis are presented in Section 2. Results are presented and discussed in Section 3. These include information on publication trends, thematic focus areas and their transition over time, and influential authors, references, sources, countries, and institutions. Finally, a summary of the findings and some recommendations for future research are provided in the final section.

2. Materials and Methods

Data for bibliometric analysis was obtained from the Web of Science (WoS). Several other databases, such as Scopus, exist that index and archive academic publication. We selected WoS for three main reasons: first, its reputation for indexing quality peer reviewed research; second, it provides detailed bibliometric information which allows researchers to obtain more accurate results using the bibliometric analysis software tools (i.e., VOSviewer and CiteSpace); third, a large number of publications on smart cities exist in the WoS, and this is enough for meeting our objective of understanding the overall structure and tends (in other words, even if some studies are missing, it will not affect the overall structure and trends). In order to include as many relevant papers as possible in the analysis, we used a broad-based search string that includes two keywords: smart city and smart cities. The specific search string was: TS = (“smart city” OR “smart cities”) AND LANGUAGE:(English) Indexes = SCI-EXPANDED, SSCI, A&HCI, ESCI Timespan = 1900–2021. In other words, we searched for articles written in the English language that have mentioned ‘smart city’ and/or ‘smart cities’ in either the title, abstract, or keywords. The literature search was conducted on 24 April 2021, in all citation indexes of the WoS (i.e., SCI-EXPANDED, SSCI, A&HCI, and ESCI). All types of publications archived in the WoS until the search date were included in the analysis. The search returned 7228 documents, and after excluding studies that were out of scope (i.e., focused on fields such as physics, material sciences, and mathematics), 5722 articles were selected for analysis. The ‘Full Record and Cited References’ of these studies were downloaded from the WoS, in formats compatible with VOSviewer and CiteSpace.
Several software tools exist for science mapping and bibliometric analysis [56]. While these tools sometimes adopt different analysis and illustration approaches, they all contribute to understanding thematic focus and trends by providing details on the complex interactions between different components of academic publications (i.e., keywords, references, authors, journals, etc.). Further details about unique features of different tools can be found in Cobo, López-Herrera, Herrera-Viedma, and Herrera [56]. In this study, VOSviewer and CiteSpace were selected considering their abilities to meet the study objectives. Both tools are freely available Java applications (VOSviewer at: (accessed on 10 May 2021); and CiteSpace at: (accessed on 10 May 2021)). The software developers also provide free access to user manuals and demo projects. Interested readers are referred to the tool manuals for detailed step-by-step description of the analyses. We used VOSviewer for conducting different analyses, namely, term co-occurrence analysis (using the ‘full counting’ counting method and setting ‘all keywords’ as the unit of analysis), citation (setting ‘documents’ as the unit of analysis), co-citation (using the ‘full counting’ counting method, and setting ‘cited references’, ‘cited sources’, and ‘cited authors’, as units of analysis), and bibliographic coupling (using the ‘full counting’ counting method, and setting ‘organizations’ and ‘countries’ as units of analysis) [57]. Term co-occurrence analysis presents frequently occurred terms and the way they are connected to one another. This can be used to highlight major thematic areas. As a term may have different variants, before conducting the analysis, a thesaurus file was developed and added to the VOSviewer database to avoid separate counting of synonyms (e.g., Internet of Things and IoT). As can be seen in Section 3.2, results of the term co-occurrence analysis (and also other analyses done by VOSviewer) are presented as a network graph of nodes and links. The node size is proportional to the occurrence frequency, and the link width is proportional to the strength of connections between two nodes. Terms that co-occur more frequently form clusters that show different thematic areas.
To understand thematic transition over time, we divided the study period into two sub-periods (1991–2015 and 2016–2021). The beginning year was set as 1991 as this is the publication date of the first paper indexed in the WoS. Additionally, 2015 was selected as a milestone considering that different reports and policy documents that may have influenced smart city research were published in that year (e.g., Agenda 2030 and the New Urban Agenda). As the process of publishing academic papers sometimes takes more than one year, we have assumed that potential influences of such policy documents on academic research have been reflected in academic publication starting from 2016. It was also possible to introduce other sub-periods. However, as shown in Section 3.1, relatively fewer studies have been published before 2015, not warranting further sub-periods. To understand thematic focus during each period, separate analyses were conducted. Co-citation analysis was conducted to identify influential authors, journals, and references. Co-citation indicates the link between two documents that are both cited simultaneously by a third document [57]. In other words, it considers not only the documents in the database, but also their cited references. In contrast, citation analysis was conducted to understand highly cited papers in the database.
To understand which countries and institutions have contributed more to this field of research, we used the bibliographic coupling analysis. “A bibliographic coupling link is a link between two items that both cite the same document” [57]. Finally, the citation burst function of CiteSpace was used to better understand which subjects have received more attention at specific times during the study period. This can complement the findings of the term co-occurrence analysis and allow a better understanding of thematic transition and intellectual turning points over time [58].

3. Results

3.1. Publications Trends

Figure 1 displays the distribution of the 5722 publications over the study period (1991–2021). The results show that there is an overall growth in the number of publications per year. It is clear that this is still a young field, as most papers have been published over the past 10 years or so. In fact, only 17 papers have been published between 1991–2010, accounting for less than 1% of the documents in the database. In the following 5 years, 429 papers have been published, indicating that ‘smart cities’ has become a mainstream research topic since 2010. From 2015, however, a rapid growth pattern can be observed. In particular, the growth pattern has been exponential since 2018. Interestingly, the number of articles published over the past three years is greater than the cumulative number of articles published between 1991 and 2018. As explained in the Introduction, this is a clear indication of the increasing recognition of the significance of smart cities for dealing with multiple challenges that cities around the world are facing and have been highlighted in major policy documents such as the New Urban Agenda and the Sustainable Development Goals (SDGs). Based on this, an upward trend in publications on smart cities is expected for the coming years.

3.2. Thematic Focus Areas and Their Transition

3.2.1. Thematic Clusters

The output of the term co-occurrence analysis for the entire dataset is shown in Figure 2. Three major clusters can be identified from this figure. These are: (1) the smart city concept, depicted by the color red, (2) big data analytics, represented by the color blue, and (3) the technological aspects, especially in relation to Internet of Things, depicted by the color green. The link’s thickness between nodes indicates strength of connection, while the size of the node is directly proportional to the term frequency. Therefore, as is clearly depicted in the diagram, the most dominant clusters were the smart city and the IoT. The other cluster that is mainly focused on the application of big data analytics and other smart solutions (e.g., machine learning, deep learning, etc.) in the energy sector has received relatively less attention.

The Smart City Concept Cluster

This co-occurrence analysis, shown in Figure 2, showcases that most of the literature in respect to the smart city is centered around the concept itself, with much attention given to the broader urban context [32,33,36,38,59] and how those have been influenced by technology application and adoption (red cluster). The literature on this concept also centered on how cities have been striving to achieve sustainability [25], more so after the high level global meetings that culminated in diverse accords and agreements such as the Paris Agreement, SDGs, the New Urban Agenda, and others. The dominance of the term ‘sustainability’ in this cluster is not surprising considering that a lot of research on smart and sustainable cities has been published in the past few years, demonstrating how smart solutions can contribute to solving issues related to various social, economic, and environmental dimensions of sustainability (e.g., see [60,61]). The analysis also shows that, in the smart cities literature, the term ‘cities’ has been frequently used in conjunction with other terms such as ‘innovation’ [62,63,64], which relates closely with the application of technology, and in the pursuit of sustainability [8,12]. Other terms that were researched in conjunction with cities include ‘policy’, showcasing a drive from researchers in understanding how policy frameworks impact on issues such as urban planning, infrastructure development, initialization of innovative programs, and others. Other terms that occurred in this cluster include ‘mobility’ and ‘transport’, showing that the transport sector is crucial in the whole agenda of making cities smarter. In fact, along with the energy sector, transportation has been one of the major sectors in which applications of smart city technologies have been studied [65]. Further, terms such as ‘e-government’ and ‘citizen participation’ have also appeared, especially in relation to ‘innovation’ and ‘information’, showing that researchers are keen to understand governance dimensions of smart cities, though not many recognize and appreciate the intricate roles and the dynamics of governments and citizens in the actualization of smart city agendas [66,67,68,69,70,71]. There are also some other terms, such as ‘indicators’ and ‘Geographic Information System’ (GIS) that, comparatively, have occurred less frequently. In the recent years there has been an increasing focus on developing and implementing smart city assessment tools and indicator sets. Among other things, such tools and indicators contribute to better informed decision making regarding smart cities and evaluate their contributions to other societal goals, such as sustainability and resilience [72]. Similarly, it is increasingly recognized that GIS technologies are essential for effective development and implementation of smart cities and, generally, for better-informed urban planning [73]. For instance, platforms enabled by real-time GIS enable acquiring, storing, processing, and visualizing large amounts of geospatial data in an efficient manner [74]. Such platforms can facilitate enhanced modeling of urban operations, enable better informed and more timely decision making, and improve the efficiency and safety of various sectors such as urban transportation [74].

The Internet of Things Cluster

This cluster (depicted in Figure 2 in green) showcases a drive by researchers to understand the influence of technology at varying levels of the smart city concept. From Figure 2, it is clear that that most of the research has been on the topic of ‘IoT’ [75,76,77], with most of the articles being centered on the influence of the ‘internet’ on the concept. The research on ‘IoT’ is interestingly seen to touch on different aspects of a city, such as financials [78,79], smart devices [80], the security of such devices [81,82,83], and many others. As far as the internet is concerned, it is clear that this term had two major thematic focus areas. One focuses on ‘security’ aspects and how they impact on the adoption of smart devices in cities. Associated research is seen to be closely related to terms such as ‘authentication’ and ‘surveillance’. It is apparent that there are notable ‘security’ concerns in regard to IoT, with themes linking to the issue of ‘privacy’ surging [69,71], generating substantial attention in terms of number of publication and citations.
The second thematic focus area that attracted some publication traffic, as shown in Figure 2, is ‘challenges’, in the application of internet aspects in smart cities. This is particularly seen to have been researched in conjunction with internet security and privacy, indicating that it is a main concern, and part of the obstacles that researchers perceive to have the potential to derail the successful implementation of smart city concepts. The term ‘infrastructure’ also appears in this cluster, depicting that researchers were interested in understanding the intricate matters of how the internet infrastructure was rendered to facilitate application of IoT technology [84,85,86]. Another term that is seen to have received considerable attention is ‘blockchain’, especially in regard to security [87]. The presence of this term in this cluster is timely, as this technology is seen as the future of security [88,89], especially in regard to issues such as contracts [90,91], computing [88,92], and in enhancing privacy [88,93]. Other terms that appear in this cluster but seem to have received, relatively, less attention include ‘sensors’, ‘fog-computing’, ‘authentication’, and ‘edge computing’, among others.

The Big Data Analytics

This cluster (indicated by blue in Figure 2) is seen to be less dominant in respect to the smart city concept as per this co-occurrence analysis. The term ‘big data analytics’, however, has co-occurred frequently with other terms and has a central position in the figure. This is not surprising considering the significance of big data analytics for smart city operations [94]. This cluster seems to be specifically focused on the applications of smart city products in the energy sector. In conjunction with the term ‘big data analytics’, are terms such as ‘networks’, ‘models’, and ‘design’ that appear to have been frequently researched in respect to this topic. These terms are also seen to appear in the edges of both the smart city cluster and the IoT cluster, highlighting their nature as overarching terms [95,96]. Among the terms that appear to have been researched in respect to big data analytics also include ‘smart grid’, ‘optimization’, ‘efficiency’, and ‘renewable energy’. These are not surprising, as the current, and probably future debate and trends in the energy sector are expected to be on renewable energies [97,98], and how those can be enhanced through the use of technology to increase efficiency. In respect to ‘optimization’, terms such as ‘machine learning’, ‘deep learning’, ‘data mining’, and ‘artificial intelligence’ are seen to have received some search attention, but in a relatively limited way. While these are at the edges of the three clusters, the, relatively, limited research and literature on them is surprising, as they are among some of the enablers of IoT technologies [99,100], showcasing a gap in the literature.

3.2.2. Thematic Focus Transition over Time

To explore the thematic focus transition, we divided the dataset into two sub-periods and conducted separate term co-occurrence analyses for each period.

First Period (1991–2015)

Although the concept of ‘smart cities’ can be noted as early as the 1980s, it did not gain considerable attention until after the fourth revolution (early 2000). This is evident in the co-occurrence analysis map (Figure 3) for the period between 1991 and 2015, where the frequency of many terms relevant to the smart city concept is seen to be low. Additionally, the list of keywords with citation bursts (Figure 4) shows that during this period, topics related to precedent concepts such as ‘intelligent city’ have still been dominant. This indicates that, though there have been some interests by researchers, the level of understanding of smart cities was still in its infancy stage. During this period, the main focus and publications were on general application of smart technologies in cities, with most of the literature touching on issues such as policies, transport, innovation, performance, model, growth, institutions, and knowledge. Other terms appearing in respect to cities include climate change, and Information and Communications Technology (ICT). This is understandable, especially in relation to climate change, as the importance of urban actions for tacking climate change was widely recognized in this period, following the publication of the 4th and 5th assessment reports of the Intergovernmental Panel on Climate Change (IPCC), in 2007 and 2014, respectively [101,102]. The importance of cities was further recognized with the ratification of the Paris Agreement [103] and the birth of the Sustainable Development Goals (SDGs) towards the end of this period [104]. In conjunction with climate change, it is evident that research attention and publications were drawn to the issue of ICT and innovation as some of the possible dimensions where solutions, especially in regard to sustainability, could be drawn; hence, publications on these topics started to gain some prominence.
In respect to technology as an enabler of smart cities, it is evident that attention was consolidated on issues such as security, innovation, and policies. It is evident that within the period 1991–2015, the concept of smart city started to attract attention in many intellectual quarters, though the internet employment in different urban fabric was still in infancy. Therefore, considerations to issues of security, especially in regard to data and surveillance, is noted to be prevalent in the civic realm. In addition, it is evident from the analysis that most technologies were being directly focused on the energy sector, though the number of publications in regard to the same are still limited, and key words such as ‘smart grid’ and ‘renewable energy’ were not attracting much attention from academia. Another area of noted interest during this period is the Internet of Things. This topic was very relevant during this period, as most of the emerging technologies that could help actualize the smart city concept were hinged on it. For this reason, most of the publications on IoT are seen to have focused on other sub-cluster terms such as ‘big data analytics’, ‘internet’, ‘management’, and ‘design’. During this period, considering that smart devices need to communicate with each other, terms such as ‘information’, ‘architecture’, and ‘sensor network’ were seen to have started gaining some consideration amongst researchers, but in limited scale. For instance, there were very few publications and interest on issues such as ‘cloud computing’, ‘sensors’, and ‘wireless sensors networks’, and this could be attributed to the fact that those technologies had not gained substantial traction by then.

Second Period (2016–2021)

This period, as depicted in Figure 5, has witnessed an explosion of publications on all the three main clusters. As explained earlier, publication of international policy frameworks and their emphasis on smart solutions may have contributed to this increased attention to smart cities research. The IoT cluster has seemingly gained more attention in this period. Attention in this cluster is seen to have focused especially on the sub-cluster ‘internet’, which is seen to have attracted further research in new terms such as ‘challenges, ‘protocol’, and ‘cloud’. These terminologies may have emerged with the realization that IoT enabled devices need to communicate in a standardized protocol to aid in the seamless collection of data, and further, relaying back the insights after analysis of data to relevant parties for better urban management [95]. During this period, it is observable that the term ‘energy’ is still a key term that is strongly connected to the term ‘optimization’. This may indicate the increasing use of smart solutions for enhancing operational efficiency of energy systems. Attention to energy is not surprising, given that energy sector plays an essential role in addressing key challenges highlighted in policy documents such as SDGs and the New Urban Agenda. In addition, several other key terms such as ‘machine learning’ and ‘deep learning’ have appeared in this cluster and gained even a stronger position than ‘energy’. This may indicate that, though energy-related issues are dominant in the global agenda, they have been overtaken by other research areas in respect to IoT technologies, leading to the understanding that research interests and global conversations moved towards how to utilize the concept across an array of other fields, hence leading to the need for the development and deployment of the data and technology infrastructure.
In respect to data management, it is clear that during the second period (Figure 5), there has been increased attention on areas such as big data [35,105,106] and computation, especially in relation to climate change [107,108] and others become apparent. Interestingly, there is a perceived focus on addressing to climate change [8,10,107,108,109]. The significance of ‘big data’ in the second period is also evident from the citation burst analysis (Figure S1 of the Supplementary Materials). The importance of data in the management of the smart city may also have triggered the explosion of research and publications in this cluster, with new terminologies such as ‘artificial intelligence’, ‘optimization’, ‘simulation’, ‘efficiency’, ‘prediction’, ‘deep learning’, ‘machine learning’, and ‘performance’ gaining traction. The emergence of these new terms shows how much attention has been given to data management, and the advancement in technology regarding the analysis of data that is continuously being produced in, and by, smart cities. Data management and big data analytics are expected to gain further momentum in the post-COVID era. In fact, there are arguments that the recent pandemic has increased interest in big data analytics and smart city development [110].
In respect to the smart city cluster, this second period is observed to have experienced numerous publications, not only touching on cities, but also on the relationship between cities, data, and IoT. However, in respect to cities, and with the growing number of smart cities [111,112,113], it is evident that researchers were and continue to be interested in areas such as sustainability, governance, technology, policies, and the impacts of the same. Those areas of interest are some of the new emerging frontiers that the literature has gained during this timeline. However, some topics, such as institutions and their influence in achieving ‘smartness’, are seen to continue attracting more attention, even in the second period. Further, other areas that seem to have remained pertinent since the emergence of the smart city concept, as captured in Figure 4 and Figure 5 above, include the transport sector where, in the second period (Figure 4), researchers are seen to have concentrated more on mobility, especially due to emergence of technology-enabled services such as ridesourcing [114], smart cars [31,114,115], and new concepts such as the 15-min concept [12] which emphasizes the reducing of vehicular use by giving precedence to cycling and walking due to reduced proximity between different urban essentials.

3.3. Influential Sources

The co-citation analysis was used to find out which journals have had the highest impact in the development of the field. Again, the size of the nodes is proportional to the number of citations, and link width is proportional to the strength of connection between two nodes. Quantitative details related to the citation count and total link strengths of the top 20 journals can be found in Table S1 of the Supplementary Materials. Results show that journals such as IEEE Access, Cities, IEEE Internet of Things, Lecture Notes in Computer Science, IEEE Communications Magazine, Future Generation Computer Systems, and Journal of Urban Technology have had higher influence. Three major clusters can be identified from the results of the co-citation sources analysis, as shown in Figure 6. The largest cluster (green) includes journals that are mainly focused on Internet of Things (IoT) and other technical issues (e.g., related to internet, cloud computing, architecture of smart cities, and wireless networks, etc.). This relates to the green cluster (The Internet of Things cluster) in Figure 2. As expected, electrical engineering journals and in particular those published by Institute of Electrical and Electronics Engineers (IEEE) have played a significant role in advancing the underlying technical issues of smart cities. The most prominent journals of this cluster are IEEE Access, IEEE Internet of Things Journal, Lecture Notes in Computer Science, IEEE Communications Magazine, Future Generation Computer Systems, and Sensors.
The second largest cluster (red in Figure 6) is primarily composed of urban planning and policy journals. This cluster relates to the red cluster (the smart cities topic cluster) in the thematic cluster analysis (Figure 2). Results show that urban planning and policy issues have mainly been addressed by journals such as Cities, Journal of Urban Technology, Sustainable Cities and Society, Sustainability, and Urban Studies.
The third cluster (depicted in blue in Figure 6) is dominated by journals focused on energy-related issues. This corresponds to the blue cluster in Figure 2 that is focused on topics such as ‘energy’, smart grid’, ‘optimization’, ‘efficiency’, and ‘renewable energies’. Once again, this shows the specific attention of the smart cities literature to energy-related applications. The most influential journals of this cluster include Journal of Cleaner Production, IEEE Transactions on Smart Grid, Renewable & Sustainable Energy Reviews, and Energy and Buildings.

3.4. Major Contributing Countries and Institutions

In order to identify the most prominent countries that have contributed the most to the knowledge in the field, a bibliographic coupling analysis was conducted. The results are shown in Figure 7. The list of the top 20 most prominent countries with the number of documents, number of citations, and total link strength is presented in Table S2 of the Supplementary Materials.
It is noted that countries such as China, USA, Italy, England, India, Spain, Australia, South Korea, and Canada have published more on this topic. These countries also rank high in terms of the total number of citations. Examples of highly cited papers from the top 10 countries are shown in Table 1. Interestingly, while developed countries have contributed more, several developing countries have also been highlighted in the figure, showcasing that there is a global adoption of the concept, irrespective of GDP and development status.
However, there are many countries, particularly from Africa and Asia that are missing. The clusters shown in Figure 7 indicate that there is a close collaboration among countries that are geographically proximate. For instance, the red cluster primarily includes European countries (England, France, Italy, Spain, Germany, and Greece), while the green cluster includes a broader range of countries from Northern America (USA, Canada) to Asia (China, South Korea, India, Japan, Saudi Arabia, and Pakistan).
The bibliographic coupling analysis was also used to find out which countries are in the forefront of the field. Figure 8 shows that universities from Italy, the US, China, and Saudi Arabia have contributed more to the development of the discourses in the field. Those are universities such as Polytechnic University of Milan, University of Naples Federico II, King Saud University, MIT, Chinese Academy of Sciences, and Huazhong University of Science and Technology. The list of the most prominent organizations with the number of documents, number of citations, and total link strength is presented in Table S3 of the Supplementary Materials.

3.5. Influential Documents

The co-citation analysis was also used to identify the most prominent publications in this field. The output of co-citation analysis by cited references is shown in Figure 9. The top 20 most cited references are shown in Table S4 of the Supplementary Materials. It should be noted that this list is not exhaustive, and other influential documents also exist that have not been included in order to ensure readability of the figure (e.g., [126]). This analysis shows that there are three clusters of the most prominent publications (red, green, and blue). The red cluster includes studies primarily focusing on the main core concepts, definitions, and trends of smart cities (e.g., [122,123,127,128,129,130,131]). This cluster associates with the red cluster (the smart city concept cluster), identified in Thematic analysis in Section 3.2. The majority of the influential documents in this cluster include studies that are primary resources of the smart cities literature in the first period (1991–2015). Obviously, given their focus on fundamental smart city concepts, they have played important roles in guiding smart city research. The second largest cluster depicted by blue color, is mainly comprised of studies focused on the implementation and realization of smart cities. These cover various issues such as success of smart city programs in achieving their (utopian) goals [132,133,134], utilities of big data analytics for improving socio-economic and governance mechanisms in cities and enhancing operational efficiency [135,136], weaknesses that need to be addressed [133], corporate smart cities [132,134], smart mentality [136], and roles of different stakeholders and actors in the management and realization of smart cities [136]. This cluster is in close relationship with the blue cluster in Figure 2 that is focused on the applications of smart cities. The third largest cluster (green) primarily addresses issues related to the underlying technological foundations of smart cities. These include issues related to the IoT, cloud computing, internet algorithms, wireless networks, etc. [117,137,138]. This cluster is closely associated with the Internet of Things cluster identified in the thematic cluster analysis in Figure 2.
The results of citation burst analysis also confirm those of the co-citation analysis and show that works such as [123,127,133,139] have been very influential in advancing smart city discourse in the literature (Figure S2 of the Supplementary Materials).

3.6. Influential Authors

To identify the most influential authors it is also possible to set ‘cited authors’ as the unit of analysis in the co-citation analysis. Details related to the top 20 most prominent authors (in terms of citation) is shown in Table S5 of the Supplementary Materials. As shown in Figure 10, three major clusters of influential authors can be identified that are, to a great extent, consistent with the results of the analysis in the previous sections (prominent publications and thematic cluster analysis). The red cluster includes authors that have mainly worked on the core concepts and definition of smart cities (such as Tan Yigitcanlar, Andrea Caragliu, Nicos Komninos, Taewoo Nam, Margarita Angelidou, Vito Albino, and Michael Batty, etc.). In other words, the authors of this cluster have primarily contributed to topics included in the smart city concept cluster of Figure 2.
The second important cluster (blue) includes the authors that have expertise in big data and have worked on issues related to the implementation and the future of smart cities (e.g., Rob Kitchin, Anthony Townsend, Robert Hollands, and Alberto Vanolo). This cluster also associates with the big data analytics and smart city applications cluster identified in thematic analyses (blue cluster in Figure 2) and blue cluster identified in previous section (influential documents in Figure 9).
The third cluster (green) includes authors whose expertise revolve around the Internet of Things and the underlying technical issues related to smart cities. Key authors in this cluster are Andrea Zanella and Luigi Atzori. This cluster is also in close connections with the green cluster (Internet of Things) identified in Figure 2 and the green cluster in the most influential documents (Figure 9).

4. Discussion

The concept of smart cities has gained significant momentum in science and policy circles over the past decade or so. In fact, many cities around the world have developed and/or are planning to develop smart city programs. In doing so, they hope to make great strides towards improving the quality of life of their citizens, enhance the efficiency and effectiveness of urban operations, and develop solutions to overcome societal challenges such as climate change. Along with the growing interest in smart city agendas, the scientific literature on smart cities has also been expanding rapidly over the past few years. The main objectives of this study were to provide an overall understanding of the knowledge structure and trends in the smart cities literature, and to find out which sources, references, countries, institutions, and authors have made significant contribution to the development of the field. For this purpose, we relied on bibliometric analysis and science mapping tools (VOSviewer and CiteSpace) that allow analyzing performance and visualizing knowledge domain.
Our results show that, although some research has been published on smart cities since the early 1990s, the field experienced a slow pace of growth until 2010. However, there has been a sudden increase in interest since 2010, and the number of publications has progressively increased ever since. This could be attributed to the extensive investment campaigns of companies such as IBM in the late 2000s [5], and the major advances in information and communication technologies and their widespread penetration of urban communities [6]. While between 2010 and 2015 the field has grown considerably, it was not until 2015 that the growth rate become exponential. Generally, 2015 is considered a milestone year for research related to cities, given the emphasis on the role of cities in major international policy documents released in or around 2015, such as the 2030 agenda, the New Urban Agenda, the Sendai Framework for Disaster Risk Reduction, and the Paris Climate Agreement.
To better understand the thematic focus of the field over the past three decades, we first conducted a term co-occurrence analysis for the whole study period. This showed that existing literature can be divided into three major clusters focused on smart city concepts, IoT, and big data analytics. Obviously, as this is still a relatively new field of research, the literature is dominated by underlying conceptual and technical issues related to the planning and development of smart cities. In contrast, research on implementation and applications of smart cities has been relatively limited. Regarding smart city concepts, existing research has mainly focused on issues such as linkages between smart and sustainable cities, and use of smart solutions to transform urban governance. Linkages between smart cities, and other important topics such as climate change and resilience, seem to have received relatively less attention. As for the cluster focused on underlying technical issues, there has been a relatively balanced focus on various topics such as IoT, the architecture of internet networks, security/privacy issues, and cloud/fog/edge computing. The cluster on implementation is mainly focused on applications of big data analytics and machine learning techniques for enhancing efficiency and optimizing urban operations. It is clear that these have mainly been applied to the energy and transportation sectors, and further research in other sectors is needed.
Dividing the study period into two sub-periods (1991–2015 and 2016–2021) provided more insights into the thematic structure of the field and how it has evolved over time. It was found that until 2015 the knowledge base was limited. However, the three major cluster that were discussed earlier could still be identified. As expected, during these initial years, concepts and fundamental technical issues were dominant. During the second period, the intellectual base has grown significantly but the focus has remained on the three major clusters identified for the first period. In terms of concepts and planning-related issues, more attention to linkages between smart cities and sustainability/climate change can be observed in the second period. The term ‘climate change’, however, is still not a dominant term. Attention to sustainability and climate change is expected to further continue in the coming years as there are increasing hopes that smart cities will increase the capacity to overcome societal challenges. Additionally, more attention to issues related to decision making and governance in the second period indicates the increasing recognition of the role that smart solutions can play in transforming urban governance. As for the cluster on technical issues, a clear transition is the attention to issues related to security and privacy. As discussed earlier, these have been, particularly, discussed in relation to blockchain, indicating the increasing recognition of its importance for addressing security-related concerns that may further increase in the coming years. Regarding the cluster on applications, the noteworthy transitions include increased attention to data management and big data analytics, and further attention to utilities of smart technologies for enhancing operational optimization. As for the sectoral focus, energy and transportation are the two sectors that have been emphasized in both periods. Based on this, we argue that more research in other sectors is also needed as smart city initiatives continue to be rolled out around the world in the coming years. In fact, the COVID-19 pandemic has given even more momentum to smart city initiatives around the world, and many cities have relied on smart solutions to combat the pandemic [110]. An updated bibliometric analysis in the next 1–2 years is needed to understand the influence of the recent pandemic on the structure of the field.
Overall, what can be learned from the thematic analysis is that existing research on smart cities is dominated by either conceptual issues or underlying technical aspects. It is, therefore, essential to do more research on the implementation of smart cities and actual and/or potential contributions of smart cities to solving societal issues. Terms related to energy and transportation were highlighted in the term co-occurrence analysis, indicating that issues related to the implementation of smart solutions in these sectors have received some attention. However, smart solutions can also be applied to other urban sectors. Another potential gap is limited focus on people and governance as two major dimensions of smart city development [72]. Terms related to these dimensions (e.g., citizen participation and e-government) had a marginal position in the term maps and, therefore, warrant further research. This, however, does not mean that other dimensions do not deserve further research. For instance, while climate change has gained traction over time, it still does not have a central position in the term maps. Considering the significance of addressing climate change, more research is needed to better understand actual and/or potential contributions of smart cities to achieving climate change adaptation and/or mitigation targets.
In addition to analyzing the thematic structure, this paper also identified key journals, authors, references, institutions, and countries that have made relatively more contributions to the development of the field. This information can be used by interested readers, especially those new to the field, to better understand the structure of the smart cities literature and make more informed decisions regarding their future research planning and design. Overall, this study has provided a better understanding of the overall structure of the smart cities literature. However, given the rapid pace of publications, regular updates are needed to keep up with the tremendous knowledge explosion on smart cities. Additionally, more specific analyses on each of the three clusters identified in this study is recommended, as it may provide more detailed information on how they have evolved over years. Finally, it is essential to mention that the approach taken in this study is not without limitations. The main limitation is that the existing software tools for bibliometric analysis can only process data related to publications indexed in scientific databases such as the WoS and Scopus. Accordingly, they cannot be used to analyze potentially influential documents not indexed in such databases (i.e., grey literature). Indeed, it is important to reiterate that bibliometric analyses cannot replace systematic reviews that can be used to analyze both grey and peer-reviewed literature. Conducting more systematic reviews is also necessary to gain more granular information regarding, for example, geographic focus of the research or the actual/potential contributions of smart cities to addressing societal challenges such as climate change.

Supplementary Materials

The following are available online at, Figure S1: Top 25 keywords with the strongest citation bursts in the second period. Figure S2: Top 25 references with the strongest citation bursts. Table S1: Top 20 most influential journals. Table S2: Countries making more contribution to the smart cities literature. Table S3: Organizations that have made more contribution to the development of the field. Table S4: Most influential documents. Table S5: The most influential authors.

Author Contributions

Conceptualization, A.S., and H.G.; methodology, A.S., and H.G.; software, A.S.; formal analysis, A.S.; data curation, A.S.; writing—original draft preparation, A.S., H.G., Z.A., and B.F.; writing—review and editing, A.S., H.G., Z.A., and B.F.; visualization, A.S., and H.G.; supervision, A.S. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Data Availability Statement

The datasets generated during and analyzed in the current study are not publicly available due to further, ongoing research projects but are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Los Angeles Community Analysis Bureau. The State of the City Report: A Cluster Analysis of Los Angeles: A Report; Community Analysis Bureau: Los Angeles, CA, USA, 1974. [Google Scholar]
  2. Alberts, G.; Went, M.; Jansma, R. Archaeology of the Amsterdam digital city; why digital data are dynamic and should be treated accordingly. Internet Hist. 2017, 1, 146–159. [Google Scholar] [CrossRef][Green Version]
  3. Swabey, P. IBM, Cisco and the Business of Smart Cities. Available online: (accessed on 21 July 2020).
  4. Allam, Z.; Newman, P. Redefining the Smart City: Culture, Metabolism and Governance. Smart Cities 2018, 1, 4–25. [Google Scholar] [CrossRef][Green Version]
  5. IBM IBM Pledges $50 Million To Create 100 Smarter Cities. Available online: (accessed on 25 April 2021).
  6. Gascó-Hernandez, M. Building a Smart City: Lessons from Barcelona. Commun. ACM. 2018, 61, 50–57. [Google Scholar] [CrossRef]
  7. Allam, M.Z. Redefining the Smart City: Culture, Metabolism and Governance. Case Study of Port Louis, Mauritius; Curtin University: Perth, Australia, 2018. [Google Scholar]
  8. Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities 2019, 89, 80–91. [Google Scholar] [CrossRef]
  9. Allam, Z. Cities and the Digital Revolution: Aligning Technology and Humanity; Springer International Publishing: Berlin/Heidelberg, Germany, 2020. [Google Scholar]
  10. Allam, Z. Data as the New Driving Gears of Urbanization. In Cities and the Digital Revolution: Aligning Technology and Humanity; Allam, Z., Ed.; Springer International Publishing: Cham, Switzerland, 2020; pp. 1–29. [Google Scholar]
  11. Allam, Z. The Emergence of Anti-Privacy and Control at the Nexus between the Concepts of Safe City and Smart City. Smart Cities 2019, 2, 96–105. [Google Scholar] [CrossRef][Green Version]
  12. Moreno, C.; Allam, Z.; Chabaud, D.; Gall, C.; Pratlong, F. Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities. Smart Cities 2021, 4, 93–111. [Google Scholar] [CrossRef]
  13. Facts & Factors Development Growth in Global Smart Cities Market Size & Share Will Reach to USD 297.7 Billion By 2026: Facts & Factors. Available online:,%25%20from%202019%20to%202026%E2%80%9D (accessed on 25 April 2021).
  14. Markets and Markets Smart Cities Market Report—Global Forecast to 2025. Available online: (accessed on 4 January 2021).
  15. Mordor Intelligence Smart Cities Market—Growth, Trends, and Forecast (2020–2025). Available online: (accessed on 4 January 2021).
  16. Liu, S. Global IoT Market Size 2017–2025. Available online: (accessed on 13 December 2019).
  17. UNited Nations Human Settlements Programme. People-Centered Smart Cities; UN-Habitat: Nairobi, Kenya, 2021. [Google Scholar]
  18. Zvolska, L.; Lehner, M.; Voytenko Palgan, Y.; Mont, O.; Plepys, A. Urban sharing in smart cities: The cases of Berlin and London. Local Environ. 2019, 24, 628–645. [Google Scholar] [CrossRef][Green Version]
  19. Mancebo, F. Smart city strategies: Time to involve people. Comparing Amsterdam, Barcelona and Paris. J. Urban. Int. Res. Placemaking Urban Sustain. 2020, 13, 133–152. [Google Scholar] [CrossRef]
  20. Kolotouchkina, O.; Seisdedos, G. Place branding strategies in the context of new smart cities: Songdo IBD, Masdar and Skolkovo. Place Branding Public Dipl. 2017, 14, 115–124. [Google Scholar] [CrossRef]
  21. Argyriou, I. 9—The smart city of Hangzhou, China: The case of Dream Town Internet village. In Smart City Emergence; Anthopoulos, L., Ed.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 195–218. [Google Scholar]
  22. Meijer, A.; Rodríguez Bolívar, M.P. Governing the smart city: A review of the literature on smart urban governance. Int. Rev. Adm. Sci. 2015, 82, 392–408. [Google Scholar] [CrossRef]
  23. Tan, S.Y.; Taeihagh, A. Smart City Governance in Developing Countries: A Systematic Literature Review. Sustainability 2020, 12, 899. [Google Scholar] [CrossRef][Green Version]
  24. Orlowski, A.; Romanowska, P. Smart Cities Concept: Smart Mobility Indicator. Cybern. Syst. 2019, 50, 118–131. [Google Scholar] [CrossRef]
  25. Valcárcel-Aguiar, B.; Murias, P.; Rodríguez-González, D. Sustainable Urban Liveability: A Practical Proposal Based on a Composite Indicator. Sustainability 2019, 11, 86. [Google Scholar] [CrossRef][Green Version]
  26. Toh, C.K. Security for smart cities. IET Smart Cities 2020, 2, 95–104. [Google Scholar] [CrossRef]
  27. Lacinák, M.; Ristvej, J. Smart City, Safety and Security. Procedia Eng. 2017, 192, 522–527. [Google Scholar] [CrossRef]
  28. Guerrini, F. Cities Cannot be Reduced to Just Big Data and IoT: Smart City Lessons from Yinchuan, China. Available online: (accessed on 1 June 2021).
  29. Zhao, Z.; Zhang, Y. Impact of Smart City Planning and Construction on Economic and Social Benefits Based on Big Data Analysis. Complexity 2020, 2020, 8879132. [Google Scholar] [CrossRef]
  30. Bonnefon, J.F.; Shariff, A.; Rahwan, I. The social dilemma of autonomous vehicles. Science 2016, 352, 1573–1576. [Google Scholar] [CrossRef] [PubMed][Green Version]
  31. Rafael, S.; Correia, L.P.; Lopes, D.; Bandeira, J.; Coelho, M.C.; Andrade, M.; Borrego, C.; Miranda, A.I. Autonomous vehicles opportunities for cities air quality. Sci. Total Environ. 2020, 712, 136546. [Google Scholar] [CrossRef] [PubMed]
  32. Rocha, N.P.; Dias, A.; Santinha, G.; Rodrigues, M.; Queirós, A.; Rodrigues, C. Smart Cities and Public Health: A Systematic Review. Procedia Comput. Sci. 2019, 164, 516–523. [Google Scholar] [CrossRef]
  33. Zhuang, R.; Fang, H.; Zhang, Y.; Lu, A.; Huang, R. Smart learning environments for a smart city: From the perspective of lifelong and lifewide learning. Smart Learn. Environ. 2017, 4, 6. [Google Scholar] [CrossRef]
  34. Allam, Z.; Jones, D.S. Future (post-COVID) digital, smart and sustainable cities in the wake of 6G: Digital twins, immersive realities and new urban economies. Land Use Policy 2021, 101, 105201. [Google Scholar] [CrossRef]
  35. Zhou, K.; Fu, C.; Yang, S. Big data driven smart energy management: From big data to big insights. Renew. Sustain. Energy Rev. 2016, 56, 215–225. [Google Scholar] [CrossRef]
  36. Camero, A.; Alba, E. Smart City and information technology: A review. Cities 2019, 93, 84–94. [Google Scholar] [CrossRef]
  37. Cocchia, A. Smart and Digital City: A Systematic Literature Review. In Smart City. Progress in IS; Dameri, R., Rosenthal-Sabroux, C., Eds.; Springer: Cham, Switzerland, 2014. [Google Scholar]
  38. Ruhlandt, R.W.S. The governance of smart cities: A systematic literature review. Cities 2018, 81, 1–23. [Google Scholar] [CrossRef]
  39. Pereira, G.; Parycek, P.; Falco, E.; Kleinhans, R. Smart governance in the context of smart cities: A literature review. Inf. Polity. 2018, 23, 143–162. [Google Scholar] [CrossRef][Green Version]
  40. Giffinger, R.; Pichler-Milanović, N. Smart cities: Ranking of European Medium-Sized Cities; Centre of Regional Science, Vienna University of Technology: Vienna, Austria, 2007. [Google Scholar]
  41. Sharifi, A. A typology of smart city assessment tools and indicator sets. Sustain. Cities Soc. 2020, 53, 101936. [Google Scholar] [CrossRef]
  42. Talari, S.; Shafie-khah, M.; Siano, P.; Loia, V.; Tommasetti, A.; Catalão, J.P.S. A Review of Smart Cities Based on the Internet of Things Concept. Energies 2017, 10, 421. [Google Scholar] [CrossRef][Green Version]
  43. Sharifi, A. Urban sustainability assessment: An overview and bibliometric analysis. Ecol. Indic. 2021, 121, 107102. [Google Scholar] [CrossRef]
  44. Mora, L.; Bolici, R.; Deakin, M. The First Two Decades of Smart-City Research: A Bibliometric Analysis. J. Urban Technol. 2017, 24, 3–27. [Google Scholar] [CrossRef]
  45. Guo, Y.-M.; Huang, Z.-L.; Guo, J.; Li, H.; Guo, X.-R.; Nkeli, M.J. Bibliometric Analysis on Smart Cities Research. Sustainability 2019, 11, 3606. [Google Scholar] [CrossRef][Green Version]
  46. Zheng, C.; Yuan, J.; Zhu, L.; Zhang, Y.; Shao, Q. From digital to sustainable: A scientometric review of smart city literature between 1990 and 2019. J. Clean. Prod. 2020, 258, 120689. [Google Scholar] [CrossRef]
  47. Pérez, L.M.; Oltra-Badenes, R.; Oltra Gutiérrez, J.V.; Gil-Gómez, H. A Bibliometric Diagnosis and Analysis about Smart Cities. Sustainability 2020, 12, 6357. [Google Scholar] [CrossRef]
  48. Moradi, S. The scientometrics of literature on smart cities. Libr. Hi Tech 2020, 38, 385–398. [Google Scholar] [CrossRef]
  49. Zhao, L.; Tang, Z.-Y.; Zou, X. Mapping the Knowledge Domain of Smart-City Research: A Bibliometric and Scientometric Analysis. Sustainability 2019, 11, 6648. [Google Scholar] [CrossRef][Green Version]
  50. Wamba, S.F.; Queiroz, M.M. A Bibliometric Analysis and Research Agenda on Smart Cities; Springer International Publishing: Cham, Switzerland, 2019; pp. 325–335. [Google Scholar]
  51. Janik, A.; Ryszko, A.; Szafraniec, M. Scientific Landscape of Smart and Sustainable Cities Literature: A Bibliometric Analysis. Sustainability 2020, 12, 779. [Google Scholar] [CrossRef][Green Version]
  52. Zhou, S.; Zhang, X.; Liu, J.; Zhang, K.; Zhao, Y. Exploring development of smart city research through perspectives of governance and information systems: A scientometric analysis using CiteSpace. J. Sci. Technol. Policy Manag. 2020, 11, 431–454. [Google Scholar] [CrossRef]
  53. Su, L.-x.; Lyu, P.-H.; Yang, Z.; Ding, S.; Zhou, K.-L. Scientometric cognitive and evaluation on smart city related construction and building journals data. Scientometrics 2015, 105, 449–470. [Google Scholar] [CrossRef]
  54. Mouazen, A.M.; Hernández-Lara, A.B. The role of sustainability in the relationship between migration and smart cities: A bibliometric review. Digit. Policy Regul. Gov. 2021, 23, 77–94. [Google Scholar]
  55. Bernardes, M.B.; de Andrade, F.P.; Novais, P. Indicators for Smart Cities: Bibliometric and Systemic Search; Springer International Publishing: Cham, Switzerland, 2017; pp. 98–105. [Google Scholar]
  56. Cobo, M.J.; López-Herrera, A.G.; Herrera-Viedma, E.; Herrera, F. Science mapping software tools: Review, analysis, and cooperative study among tools. J. Am. Soc. Inf. Sci. Technol. 2011, 62, 1382–1402. [Google Scholar] [CrossRef]
  57. van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed][Green Version]
  58. Chen, C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA. 2004, 101 (Suppl. 1), 5303–5310. [Google Scholar] [CrossRef][Green Version]
  59. Marrone, M.; Hammerle, M. Smart Cities: A Review and Analysis of Stakeholders’ Literature. Bus. Inf. Syst. Eng. 2018, 60, 197–213. [Google Scholar] [CrossRef]
  60. Bibri, S.E.; Krogstie, J. Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustain. Cities Soc. 2017, 31, 183–212. [Google Scholar] [CrossRef]
  61. Kramers, A.; Höjer, M.; Lövehagen, N.; Wangel, J. Smart sustainable cities—Exploring ICT solutions for reduced energy use in cities. Env. Model. Softw. 2014, 56, 52–62. [Google Scholar] [CrossRef]
  62. Sinha, A.; Sengupta, T.; Alvarado, R. Interplay between technological innovation and environmental quality: Formulating the SDG policies for next 11 economies. J. Clean. Prod. 2020, 242, 118549. [Google Scholar] [CrossRef]
  63. Yigitcanlar, T.; Bulu, M. Urban Knowledge and Innovation Spaces. J. Urban Technol. 2016, 23, 1–9. [Google Scholar] [CrossRef]
  64. Han, S.; Yoo, G.M.; Kwak, S. A Comparative Analysis of Regional Innovation Characteristics Using an Innovation Actor Framework. Sci. Technol. Soc. 2018, 23, 137–162. [Google Scholar] [CrossRef][Green Version]
  65. Gohar, M.; Muzammal, M.; Ur Rahman, A. SMART TSS: Defining transportation system behavior using big data analytics in smart cities. Sustain. Cities Soc. 2018, 41, 114–119. [Google Scholar] [CrossRef]
  66. Woetzel, J.; Kuznetsova, E. Smart City Solutions: What Drives Citizen Adoption Around the Globe? Mckinsey Center for Government: Singapore, 2018; pp. 1–60. [Google Scholar]
  67. Samad, M.E.S.; Hawken, S.; Sargolzaei, S.; Foroozanfa, M. Implementing citizen centric technology in developing smart cities: A model for predicting the acceptance of urban technologies. Technol. Forecast. Soc. Chang. 2019, 142, 105–116. [Google Scholar]
  68. Calzada, I. (Smart) Citizens from Data Providers to Decision-Makers? The Case Study of Barcelona. Sustainability. 2018, 10, 3252. [Google Scholar] [CrossRef][Green Version]
  69. Martinez-Balleste, A.; Perez-Martinez, P.; Solanas, A. The Pursuit of Citizens’ Privacy: A privacy-Aware Smart City is Possible. IEEE Commun. Manag. 2013, 51, 136–141. [Google Scholar] [CrossRef]
  70. Eden Strategy Institute, ONG&ONG Pte. Smart City Governments; Eden Strategy Institute and ONG&ONG Pte Ltd: Singapore, 2018; p. 106. [Google Scholar]
  71. Zoonen, L.V. Privacy concerns in smart cities. Gov. Inf. Q. 2016, 33, 472–480. [Google Scholar] [CrossRef][Green Version]
  72. Sharifi, A. A critical review of selected smart city assessment tools and indicator sets. J. Clean. Prod. 2019, 233, 1269–1283. [Google Scholar] [CrossRef]
  73. Feizizadeh, B.; Ronagh, Z.; Pourmoradian, S.; Gheshlaghi, H.A.; Lakes, T.; Blaschke, T. An efficient GIS-based approach for sustainability assessment of urban drinking water consumption patterns: A study in Tabriz city, Iran. Sustain. Cities Soc. 2021, 64, 102584. [Google Scholar] [CrossRef]
  74. Li, W.; Batty, M.; Goodchild, M.F. Real-time GIS for smart cities. Int. J. Geogr. Inf. Sci. 2020, 34, 311–324. [Google Scholar] [CrossRef]
  75. Tompson, T. Understanding the Contextual Development of Smart City Initiatives: A Pragmatist Methodology. She Ji J. Des. Econ. Innov. 2017, 3, 210–228. [Google Scholar] [CrossRef]
  76. Rishi, R.; Saluja, R. Future of IoT; EY: Bengaluru, India, 2019; pp. 1–32. [Google Scholar]
  77. Lueth, K.L.; Pasqua, E. IoT Solution World Congress 2019 Report; IoT Analytics: Hamburg, Germany, 2019. [Google Scholar]
  78. Vishwakarma, S.K.; Upadhyaya, P.; Kumari, B.; Mishra, A.K. Smart Energy Efficient Home Automation System Using IoT. In Proceedings of the 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU), Ghaziabad, India, 18–19 April 2019; pp. 1–4. [Google Scholar]
  79. Khanboubi, F.; Boulmakoul, A.; Tabaa, M. Impact of digital trends using IoT on banking processes. Procedia Comput. Sci. 2019, 151, 77–84. [Google Scholar] [CrossRef]
  80. Berntzen, L.; Johannessen, M.R.; Florea, A. Smart Cities: Challenges and a Sensor-based Solution. Int. J. Adv. Intell. Syst. 2016, 9, 579–588. [Google Scholar]
  81. Maple, C. Security and privacy in the internet of things. J. Cyber Policy. 2017, 2, 155–184. [Google Scholar] [CrossRef]
  82. Edwards, L. Privacy, security and data protection in smart cities: A critical EU law perspective. Eur. Data Prot. Law Rev. 2016, 2, 28–58. [Google Scholar] [CrossRef]
  83. Mosenia, A.; Jha, N.K. A comprehensive study of security of Internet of Things. IEEE Trans. Emerg. Top. Comput. 2017, 5, 586–602. [Google Scholar] [CrossRef]
  84. Minoli, D.; Sohraby, K.; Occhiogrosso, B. IoT Considerations, Requirements, and Architectures for Smart Buildings—Energy Optimization and Next-Generation Building Management Systems. IEEE Internet Things J. 2017, 4, 269–283. [Google Scholar] [CrossRef]
  85. Barns, S.; Cosgrave, E.; Acuto, M.; Mcneill, D. Digital Infrastructures and Urban Governance. Urban Policy Res. 2016, 20–31. [Google Scholar] [CrossRef]
  86. Alvalez, R. The Relevance of Informational Infrastructures in Future Cities. Available online: (accessed on 1 June 2021).
  87. Hammi, M.T.; Hammi, B.; Bellot, P.; Serhrouchni, A. Bubbles of Trust: A decentralized blockchain based authentication system for IoT. Comput. Secur. 2018, 78, 126–142. [Google Scholar] [CrossRef]
  88. Naz, M.; Al-zahrani, F.A.; Khalid, R.; Javaid, N.; Qamar, A.M.; Afzal, M.K.; Shafiq, M. A Secure Data Sharing Platform Using Blockchain and Interplanetary File System. Sustainability 2019, 11, 7054. [Google Scholar] [CrossRef][Green Version]
  89. Elisa, N.; Yang, L.; Chao, F.; Cao, Y. A framework of blockchain-based secure and privacy-preserving E-government system. Wirel. Netw. 2018. [Google Scholar] [CrossRef][Green Version]
  90. Marsal-Llacuna, M.-L. Future living framework: Is blockchain the next enabling network? Technol. Forecast. Soc. Chang. 2018, 128, 226–234. [Google Scholar] [CrossRef]
  91. Christidis, K.; Devetsikiotis, M. Blockchains and Smart Contracts for the Internet of Things. IEEE Access 2016, 4, 2292–2303. [Google Scholar] [CrossRef]
  92. Yavuz, E.; Koç, A.K.; Çabuk, U.C.; Dalkılıç, G. Towards secure e-voting using ethereum blockchain. In Proceedings of the 2018 6th International Symposium on Digital Forensic and Security (ISDFS), Antalya, Turkey, 22–25 March 2018; pp. 1–7. [Google Scholar]
  93. Zyskind, G.; Nathan, O.; Pentland, A. Decentralizing Privacy: Using Blockchain to Protect Personal Data. In Proceedings of the Conference: 2015 IEEE Security and Privacy Workshops (SPW), San Jose, CA, USA, 21–22 May 2015; pp. 180–184. [Google Scholar]
  94. Soomro, K.; Bhutta, M.N.M.; Khan, Z.; Tahir, M.A. Smart city big data analytics: An advanced review. WIREs Data Min. Knowl. Discov. 2019, 9, e1319. [Google Scholar] [CrossRef]
  95. Van den Abeele, F.; Hoebeke, J.; Moerman, I.; Demeester, P. Integration of Heterogeneous Devices and Communication Models via the Cloud in the Constrained Internet of Things. Int. J. Distrib. Sens. Netw. 2015, 11, 683425. [Google Scholar] [CrossRef]
  96. Lim, C.; Kim, K.-J.; Maglio, P.P. Smart cities with big data: Reference models, challenges, and considerations. Cities 2018, 82, 86–99. [Google Scholar] [CrossRef]
  97. Majeed, B.O.; Zulqarnain, M.; Majeed, B.T. Recent advancement in smart grid technology: Future prospects in the electrical power network. Ain Shams Eng. J. 2021, 12, 687–695. [Google Scholar] [CrossRef]
  98. Bertolini, M.; Buso, M.; Greco, L. Competition in smart distribution grids. Energy Policy 2020, 145, 111729. [Google Scholar] [CrossRef]
  99. Guo, K.; Lu, Y.; Gao, H.; Cao, R. Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City. Sensors 2018, 18, 1341. [Google Scholar] [CrossRef][Green Version]
  100. Blanco, J.L.; Fuchs, S.; Parsons, M.; Ribeirinho, M.J. Artificial intelligence: Construction technology’s next frontier. Build. Econ. 2018, 7–13. [Google Scholar] [CrossRef]
  101. Hughes, S.; Sarzynski, A. Building capacity for climate change adaptation in urban areas. Urban Clim. 2015, 13, 1–3. [Google Scholar] [CrossRef]
  102. Hales, S.; Baker, M.; Howden-Chapman, P.; Menne, B.; Woodruff, R.; Woodward, A. Implications of global climate change for housing, human settlements and public health. Rev. Environ. Health. 2007, 22, 295–302. [Google Scholar] [CrossRef] [PubMed]
  103. UNFCCC Paris Agreement—Status of Ratification. Available online: (accessed on 18 January 2020).
  104. Trindada, E.P.; Hinning, M.P.F.; Costa, E.M.D.; Marques, J.S.; Bastos, R.C.; Yigitcanlar, T. Sustainable development of smart cities: A systematic review of the literature. J. Open Innov. Technol. Mark. Complex. 2017, 3, 11. [Google Scholar] [CrossRef][Green Version]
  105. Ersoy, A.; Alberto, K.C. Understanding urban infrastructure via big data: The case of Belo Horizonte. Reg. Stud. Reg. Sci. 2019, 6, 374–379. [Google Scholar] [CrossRef]
  106. Al Nuaimi, E.; Al Neyadi, H.; Mohamed, N.; Al-Jaroodi, J. Applications of big data to smart cities. J. Internet Serv. Appl. 2015, 6, 25. [Google Scholar] [CrossRef][Green Version]
  107. Bauer, P.; Thorpe, A.; Brunet, G. The quiet revolution of numerical weather prediction. Nature 2015, 525, 47–55. [Google Scholar] [CrossRef]
  108. McGovern, A.; Elmore, K.L.; II, D.J.G.; Haupt, S.E.; Karstens, C.D.; Lagerquist, R.; Smith, T.; Williams, J.K. Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather. Bull. Am. Meteorol. Soc. 2017, 98, 2073–2090. [Google Scholar] [CrossRef]
  109. Allam, Z. Biotechnology to Render Future Cities as Living and Intelligent Organisms. In Biotechnology and Future Cities: Towards Sustainability, Resilience and Living Urban Organisms; Allam, Z., Ed.; Springer International Publishing: Cham, Switzerland, 2020; pp. 1–15. [Google Scholar]
  110. Sharifi, A.; Khavarian-Garmsir, A.R. The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management. Sci. Total Environ. 2020, 749, 142391. [Google Scholar] [CrossRef]
  111. Sánchez-Corcuera, R.; Nuñez-Marcos, A.; Sesma-Solance, J.; Bilbao-Jayo, A.; Mulero, R.; Zulaika, U.; Azkune, G.; Almeida, A. Smart cities survey: Technologies, application domains and challenges for the cities of the future. Int. J. Distrib. Sens. Netw. 2019, 15. [Google Scholar] [CrossRef][Green Version]
  112. Joss, S.; Sengers, F.; Schraven, D.; Caprotti, F.; Dayot, Y. The Smart City as Global Discourse: Storylines and Critical Junctures across 27 Cities. J. Urban Technol. 2019, 26, 3–34. [Google Scholar] [CrossRef][Green Version]
  113. Slavova, M.; Okwechime, E. African Smart Cities Strategies for Agenda 2063. Afr. J. Manag. 2016, 2, 210–229. [Google Scholar] [CrossRef]
  114. Gambella, C.; Monteil, J.; Dekusar, A.; Cabrero Barros, S.; Simonetto, A.; Lassoued, Y. A city-scale IoT-enabled ridesharing platform. Transp. Lett. 2019, 12, 706–712. [Google Scholar] [CrossRef][Green Version]
  115. Pojani, D.; Stead, D. Sustainable Urban Transport in the Developing World: Beyond Megacities. Sustainability 2015, 7, 7784–7805. [Google Scholar] [CrossRef][Green Version]
  116. Shapiro, J.M. Smart Cities: Quality of Life, Productivity, and the Growth Effects of Human Capital. Rev. Econ. Stat. 2006, 88, 324–335. [Google Scholar] [CrossRef]
  117. Zanella, A.; Bui, N.; Castellani, A.; Vangelista, L.; Zorzi, M. Internet of Things for Smart Cities. IEE Internet Things. 2014, 1, 22–32. [Google Scholar] [CrossRef]
  118. Ning, Z.; Xia, F.; Ullah, N.; Kong, X.; Hu, X. Vehicular Social Networks: Enabling Smart Mobility. IEEE Commun. Mag. 2017, 55, 16–55. [Google Scholar] [CrossRef]
  119. de Jong, M.; Joss, S.; Schraven, D.; Zhan, C.; Weijnen, M. Sustainable–smart–resilient–low carbon–eco–knowledge cities; making sense of a multitude of concepts promoting sustainable urbanization. J. Clean. Prod. 2015, 109, 25–38. [Google Scholar] [CrossRef][Green Version]
  120. Sanchez, L.; Muñoz, L.; Galache, J.A.; Sotres, P.; Santana, J.R.; Gutierrez, V.; Ramdhany, R.; Gluhak, A.; Krco, S.; Theodoridis, E.; et al. SmartSantander: IoT experimentation over a smart city testbed. Comput. Netw. 2014, 61, 217–238. [Google Scholar] [CrossRef][Green Version]
  121. Jin, J.; Gubbi, J.; Marusic, S.; Palaniswami, M. An Information Framework for Creating a Smart City through Internet of Things. IEEE Internet Things. 2014, 1, 112–121. [Google Scholar] [CrossRef]
  122. Albino, V.; Berardi, U.; Dangelico, R.M. Smart Cities: Definitions, Dimensions, Performance, and Initiatives. J. Urban Technol. 2015, 22, 3–21. [Google Scholar] [CrossRef]
  123. Caragliu, A.; Del Bo, C.; Nijkamp, P. Smart Cities in Europe. J. Urban Technol. 2011, 18, 65–82. [Google Scholar] [CrossRef]
  124. Lee, J.H.; Hancock, M.G.; Hu, M.-C. Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco. Technol. Soc. 2014, 89, 80–99. [Google Scholar] [CrossRef]
  125. Awasthi, A.; Venkitusamy, K.; Padmanaban, S.; Selvamuthukumaran, R.; Blaabjerg, F.; Singh, A.K. Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm. Energy 2017, 133, 70–78. [Google Scholar] [CrossRef]
  126. Giffinger, R.; Gudrun, H. Smart cities ranking: An effective instrument for the positioning of the cities? ACE Archit. City Environ. 2010, 4, 7–26. [Google Scholar]
  127. Chourabi, H.; Nam, T.; Walker, S.; Gil-Garcia, J.R.; Mellouli, S.; Nahon, K.; Pardo, T.A.; Scholl, H.J. Understanding Smart Cities: An Integrative Framework. In Proceedings of the 2012 45th Hawaii International Conference on System Sciences, Maui, HI, USA, 4–7 January 2012; pp. 2289–2297. [Google Scholar]
  128. Neirotti, P.; De Marco, A.; Cagliano, A.C.; Mangano, G.; Scorrano, F. Current trends in Smart City initiatives: Some stylised facts. Cities 2014, 38, 25–36. [Google Scholar] [CrossRef][Green Version]
  129. Harrison, C.; Eckman, B.; Hamilton, R.; Hartswick, P.; Kalagnanam, J.; Paraszczak, J.; Williams, P. Foundations for Smarter Cities. IBM J. Res. Dev. 2010, 54, 1–16. [Google Scholar] [CrossRef]
  130. Ahvenniemi, H.; Huovila, A.; Pinto-Seppa, I.; Airaksinen, M. What are the differences between sustainable and smart cities? Cities 2017, 60, 234–245. [Google Scholar] [CrossRef]
  131. Nam, T.; Pardo, T.A. Conceptualizing smart city with dimensions of technology, people, and institutions. In Proceedings of the 12th Annual International Digital Government Research Conference: Digital Government Innovation in Challenging Times, Association for Computing Machinery, College Park, MD, USA, 12–15 June 2011; pp. 282–291. [Google Scholar]
  132. Hollands, R.G. Will the real smart city please stand up? City 2008, 12, 303–320. [Google Scholar] [CrossRef]
  133. Townsend, A.M. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia; WW Norton & Company: New York, NY, USA, 2013. [Google Scholar]
  134. Söderström, O.; Paasche, T.; Klauser, F. Smart cities as corporate storytelling. City 2014, 18, 307–320. [Google Scholar] [CrossRef]
  135. Kitchin, R. The real-time city? Big data and smart urbanism. GeoJournal. 2014, 79, 1–14. [Google Scholar] [CrossRef][Green Version]
  136. Vanolo, A. Smartmentality: The Smart City as Disciplinary Strategy. Urban Stud. 2014, 51, 883–898. [Google Scholar] [CrossRef]
  137. Atzori, L.; Iera, A.; Morabito, G. The Internet of Things: A survey. Comput. Netw. 2010, 54, 2787–2805. [Google Scholar] [CrossRef]
  138. Gubbi, J.; Buyya, R.; Marusic, S.; Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Gener. Comput. Syst. 2013, 29, 1645–1660. [Google Scholar] [CrossRef][Green Version]
  139. Batty, M.; Axhausen, K.W.; Giannotti, F.; Pozdnoukhov, A.; Bazzani, A.; Wachowicz, M.; Ouzounis, G.; Portugali, Y. Smart cities of the future. Eur. Phys. J. Spec. Top. 2012, 214, 481–518. [Google Scholar] [CrossRef][Green Version]
Figure 1. The number of articles published per year. Note that the lower number of articles in 2021 is as the literature search for the purpose of this paper was done in early 2021. An upward trend is expected for 2021 and the following years.
Figure 1. The number of articles published per year. Note that the lower number of articles in 2021 is as the literature search for the purpose of this paper was done in early 2021. An upward trend is expected for 2021 and the following years.
Sustainability 13 07140 g001
Figure 2. The output of the term co-occurrence analysis for the whole study period (1991–2021).
Figure 2. The output of the term co-occurrence analysis for the whole study period (1991–2021).
Sustainability 13 07140 g002
Figure 3. The output of the term co-occurrence analysis for the first period (1991–2015).
Figure 3. The output of the term co-occurrence analysis for the first period (1991–2015).
Sustainability 13 07140 g003
Figure 4. Top 25 keywords with the strongest citation bursts in the first period. Bold format represent the beginning year of the burst period.
Figure 4. Top 25 keywords with the strongest citation bursts in the first period. Bold format represent the beginning year of the burst period.
Sustainability 13 07140 g004
Figure 5. The output of the term co-occurrence analysis for the second period (2016–2021).
Figure 5. The output of the term co-occurrence analysis for the second period (2016–2021).
Sustainability 13 07140 g005
Figure 6. The most influential journals contributing to the development of smart cities literature.
Figure 6. The most influential journals contributing to the development of smart cities literature.
Sustainability 13 07140 g006
Figure 7. Countries that have made significant contribution to the development of the smart cities literature.
Figure 7. Countries that have made significant contribution to the development of the smart cities literature.
Sustainability 13 07140 g007
Figure 8. Organizations that have made significant contributions to the advancement of the field.
Figure 8. Organizations that have made significant contributions to the advancement of the field.
Sustainability 13 07140 g008
Figure 9. The most influential documents contributing the development of the field.
Figure 9. The most influential documents contributing the development of the field.
Sustainability 13 07140 g009
Figure 10. The most influential authors.
Figure 10. The most influential authors.
Sustainability 13 07140 g010
Table 1. The top-10 countries contributing to smart cities research and examples of their highly cited publications.
Table 1. The top-10 countries contributing to smart cities research and examples of their highly cited publications.
CountryAffiliation of the First/Corresponding AuthorExample of Highly Cited Publication
USAUniversity of Chicago[116]
ItalyUniversity of Padova[117]
ChinaDalian University of Technology[118]
EnglandUniversity of Westminster[119]
SpainUniversity of Cantabria[120]
AustraliaSwinburne University of Technology[121]
CanadaRyerson University[122]
The NetherlandsVrije Universiteit Amsterdam[123]
South KoreaYonsei University[124]
IndiaNational Institute of Technology Allahabad[125]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Sharifi, A.; Allam, Z.; Feizizadeh, B.; Ghamari, H. Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends. Sustainability 2021, 13, 7140.

AMA Style

Sharifi A, Allam Z, Feizizadeh B, Ghamari H. Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends. Sustainability. 2021; 13(13):7140.

Chicago/Turabian Style

Sharifi, Ayyoob, Zaheer Allam, Bakhtiar Feizizadeh, and Hessam Ghamari. 2021. "Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends" Sustainability 13, no. 13: 7140.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop