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Review

Review of Big Data Implementation and Expectations in Smart Cities

by
Yingnan Zhuang
1,
Jeremy Cenci
2 and
Jiazhen Zhang
3,*
1
Faculty of Architecture and Urban Planning, Chongqing University, Shazheng Street, 174, Shapingba District, Chongqing 400045, China
2
Faculty of Architecture and Urban Planning, University of Mons, Rue d’Havré 88, 7000 Mons, Belgium
3
Wales College, Lanzhou University, 222 Tianshui South Rd., Chengguan District, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Buildings 2024, 14(12), 3717; https://doi.org/10.3390/buildings14123717
Submission received: 16 October 2024 / Revised: 12 November 2024 / Accepted: 13 November 2024 / Published: 21 November 2024
(This article belongs to the Special Issue Urban Planning and Construction Management Under Smart City Development)
Browse Figures
Figure 1
<p>Number of publications, 2015–2023.</p> "> Figure 2
<p>Knowledge map of collaborative journals publishing on big data and smart cities, 2015–2023.</p> "> Figure 3
<p>Knowledge map of countries cooperating in research on big data and smart cities, 2015–2023.</p> "> Figure 4
<p>Knowledge map of cooperative institutions in research on big data and smart cities, 2015–2023.</p> "> Figure 5
<p>Knowledge map of cooperative institutions researching big data and smart cities, 2015–2023.</p> "> Figure 6
<p>Co-occurrence network of highly cited articles in the field of big data and smart cities, 2015–2023.</p> "> Figure 7
<p>Time-zone view of research subjects, 2015–2023.</p> "> Figure 8
<p>Keywords co-occurrence network for big data and smart cities research, 2015–2023.</p> "> Figure 9
<p>Annual variations in co-occurring keywords in research papers related to big data and smart cities, 2015–2023.</p> "> Figure 10
<p>Co-citation network and clusters of articles in big data and smart cities research, 2015–2023.</p> "> Figure 11
<p>Annual variations in co-occurring keywords in big data and smart city research papers, 2015–2023.</p> "> Figure 12
<p>Mainstream framework in big data research and smart cities.</p> ">
Versions Notes

Abstract

:
With the construction of smart cities advancing, research on big data and smart cities has become crucial for sustainable development. This study seeks to fill gaps in the literature and elucidate the significance of big data and smart city research, offering a comprehensive analysis that aims to foster academic understanding, promote urban development, and drive technological innovation. Using bibliometric methods and Citespace software (6.2.R3), this study comprehensively examines the research landscape from 2015 to 2023, aiming to understand its dynamics. Under the guidance of the United Nations, global research on big data and smart cities is progressing. Using the Web of Science (WOS) Core Collection as the data source, an exhaustive visual analysis was conducted, revealing various aspects, including the literature output, journal distribution, geographic study trends, research themes, and collaborative networks of scholars and institutions. This study reveals a downward trend despite research growth from 2015 to 2020, focusing on digital technology, smart city innovations, energy management and environmental applications, data security, and sustainable development. However, biases persist towards technology, information silos, homogenised research, and short-sighted strategies. Research should prioritise effectiveness, applications, diverse fields, and interdisciplinary collaboration to advance smart cities comprehensively. In the post-COVID-19 era, using big data to optimise city management is key to fostering intelligent, green, and humane cities and to exploring efficient mechanisms to address urban development challenges in the new era.

1. Introduction

A smart city utilises information technology in infrastructure, buildings, and daily life to address social, economic, and environmental issues [1]. Big data, referring to large datasets that pose challenges for typical database tools, supports smart city planners in making informed, scientific decisions for urban development [2]. This approach enhances intelligence in key urban areas, promoting efficiency, sustainability, and optimisation [3]. Smart cities use advanced digital technologies such as the Internet of Things (IoT), cloud computing, and big data to improve people-oriented design, planning, building, managing, and serving urban areas [4]. This aligns with the people-oriented smart city concept that the United Nations Human Settlements Programme (UN-HABITAT) has advocated since 2018 [5]. The global push for smart cities and big data aims to streamline city management, encourage sustainability, and address urbanisation issues [6,7]. Governments worldwide back this trend with policies. For example, the US has its ‘Smart Planet’ strategy, Japan’s ‘I-Japan Strategy 2015′ brings convenience to the people through the intelligence of public utilities, Australia uses big data and IoT in its ‘National Digital Economy’ plan, and Singapore’s ‘Smart Nation 2025′ boosts Intelligent Transportation Systems, facilitating information exchange between roads, users, and transport networks [8,9,10,11].
This study will help researchers understand research trends, analyse the progress in big data and smart cities, and explore targeted strategies for improving the construction of urban systems. These intelligent strategies assist city managers in efficiently monitoring operations, boosting economic recovery, enhancing public services, and responding to health crises. The disasters and losses caused by the COVID-19 pandemic require us to re-examine our existing urban systems [12]. Applying big data and artificial intelligence technologies, such as the Internet of Things and large language models, empowers smart cities’ planning, construction, and operations, leading to optimal resource allocation and sustainable urban development [13].
Researchers have recently conducted exploratory studies in this area. For instance, the main research streams have explored the integration of cloud computing with the IoT, anticipating that it will significantly shape the internet and smart cities in the future [14,15]. Similarly, studies of the role played by deep learning in smart cities’ mobile networks reveal the potential for more intelligent systems to be developed [16,17]. These advancements demonstrate progress in integration for urban management while simultaneously underscoring the necessity of addressing privacy and security concerns [18,19,20].
Recent research concerning big data and smart cities often focuses separately on the design and planning of smart cities on the one hand and big data technologies on the other, neglecting the crucial intersection of both [21]. This approach can lead to an unbalanced emphasis on urban infrastructure and smart technologies or data analysis, disregarding their combined practical value [22]. The application of big data and smart cities during the COVID-19 pandemic, exemplified by China’s Baidu Map migration big data platform and anti-epidemic information topics on WeChat and Alipay, ensures the accuracy of epidemic information [23]. These platforms provide the public with timely and comprehensive epidemic information, aiding prevention and control efforts. This underscores the urgency and practical significance of researching big data and smart cities in the context of large-scale public events [24]. Therefore, there is a pressing need for research that bridges this gap. In its groundbreaking integration of big data with smart cities’ research, this study is essential in bridging the gap between the technology and its practical applications.
In this study, we use the state-of-the-art literature, a time-based overview of the current state of knowledge about a phenomenon, to suggest directions for future research in big data and smart cities [25]. Currently, there is no effective research from this perspective yet. Through bibliometrics and Citespace visualisation, this study will analyse research hotspots, trends, and strategies in big data and smart cities, offering valuable insights for accelerating smart city planning and construction [26].
This study emphasises the centrality of big data in developing smart cities and the need for interdisciplinary collaboration. This research aims to identify gaps, promote knowledge sharing, and provide decision support for stakeholders, promoting the integration of technology and urban construction in driving the sustainable development of smart cities.
The article’s culture is as follows: Section 2 provides a literature review of research in related fields, clarifying the definition of the research object, the source, and the value and significance of the research. Section 3 introduces the study’s data sources and research methodology and designs the study’s framework. Section 4 analyses the data results, including the study’s progress, research fields, hotspots, strategies, etc. Section 5 discusses the study, reflecting on its results, shortcomings, and limitations, and then, proposes directions for improvement in response to the shortcomings. Section 6 is the conclusion, discussing prospects and the study’s outlook.

2. Literature Review

2.1. Definition of Big Data and Smart Cities

2.1.1. Smart City

The smart city concept first originated in the United States in the 1990s [27]. In 1990, the International Congress on the theme ‘Smart Cities, Global Networks’ was held in San Francisco, USA. [28]. The definition of a smart city has gradually diversified, necessitating a multidisciplinary understanding to grasp its basic connotations [29]. Generally, researchers define a smart city from the perspective of city function and technology [30,31,32]. From a functional perspective, scholars believe that the development of a smart city is related to the wisdom level, coordination relationship, and mutual promotion of the city’s economy, government, people, transportation, environment, and life [33,34]. From the technical perspective, scholars generally believe that the development of smart cities results from integrating advanced information technology, including information storage, data circulation, and information utilisation, with the city’s existing systems [35].
In this research, a smart city is defined as an urban entity that uses big data and advanced information technology to tackle urbanisation’s multifaceted challenges [36] effectively. By promoting data sharing, information storage, and efficient information use in areas including the economy, governance, services, transportation, environmental protection, and daily life, this approach drives cities towards greater speed, convenience, and intelligence, better serving the human inhabitants [36].

2.1.2. Big Data

2008, Nature published a special issue titled ‘Big Data’ [37]. Subsequently, McKinsey’s ‘Big Data: The Next Frontier for Innovation, competition, and Productivity’, published in 2011, was the first study to formally and systematically introduce the concept of big data, which refers to datasets, such as the internet, social media, and mobile devices, as well as the explosive growth in the volume of data [38]. Big data refers to datasets of a size that exceeds typical database software tools’ capture, storage, management, and analytical capabilities [39]. McKinsey contends that the era of big data has arrived and that big data has become an important factor in production [40].
The arrival of the big data era offers powerful data support for increasingly complex urban research, which fits the need for new technologies in smart cities. Offering revolutionary opportunities for the construction and sustainable development of Smart Cities in 2015, the Smart City Expo World Congress (SCEWC), an event organised by UN-Habitat, the World Economic Forum, the Spanish Ministry of Industry, and many other organisations and government departments, was held in Barcelona [41,42]. Based on the 2030 Agenda for Sustainable Development goals adopted by the United Nations General Assembly in 2015, the conference proposed combining ideas and tools such as big data and technology with the development of smart cities [43].

2.1.3. Connection Between Big Data and Smart Cities

There are six main elements of a smart city: (1) a smart economy, (2) smart people, (3) smart governance, (4) smart mobility, (5) a smart environment, and (6) smart living [44]. When sources of data from human, intermediary, media, and machine-generated sources in urban environments are effectively captured, managed, and analysed by data science tools, the problems from these six characteristic areas can be addressed well, and big data can become an important means to promote smart city development [45,46,47]. The precision and fairness of urban governance, information collaboration and sharing, residents’ well-being, and economic development are important concerns in constructing smart cities [48]. In this regard, exploring the deep integration of big data and smart cities is of significant potential and value.

2.2. Development Mechanisms for Implementing Big Data in Smart Cities

2.2.1. Development Path

The development of big data implementation in smart cities can be divided into four stages: (1) the starting stage, (2) the technological accumulation stage, (3) the application exploration and exploration stage, and (4) the integration and breakthrough stage:
(1)
The starting stage: The concept of smart cities is proposed and understood during the starting stage, and big data technology starts to develop. A smart city is regarded as a concept that enhances the efficiency of urban management and services through information technology [49].
(2)
The technological accumulation stage: With the maturation of IoT, cloud computing, and mobile internet technologies, the construction of smart cities takes on a stronger technical foundation. Big data technology has also been rapidly developed during this stage, providing strong data processing and analysis capabilities for smart cities [50].
(3)
The application and exploration stage: Smart cities have begun to explore applications in various fields such as the economy, environment, transportation, healthcare, energy, etc., and they realise real-time monitoring and intelligent decision-making support for urban operations using big data technology [51]. Smart cities also commit to improving public service levels, optimising resource allocation, and transforming urban governance capabilities towards greater fairness and sustainability [52].
(4)
The integration and breakthrough stage: During this phase, smart cities deeply integrate data resources from IoT, cloud computing, and big data to achieve precise and intelligent urban management [53]. As the degree of intelligence in cities advances, overcoming technical hurdles, enhancing data management, and ensuring data security remain crucial for fully unleashing the value of data in their development [54].

2.2.2. Current Status

Technological innovation, policies and regulations, and the economy have emerged as key factors influencing the development of smart cities and big data research [55].
First, the development of smart cities and big data relies on the maturity of related innovations in information technology and big data technologies [56]. With the breakthrough development of the IoT, cloud computing, AI, and other technologies, smart cities have realised more efficient data collection, processing, and application [57]. These technologies provide smart cities with a strong infrastructure and intelligent decision-support capabilities.
The relevant policies and regulations in developing smart cities and big data are crucial. Policy orientation and data protection directly affect the speed and breadth of smart city construction [58].
Economic factors should also be considered. The construction of smart cities requires significant initial investment in a large-scale data platform, which has a lengthy ROI cycle and must prove cost-effective for builders [59]. Additionally, as smart cities and big data services become more market-driven, market demand emerges as a crucial force determining their progress [60].
In addition to smart cities and big data, digital twins, digital cities, informalised cities, and internet cities have emerged [61]. These concepts emphasise improving urban management and service efficiency using information technology to achieve sustainable city development [62]. Despite the differences in the implementation paths and focuses, this focuses on the research dynamics in this field, analysing the research hotspots, development trends, and research strategies related to big data and smart cities between 2015 and 2023 and putting forward qualitative suggestions for the undeveloped field of big data and smart cities in light of the current research situation.

3. Data and Methodology

3.1. Data Sources

This study uses the Web Of Science (WOS) Core Collection as one of the most authoritative academic databases worldwide [63]. The WOS Core Collection has a high level of academic authority due to its strict inclusion standards and extensive disciplinary coverage [64]. It has carefully selected and collected over 1.7 billion cited articles and 159 million records, covering fields such as the natural sciences, social sciences, arts, and humanities. This extensive literature coverage provides abundant data sources for research, ensuring sufficient sample sizes, comprehensiveness, and research value.
The theme of this research is the visualisation and analysis of the research hotspots, development trends, and dynamics of big data and smart cities. To ensure the accuracy of our research, we selected ‘big data’ and ‘smart city’ as the core topics of our search and analysis. The document types included in the search were articles, proceeding papers, and review articles; we excluded early-access publications, retracted publication data papers, and book chapters, as well as duplicate articles, aiming to improve the efficiency of the search and guarantee the relevance and quality of the search results. As noted in Section 2.1, in terms of big data and smart cities, the 2015 Smart City Expo World Congress (SCEWC) put forward the innovative concept of combining big data with the construction of smart cities; this innovation has significantly accelerated the process of researching big data and smart cities globally [65]. For this reason, the data collection timeframe set for this search was from 1 January 2015 to 31 December 2023. Based on the above search criteria, 3067 papers were retrieved and exported as plain text documents, and the final database contains the articles’ titles, authors and author information, abstracts, keywords, publication dates, categories, sources, and document type.

3.2. Research Methods

Citespace (6.2.R3) is used as a visualisation tool in this review. Citespace, a Java-based visualisation, is suitable for use on any platform due to its high level of data compatibility, allowing for the in-depth visualisation and analysis of data from different dimensions. The user interface and visual graphic language of Citespace are clear and diverse, and they can make complex visual graphs highly readable [66]. The tool has been widely used in scientific literature research due to its powerful citation network analysis function [67,68]. With the help of Citespace, a graph or table called a “knowledge graph” or “bibliometric graph” can be created, which allows researchers to efficiently identify the key literature, research hotspots, and development trends, as well as the connections between different topics in the field, by using the generated icons. This intuitive visualisation provides powerful support for in-depth exploration of emerging topics such as big data and smart cities. It assists in comprehensively elucidating the knowledge structure and development of the field.
After completing the WOS data collection, we moved on to further analysis. First, annual trends in the number of publications between 2015 and 2023 were derived using the analysis tools in WOS. Then, the database of plain-text file documents was inputted into Citespace (6.2.R3), and the collaborative distribution network of the issuing journals, regions, authors, and research organisations with co-citation and co-presentation analyses were performed for the research progress section. Subsequently, the statistical analysis of highly cited articles and research areas is carried out for the research area section; we used Excel to assist with the data statistics. Finally, co-occurrence and clustering analyses of keywords and studies were conducted for the research hotspots and strategies section to reveal the research hotspots and trends in big data and smart cities.

4. Results

4.1. Overview of Research Progress

4.1.1. Tendency of Publications

The statistical analysis and charting of annual publications reveal trends in the research, indicating changes in the research focus, technological progress, and research activity [69]. The yearly publication counts for big data and smart cities, derived from the WOS analysis results and compiled using Excel, results in the distribution presented in Figure 1. Between 2015 and 2023, the number of publications on big data and smart city literature showed a growth trend, followed by a decline, which peaked in 2020. The development trend of the number of publications is divided into two stages:
  • National strategies drive significant growth (2015–2020)
Big data and smart cities research grew significantly during this phase, peaking in 2020. During this stage, countries actively responded to the call of UN-Habitat and the annual SCEWC conference for big data to promote the construction of smart cities; they released national strategies and plans on big data [70]. For instance, the US federal government launched the White House Smart Cities Initiative, and the Chinese State Council released the Outline of Action for Promoting the Development of Big Data [71,72]. These national initiatives and programs have played a role in the ongoing growth of research in this field.
  • Decline and future potential in research (2021–2023)
The number of publications on big data and smart cities exhibited a yearly decrease during this period, particularly in 2023. This trend was influenced by COVID-19 and the intensification of research contextualised by the global health and economic crises [73,74]. During the pandemic, academic conferences related to big data and smart cities were cancelled due to the pandemic’s impact, and they could not be carried out offline. Academic exchanges between different regions were restricted. At the same time, the research focus has shifted towards more specific topics, such as resilient cities, due to the need for public safety incidents, which may lead to a decrease in publications [75]. Nevertheless, big data and smart cities are key to economic recovery, urban governance, and digital transformation in the pandemic era, suggesting a potential resurgence in related publications.
Buildings 14 03717 g001
Figure 1. Number of publications, 2015–2023.
Figure 1. Number of publications, 2015–2023.
Buildings 14 03717 g001

4.1.2. Major Journal Analysis

A statistical analysis of the distribution of published journals reveals the importance and influence of research results in various fields, clarifying collaborative efforts and academic exchanges [76]. We set the node types to “cited journal” and proceeded with the co-citation analysis. Figure 2 shows a map of the co-cited journals, illustrating the distribution, connections, and main categories of journals in 2015–2023. The darker the color of the dots in Figure 2, the greater the influence of the journal in the field of big data and smart cities. The number of nodes in the journal distribution was 797, the connection value was 2393, and the density was 0.0075.
The top 10 journals with the highest number of papers on big data and smart cities are listed in Table 1. It is clear that the articles were mostly published from 2015 to 2017, with the top three most cited journals being ‘IEEE Access’, ‘Future IoT Journal’, and ‘Sensors’. By classifying the journals, as shown in Figure 2, it is clear that big data and smart cities research stresses interdisciplinary collaboration, spanning fields including computer science, environmental engineering, urban planning, social sciences, and geosciences.

4.1.3. Major Regions Analysis

Obtaining data on the distribution of cooperating countries is crucial for understanding different countries’ research focus in the field, promoting international academic exchanges, and predicting future cooperation opportunities and directions [77,78].
We visually analysed the cooperation networks among countries regarding big data and smart cities by designating the node types as “country”. Table 2 lists the top 10 countries with the most articles published in this domain. Notably, from 2015 to 2023, China, the US, India, Italy, and the UK emerged as the top five leading nations in publications. These countries actively facilitate progress in big data and smart city initiatives.
The network structure shown in Figure 3 highlights the cooperation intensity between regions. Although India and Australia are not among the top publishers, they distinguish themselves through their extensive international collaborations, which leads to them having the most joint publications. Meanwhile, China and the United States are the top two countries in terms of the volume of literature published, and both occupy central positions in the cooperation network. Nevertheless, they appear relatively conservative regarding international cooperation compared to the other countries in the top 10 list in Table 2. In addition, Canada and South Korea are currently on the periphery of the cooperation network and urgently need to strengthen their international cooperation and exchange. It would be mutually beneficial if countries such as the United States and Canada actively expand their research cooperation and exchange channels within the future cooperation network, globally sharing their research findings and practical expertise in smart cities and big data. This would motivate countries with fewer publications to actively engage in big data and smart cities research, potentially reversing the trend of declining research interest and bringing new opportunities for the smart construction of global cities.

4.1.4. Author Cooperation Distribution Analysis

Producing an author cooperation network helps researchers visualise the impact of key authors and their teams, thereby enabling a better grasp of the collaborative relationship between big data and smart cities research, as well as the main research topics and areas of progress [79].
Figure 4 clearly shows the author’s collaboration network in big data and smart cities research, which contains 401 nodes and 335 connections and has a network density of 0.0042. The connecting lines in the figure indicate the collaborative relationships between authors, while the node’s annual cycle size reflects the author’s publication frequency [80]. The darker the color of the dots in Figure 4, the greater the influence of the institutions in the field of big data and smart cities. In Figure 4, scholars such as Simon Elias Bibri, Rashid Mehmood, and Iyad Katib form a more extensive collaborative network. In contrast, the collaboration between Luca Foschini, Antonio Corradi, and Isam Mashhour Al Jawarneh, as well as between Anand Paul, M Mazhar Rathore, and Awais Ahmad, also form small collaborative clusters. Three scholars, Simon Elias Bibri, Rashid Mehmood, and Luca Foschini, have the largest node chronological area. As shown in Table 3, although they rank among the top three authors’ collaborative networks in terms of publications in the field, their network remains decentralised with minimal academic exchange and cooperation, lacking a leading core team to guide research in big data and smart cities.
In summary, despite active contributions to big data and smart cities research from scholars such as Simon Elias Bibri, Rashid Mehmood, and Luca Foschini, insufficient academic cooperation and a lack of core leadership teams remain. This hinders in-depth research and diversified progress in the field.

4.1.5. Distribution of Contributing Institutions

Analysing the distribution of contributing institutions reveals the ones with notable outputs in big data and smart cities research, thus enabling us to map the field’s research power [55]. This analysis can also promote inter-institutional collaboration and resource sharing, ultimately boosting research efficiency and quality and proving pivotal in driving advancements in this field [81,82].
Based on the research method described in Section 3.2, this study utilises the Citespace tool, setting the node types as ”institution”, to conduct a visual analysis. The results are shown in Figure 5, which visualises the institutional collaboration network in big data and smart city research, which contains 372 nodes and 530 connections and has a network density of 0.0077. The connecting lines in the figure represent the collaborative relationships between institutions, while the size of each node’s annual cycle represents the institution’s overall frequency [83]. The institutions publishing research on big data and smart cities exhibit a wide distribution in the figure, suggesting diverse contributors. Additionally, the cooperative network among these institutions is closely knit, with universities dominating as the primary institutional units.
Furthermore, higher education institutions exhibit heightened activity in big data and smart cities research. The information related to the institutions with the top 10 highest total number of publications in the field is listed in Table 4. Combined with Figure 5, Table 4 clearly shows that the Chinese Academy of Sciences has the largest number of publications, amounting to 41. Among the top 10 institutions, those from China have the highest representation, led by the Chinese Academy of Sciences, Wuhan University, and the Hong Kong Polytechnic University. Overall, institutions in this field collaborate globally and across regions, driving knowledge exchange. Universities lead this network and are central to promoting research. However, research institutes and companies linked to big data and smart cities produce fewer articles. Enhanced cooperation among all institutions can revitalise big data and smart cities research, accelerating progress in the field.

4.2. Field of Research

4.2.1. Highly Cited Articles

Analysing highly cited literature through co-citation networks can enable us to pinpoint crucial theories, methods, and ideas [84]. This facilitates summarising the main research content and knowledge base of big data and smart cities, revealing research gaps and indicating areas for future breakthroughs.
We set the node types as “reference” for this part of the visual analysis. The co-citation network is constructed to obtain Figure 6; the nodes in the co-citation network represent the literature, and the size of the nodes is proportional to the citation frequency of the studies. The connecting line indicates the co-citation relationship between the studies [85]. The darker the color of the dots in Figure 6, the greater the influence of the authors in the field. Combining Citespace with the citation report from WOS, Table 5 shows detailed information about the key articles with high citation frequency in big data and smart city fields. The citations in Table 5 are generated by the WOS and Citespace and include the number of self-citations. By referring to both Figure 6 and Table 5, the most frequently cited article is ‘Integration of Cloud Computing and IoT: A survey’, with 1212 citations and a JIF (journal impact factor) of 7.5. The second most frequently cited article is ‘Literature review’. The second most cited article is ‘Literature review of Industry 4.0 and related technologies’, with 792 citations and a JIF of 8.3. The third most cited article is ‘Literature review of Industry 4.0 and related technologies’, with 757 citations and a JIF of 8.3. The fourth most cited article is a review of Industry 4.0 and related technologies, with a total of 708 citations and a JIF of 35.6. The second and third most frequently cited articles are highly centralised in the network, demonstrating strong connections to other articles with greater intensity and frequency. The JIF measures a journal’s impact based on its papers’ average citations from the prior year. Citation counts include all references to an article, regardless of its citation frequency or JIF.
Further in-depth analysis of Table 5 reveals that the main academic areas and knowledge bases on which research is concentrated include the following topics:
  • Convergence of computer science and technology, data science, and smart cities
Table 5 notes several highly cited papers in computer science and technology. For example, Botta et al. [14] studied how cloud computing and IoT can work together, discussing their basic premises, benefits, challenges, and current platforms. This combination is expected to be key for the internet’s future and the growth of smart cities. Zhang et al. examined how deep learning can merge with mobile and wireless networks for smart city research and tech deployment [17]. Meanwhile, Oztemel et al. reviewed Industry 4.0 tech and its importance for smart cities [86]. These papers stress big data’s role in building and managing smart cities, from collection to analysis. They show the importance of big data, supported by computer science and analysis, for making cities smarter, more efficient, and more sustainable.
  • Convergence of ICT, big data, and smart cities
As new telecommunication technologies merge, researchers are increasingly exploring the integration of ICT, big data, and smart cities. Yao et al. [7] used DMVST-Net to predict the composition demand, and Marjani et al. clarified the link between big data analytics and the IoT via a new framework [87]. These studies underscore the prospect of merging telecom networks with big data models in the IoT sphere for real-time analysis and use in smart cities.
In summary, the current big data and smart cities research shows a strong focus and similarity in methods, with cross-fertilisation mainly occurring within closely related disciplines; there is limited exploration beyond these boundaries. Much of the research is concentrated on summarising the existing findings and analysing challenges, often neglecting the exploration of practical technological innovations in developing smart cities. As a result, the practical value of this research is not fully demonstrated.

4.2.2. Research Areas

Time-zone co-occurrence analysis is a powerful tool for investigating a research area’s dynamic development. It reveals evolution and interconnections across time, offering a crucial foundation for predicting disciplinary trends [88,89].
For this part of the analysis, we set the node types as “categories” and generate a time-zone co-occurrence map to analyse the category visually. In the graph, the circles represent subjects. Their position in different time zones shows when they first appeared in the dataset, and the lines connecting them indicate collaboration between subjects. As shown in Figure 7, the analysis captured 112 nodes and 298 connections, resulting in a network density of 0.0479. The results show that 2015 to 2017 marked an active phase for big data and smart cities. Many subjects emerged during this time, aligning with the growing research interest in this area. From 2018 to 2023, fewer new topics appeared, indicating a smoother development phase. However, there is still a strong correlation between these newer and earlier topics. Among the emerging categories, “computer science, information systems” (805) had the highest influence, with a centrality of 0.13, closely followed by “engineering: electrical and electronic” (786), with a centrality of 0.16.
Table 6 lists the top 10 subject groups based on their centrality rankings; they were dominated by the three main topics of computers, information and communication, and environment, with half comprising the computer-related categories alone. Computer science is closely linked to advancements in big data and smart cities, overlapping with info-comm, environmental care, and city growth. The key aspects include utilising computer science for big data platforms and city data models, recognising that ICT is the foundation of smart cities, and emphasising ecology and sustainability. However, the current research has gaps, necessitating cross-disciplinary innovation for future smart cities and the advancement of big data. Interdisciplinary collaboration in smart cities and big data is essential, as this will combine expertise from various fields to develop innovative solutions to the challenges of urbanisation. It also advances data-driven decision-making, improving urban management’s efficiency and sustainability while closely aligning technology with societal needs.

4.3. Research Hotspots and Research Strategies

4.3.1. Keyword Co-Occurrence Network

A co-occurrence analysis of keywords helps to capture insights into the core themes and hot research directions within big data and smart cities; it also enables us to understand the strength of connections between different research themes [90,91].
Utilising Citespace, we set the node type as “keywords”. In Figure 8, the nodes represent keywords, with their size corresponding to the frequency of the keywords in the articles. Connecting lines show co-occurrence relationships [92]. The analysis captured 558 nodes and 1897 connections, with a network density of 0.0121. Table 7 details 20 keywords with frequencies over 80. Figure 8 and Table 7 reveal strong linkages and similarities among the keywords. The keyword with the highest frequency is ‘big data’. It has appeared 1059 times since 2015 and is closely linked to ‘smart city’, ‘Internet’, ‘AI’, ‘model’, etc. This underscores big data’s importance in the construction of smart cities. Other key influential keywords include ‘Internet’, ‘management’, ‘technology’, ‘IoT’, ‘city’, ‘framework’, ‘architecture’, ‘cloud computing’, ‘deep learning’, and ‘challenge’, all with high centrality.
The development of big data and smart cities is heavily dependent on innovations in information technology, such as the IoT, cloud computing, AI, and network communication. Many articles explore how these advancements support urban planning and the construction of smart cities using big data analytics, examining their characteristics and practical applications [93,94]. For instance, urban planning and building smart cities based on the IoT can be achieved using big data analytics [4]. According to the keywords, the construction of big data and smart cities is inherently intertwined with its most fundamental material carriers and service objects, such as ‘city’, ‘building’, ‘social management’, and ‘people’. Other researchers focus on how big data aids urban planning and architecture, creating models to assess cities’ and structures’ current states. These studies often explore how technology can practically improve city dwellers’ lives in modern times—for example, in the article titled “On COVID-19 Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with AI to Benefit Urban Health Monitoring and Management”. In the era of Industry 4.0, many articles have also actively explored the research progress and future challenges related to big data and smart cities [51]. Challenges such as balancing data sharing and privacy with robust information architecture and addressing smart cities’ tech bottlenecks require further investigation.

4.3.2. Keyword Co-Occurrence Time-Zone Analysis

Introducing a temporal dimension to keyword co-occurrence networks reveals patterns and trends. This helps researchers to understand the field’s progression and paves the way for more in-depth clustering analysis [95].
We set “keywords” as the node types in Citespace for this aspect of the visualisation analysis. Following this, the layout was adjusted to the time zone in the control panel, revealing a time-zone map of the keyword co-occurrence network, as displayed in Figure 9. Figure 9 reveals two distinct phases: a rapid development phase from 2015 to 2020 and a gradual decline from 2021 to 2023. By extracting the top 10 high-frequency keywords for each stage, we obtain Table 8. The evolution of the research hotspots is analysed as follows:
  • Rapid development (2015–2020)
In 2015, research on the IoT, data analytics, smart management, and modelling surged, highlighting challenges in big data and smart cities. Privacy, security, and data power were initially explored. Concurrently, innovative technologies such as the IoT, deep learning, edge computing, AI, and blockchain emerged, linking big data and smart cities. Gradually, they integrated, aiding global urban development. During the COVID-19 pandemic, beginning in late 2019, these technologies were crucial for epidemic control, urban management, and public health, underscoring their significance. The field peaked in 2020.
  • Gradual decline (2021–2023)
From 2021 to 2023, COVID-19 indirectly affected the progress of big data and smart cities research, resulting in stagnation or decline. Economic strain, technological hurdles, and research cooperation barriers contributed to this trend, making technological advances more costly and challenging. However, researchers continued to focus on contemporary challenges, using complex technologies such as digital twins to simulate, predict, and optimise city operations. These technologies have been extensively tested in transportation and urban planning, helping to elucidate trends in city operations and strengthening urban management science. In the long term, big data and smart cities have significant potential to enhance urban management and public services.
Buildings 14 03717 g009
Figure 9. Annual variations in co-occurring keywords in research papers related to big data and smart cities, 2015–2023.
Figure 9. Annual variations in co-occurring keywords in research papers related to big data and smart cities, 2015–2023.
Buildings 14 03717 g009
Table 8. The top 10 high-frequency keywords of papers published on big data and smart cities in the period 2015–2023.
Table 8. The top 10 high-frequency keywords of papers published on big data and smart cities in the period 2015–2023.
No.Freq.CentralityKeywordsYear
The first period (2015–2020)
110590.03Big data2015
26300.01Smart city2015
34000.04City 2015
43610.05Internet2015
52880.04IoT2015
61960.02Challenges2016
71910.02Things2016
81810.03Framework2016
91800.05Management2015
101580.01Model2016
The second period (2021–2023)
11160.01Digital twin2021
12150.02Smart mobility2021
13110.00Bibliometric analysis2021
1490.01Circular economy2021
1590.00Industry 4.02021
1690.01Traffic flow prediction2021
1790.03Urban development2021
1880.00Digital transformation2021
1980.00Sustainable mobility2021
2070.01Federated learning2022

4.3.3. Keyword Clustering Analysis

In this part of the analysis, keywords are clustered to identify research hotspots and themes based on the previous co-occurrence network and time partition analysis. This establishes the foundation for future timeline analysis and trend forecasting [96].
We set the node type as “keywords” in Citespace, generating a visual analysis; we then selected ‘All in One: clustering, optimising layout and style’ in the upper sidebar. To generate accurate clustering results, we chose the LLR algorithm to perform the clustering calculations and obtain the results, presented in Figure 10. The figure’s labels indicate the cluster topics, with the cluster size reflecting the number of keywords. Nodes, sized by their occurrence frequency, represent these keywords. Lines connecting nodes show keyword correlations, with varying colours by year [92]. Figure 10 shows that 11 clustering labels are obtained in this analysis, and the specific information for each label is listed in Table 9. Ordered from 0 to 11, the research directions shown in the labels are the IoT, energy consumption, deep learning, city logistics, big data architecture, the circular economy, smart card data, smart mobility, smart sustainable cities, data storage, and social media. The value of the clustering module Q is =0.4831, ranging from 0 to 1. The average profile value of the clusters is S = 0.4831. The average profile value is S = 0.7258, with values ranging from −1 to 1 and greater than 0.7, indicating that the clustering shown in this paper is convincing.
As shown in Table 9, the largest cluster (#0) contains 77 articles with a silhouette value of 0.805. This research focused on the IoT, cloud computing, security, smart cities, and edge computing. The second largest cluster (#1) contains 76 articles, with a silhouette value of 0.654. This research focused on digital transformation, e-government, sustainable city, open data and citizen participation. The third largest cluster (#2) contains 53 articles, with a silhouette value of 0.595. The research focused on energy consumption, energy efficiency, transfer learning, neural networks, and smart grid. The fourth largest cluster (#3) contains 53 articles, with a silhouette value of 0.743. This research focused on deep learning, machine learning, intelligent transportation, data models, and urban computing. The fifth largest cluster (#4) consists of 47 articles, with a silhouette value of 0.688. The research focused on city logistics, differential privacy, green parks, community detection, and prediction. In the sixth largest cluster (#5), there were 47 articles, with a silhouette value of 0.555. This knowledge group includes research on big data architecture, platformisation, electric vehicles, sensor networks, and algorithmic governance. The seventh cluster (#6) comprises 45 articles and boasts a silhouette value of 0.708. This research primarily emphasises the circular economy, urban metabolism, sustainable progress, and Industry 4.0. The eighth cluster (#7) contains 38 articles, with a silhouette value of 0.852. In this cluster, the research mainly focused on smart card data, data mining, human mobility, travel behaviour, and mobile phone data. The ninth cluster (#8) contains 35 articles, with a silhouette value of 0.75. The research focused on smart mobility, sustainable mobility, traffic flow prediction, AI, and urban mobility. In the 10th cluster (#9), the number of articles is 33, with a silhouette value of 0.86. This research mainly focuses on smart sustainable cities, sustainable cities, data-driven smart sustainable cities, compact cities, and eco-cities. The 11th cluster (#10) contains 24 articles, with a silhouette value 0.829. This research focuses on data storage, electronic health records, smart devices, sharing, and medical data. The last cluster (#11) comprises 20 articles, with a silhouette value 0.915. This research concerns social media, operations management, IoT, digitalisation, and resilience.
The research clusters mentioned above are interconnected and centre on smart technology, data science, machine learning, green sustainability, digital transformation, and social technology. By examining the top 12 clusters in the related literature, three main research areas emerge in big data and smart cities: big data-driven infrastructure and service innovation, smart city energy and environmental management, and the application of data security and privacy in smart cities.
  • Innovation in big data-driven smart cities infrastructure and services:
This research aims to improve smart cities’ infrastructure and services using big data technologies. Cluster #0 underscores the roles of the IoT, the cloud, and edge computing in realising smart cities. Clusters #1, #3, #7, and #8 stress smart transportation, travel, data mining, and human behaviour analysis for service innovation. These studies aim to optimise resource allocation, boost management efficiency, and enhance residents’ quality of life.
  • Energy management and environmental protection in smart cities:
Given the growing concerns over global climate change and energy constraints, efficiently managing energy and environmental sustainability in the construction of smart cities has become a crucial research area. Clusters #2 and #6 concentrate on energy consumption, efficiency, smart grids, and the recycling economy. Implementing advanced energy-saving technologies, promoting renewable energy, and establishing intelligent energy management systems can significantly reduce carbon emissions and resource usage in cities, providing both economic and environmental benefits.
  • Data security and privacy protection in smart cities:
During the construction of smart cities, vast amounts of data containing personal, private, and sensitive information are generated and processed. Therefore, the question of how to effectively utilise data under the premise of safeguarding data security and personal privacy is a pressing issue. Security techniques, in Cluster #0, and differential privacy, in Cluster #4, offer solutions. Advanced encryption, access control, and data desensitisation fully guarantee data security and privacy.
Furthermore, the clusters shown in Figure 11 are closely interconnected, obscuring the distinct label colour blocks. This indicates a tendency towards homogeneity in big data and smart cities research, with limited cross-field collaboration and inadequate information sharing, highlighting a significant ‘information silo’ problem.

4.3.4. Research Clustering Timeline

The research clustering timeline sorts the keyword clusters according to their initial year, revealing the themes’ freshness and evolution over time [97,98].
The node types are set to “keyword” in Citespace, the layout is set to time-zone view in the control panel, and the LLR algorithm is used to generate the visual analysis. Figure 11 illustrates the timeline, where the horizontal axis represents time, showing the first appearance of nodes from left to right, which signifies the progression of research from early to recent times. The vertical axis represents the various clusters. The keywords illustrated in Figure 11 cover a wide range of topics and technological areas related to big data and smart cities, including the IoT, digital transformation, energy consumption, deep learning, urban logistics, big data architecture, the circular economy, augmented reality technology, behavioural analytics, data mining, smart mobility, and sustainability. These themes construct a research framework for big data and smart cities from the three dimensions of vectors, technologies, and applications.
During the first stage (2015–2020), research on big data and smart cities is vibrant, driven by cloud computing, deep learning, and 5G. This period was characterised by a focus on sustainable urban development that is both environmentally friendly and resource-efficient. However, the subsequent period from 2021 to 2023 saw the emergence of new barriers to the field of smart city construction, such as increased technical intricacies and a dearth of skilled professionals, which resulted in a decline in research vigour. Even so, research is still centred on technology and application concepts, neglecting personalised solutions for practical issues. There is also a growing need for long-term maintenance mechanisms in smart cities and the development of big data.
These dilemmas highlight the need for a paradigm shift from an exclusive emphasis on technical research to an approach that values the practical application of big data in the construction of smart cities. By proactively adapting to urban growth and refining our research and implementation methods, we can ensure the sustainable advancement of smart cities.

4.3.5. Research Trends Analysis

Analysing the emergent keywords uncovers key terms swiftly accumulating a high volume of citations within a defined period, which often serve as signposts for burgeoning areas of inquiry or emerging themes [99].
By selecting ‘Cluster Explorer’ in Citespace, the top 20 keywords with the strongest citation bursts were identified and listed in Table 10. Additionally, Table 10 shows the 20 bursts that occurred between 2015 and 2023. The main trend in big data and smart city research evolved through three distinct phases. (1) Between 2015 and 2016, the focus was on establishing fundamental technologies and facilities for big data and smart cities. Topics such as human mobility and business intelligence marked the initial recognition of data’s role in smart cities. At the same time, ‘vision’ and ‘cloud computing’ signified technological advancements, paving the way for future research. (2) From 2017 to 2019, the field saw increased research activity in conjunction with technological advancements. Topics expanded to include ‘smart card data’, ‘cloud’, ‘fog computing’, ‘sustainable smart cities’, ‘IoT’, ‘data management’, ‘wireless sensor networks’, ‘urban forms’, ‘typology’, ‘issues’, and ‘public participation’. This changing emphasis highlights the significance of technology, sustainability, and community in the development of smart cities, showing a growing recognition that smart cities involve not only tech advances but also social and environmental factors. (3) From 2020 to 2023, big data and smart cities research moved towards practical use and innovative progress. Trends in sensors, urban information, traffic predictions, and Industry 4.0 aimed to solve the challenges associated with smart cities, hinting at future advancements that stress cross-field and cross-industry collaborations.
Big data and smart cities research has evolved from laying the technical groundwork to achieving rapid progress, practical applications, and innovative growth. This volution has led the field to consider social and environmental aspects, paving the way for new opportunities as technology and research advance.

5. Discussion

This study focuses on big data and smart cities, employing bibliometrics and Citespace to conduct a comprehensive visual analysis. Figure 12 summarises the main research areas related to big data and smart cities. It illustrates that the progress of big data and smart cities research showed significant growth between 2015 and 2020, reaching its zenith in 2020, beyond which there was a gentle decline. The research field is biased towards technological exploration, characterised by minimal disciplinary variance, robust interdisciplinarity, and dense keyword clustering. The research hotspots mainly focus on big data-driven smart city infrastructure and service innovation, energy management and environmental protection in smart cities, data security, and privacy protection. The keyword analysis allows us to trace an evolution from basic technology to rapid development, accentuating contemporary practicality and innovative applications pointing towards future research directions.
Research findings emphasise the significance of integrating big data technology into smart city development for sustainability. This integration advances urban planning and management and fosters sustainable urban growth by optimising resource utilisation and enhancing public services. The identified hotspots, such as infrastructure innovation, energy management, and data security, directly contribute to making cities smarter, more resilient, and better equipped to face future challenges. The study aligns with urban construction trends, targeting specific areas. Future progress necessitates breaking down information silos and fostering interdisciplinary collaborations in various fields, such as psychology, medicine, and urban transportation, particularly in the post-pandemic era, where urban areas have been significantly affected. To achieve this, people can establish an interdisciplinary platform for regular seminars, workshops, and expert collaboration, complemented by an online data-sharing platform to bridge information gaps and facilitate cross-domain connectivity. By promoting big data and the construction of smart cities, we can enhance urban management, public services, and resource allocation while addressing service gaps. It will benefit crises if people work together to develop an intelligent warning system that utilises big data and artificial intelligence for real-time risk monitoring. Meanwhile, utilising big data analysis to provide information for urban planning ensures the optimal layout of medical facilities, green spaces, and emergency routes. In addition, collaborative research projects, labs, and talent training programs will further integrate different fields, boosting the city’s emergency response capabilities and aligning research efforts with practical needs, ultimately contributing to sustainable urban growth and citizen well-being.
This study has a few limitations. First, it relies on the WOS database alone, which might limit the breadth of the research, missing out on the diverse subjects and multilingual content available in databases such as PubMed and CNKI. Additionally, the dataset, starting from 2015, could omit some of the relevant literature due to the timing of the search, excluding earlier or later publications. The review does not include any articles or research published before and after this time. Therefore, the results of this study are time-sensitive. Lastly, Citespace is not the sole analysis tool available. Other software like VOS Viewer (V1.6.20), Gephi (V0.10.0), or Sci2 Tool (V1.3) could offer distinct viewpoints and outcomes. There is certainly the potential for this study’s methodology to be enhanced in future research.
Compared to previous studies, this study’s theoretical contribution to urban planning and big data lies in its in-depth bibliometric analysis, which reveals trends and strategies for developing smart cities. It underscores the role of big data in enhancing urban efficiency and equity while also emphasising the imperative for interdisciplinary collaboration and tackling challenges, including data security and technological innovation, to foster the progression of smart cities.

6. Conclusions

This study of the state-of-the-art literature uses bibliometrics and Citespace to visualise and analyse big data and smart city research trends using Citespace. It concentrates on articles published from 2015 to 2023 in the WOS database. It examines publication counts, journals, regions, research scope, authors, institutions, citations, and keywords, revealing progress, hotspots, and strategies in the field.
It was found that (1) this field experienced rapid growth followed by a gradual decline between 2015 and 2023. (2) Research on big data and smart cities demands a high level of technical expertise, with informationisation tools such as the IoT, cloud computing, and AI creating an important construction basis for big data and smart cities. (3) Existing research mainly focuses on technology and its application areas, such as technology integration, energy management, sustainable development, data security, and privacy protection. (4) There is a high degree of overlap in research themes, and changes in research directions are strongly correlated with the context of the times. Meanwhile, the following problems exist: (1) Research overemphasises technical exploration, and smart hardware stacking occurs. (2) We noted the insufficient sharing of disciplinary information, minor differences in research directions, and the problem of “information islands”. (3) Research has largely remained in the concept and design stage, with fewer practical results. (4) There is a lack of long-term development strategies and operation mechanisms.
Our discussion emphasises the importance of big data in developing smart cities, along with suggestions for broadening research and applications. Nonetheless, the study acknowledges the limitations in its data sources, keyword choices, timeframe, analytical tools, and research methods. This study advances research in big data and smart cities and charts a path for future progress. Its innovative visual analytics pinpoint crucial trends and research directions, offering essential guidance to policymakers, urban planners, and academics.
The United Nations recently urged countries to implement policies for the growth of big data and smart cities. The coming era will be pivotal for smart city building, focusing on human-centred design, digital advancements, and sustainability. Enhancing research on spatiotemporal cloud platforms and big data acquisition and prioritising people-serving intelligence is key to addressing technical hurdles.
In the future theorisation and implementation of smart cities, communication across fields should be enhanced, emphasising data integration and exploring external resources to minimise ‘data silos’. In the comprehensive application of smart cities, we should pay more attention to ensuring data security and privacy protection; it is also necessary to actively consider the current context to help the recovery and transformation of cities in the post-pandemic era. Regarding operations and maintenance, relevant departments should strengthen their research on operation and maintenance mechanisms, formulate long-term strategies and policy support to promote smart cities’ sustainable development, and regularly evaluate projects’ effectiveness to optimise practices, ensuring the long-term development of big data and smart cities.

Author Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Y.Z., J.C., and J.Z. The first draft of the manuscript was written by Y.Z. and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Le Fonds de la Recherche Scientifique–FNRS (Fund for Scientific Research) and Wallonie-Bruxelles International (WBI), grant number SUB/2020/482272.

Institutional Review Board Statement

All authors affirm that objectivity and transparency in research has been ensured and ensure that accepted principles of ethical and professional conduct have been followed.

Informed Consent Statement

Not applicable.

Data Availability Statement

Authors do not have the right to share the data. However, they will be made available to the reader upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 2. Knowledge map of collaborative journals publishing on big data and smart cities, 2015–2023.
Figure 2. Knowledge map of collaborative journals publishing on big data and smart cities, 2015–2023.
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Figure 3. Knowledge map of countries cooperating in research on big data and smart cities, 2015–2023.
Figure 3. Knowledge map of countries cooperating in research on big data and smart cities, 2015–2023.
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Figure 4. Knowledge map of cooperative institutions in research on big data and smart cities, 2015–2023.
Figure 4. Knowledge map of cooperative institutions in research on big data and smart cities, 2015–2023.
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Figure 5. Knowledge map of cooperative institutions researching big data and smart cities, 2015–2023.
Figure 5. Knowledge map of cooperative institutions researching big data and smart cities, 2015–2023.
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Figure 6. Co-occurrence network of highly cited articles in the field of big data and smart cities, 2015–2023.
Figure 6. Co-occurrence network of highly cited articles in the field of big data and smart cities, 2015–2023.
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Figure 7. Time-zone view of research subjects, 2015–2023.
Figure 7. Time-zone view of research subjects, 2015–2023.
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Figure 8. Keywords co-occurrence network for big data and smart cities research, 2015–2023.
Figure 8. Keywords co-occurrence network for big data and smart cities research, 2015–2023.
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Figure 10. Co-citation network and clusters of articles in big data and smart cities research, 2015–2023.
Figure 10. Co-citation network and clusters of articles in big data and smart cities research, 2015–2023.
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Figure 11. Annual variations in co-occurring keywords in big data and smart city research papers, 2015–2023.
Figure 11. Annual variations in co-occurring keywords in big data and smart city research papers, 2015–2023.
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Figure 12. Mainstream framework in big data research and smart cities.
Figure 12. Mainstream framework in big data research and smart cities.
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Table 1. Major journals publishing research on big data and smart cities, 2015–2023.
Table 1. Major journals publishing research on big data and smart cities, 2015–2023.
No.Freq.Cited JournalYear
1854IEEE Access2016
2672Future Generation Computer Systems2015
3661Sensors—Basel2015
4646IEEE Internet of Things Journal2015
5619Lecture Notes in Computer Science2015
6575IEEE Communications Magazine2015
7545Sustainable Cites and Society2017
8533Cities2015
9515Sustainability—Basel2017
10417Computer Networks2015
Table 2. Top 10 contributing countries in research on big data and smart cities, 2015–2023.
Table 2. Top 10 contributing countries in research on big data and smart cities, 2015–2023.
No.Freq.CentralityCountryYear
17470.05China2015
24170.05USA2015
32880.16India2015
42570.04Italy2015
52320.08England2015
62060.13Australia2015
71650.05Saudi Arabia2015
81590.11Spain2015
91560.02South Korea2015
101100.09Pakistan2017
Table 3. Top 11 most productive authors in big data and smart cities from 2015 to 2023.
Table 3. Top 11 most productive authors in big data and smart cities from 2015 to 2023.
No.Freq.CentralityAuthorYear
1290.00Simon Elias Bibri2017
2180.00Rashid Mehmood2017
3150.00Luca Foschini2015
4120.00Tan Yigitcanlar2020
5120.00Anand Paul2016
6110.00Jhon Krogstie2017
7110.00Awais Ahmad2016
8110.00Antonio Corradi2015
9110.00Zaheer Allam2019
10100.00M Mazhar Rathore2016
11100.00Iyad Katib2017
Table 4. The top 10 institutions in big data and smart cities from 2015 to 2023.
Table 4. The top 10 institutions in big data and smart cities from 2015 to 2023.
No.Freq.CentralityInstitutionCountryYear
1410.12Chinese Academy of SciencesChina2015
2410.02Norwegian University of Science and TechnologyNorway2017
3410.07King Abdulaziz UniversitySaudi Arabia2016
4340.04Egyptian Knowledge BankEgypt2017
5300.04University of New South Wales SydneyAustralia2019
6300.07Wuhan UniversityChina2015
7290.03University of LondonEngland2015
8290.15King Saud UniversitySaudi Arabia2017
9260.08Deakin UniversityAustralia2015
10250.06Hong Kong Polytechnic UniversityChina2018
Table 5. References with the strongest citation bursts in big data and smart cities, 2015 to 2023.
Table 5. References with the strongest citation bursts in big data and smart cities, 2015 to 2023.
No.YearReferenceCategoryJIFCitations
Average PerTotal
12016Integration of Cloud computing and Internet of Things: A surveyComputer science7.5134.671212
22020Literature review of Industry 4.0 and related technologiesComputer science; engineering8.3158.4792
32019Deep Learning in Mobile and Wireless Networking: A Survey Computer science; telecommunications35.6126.17757
42018Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart citiesConstruction and building technology11.7101.14708
52015Smart tourism: foundations and developmentsBusiness and economics8.570.7707
62017Smart sustainable cities of the future: An extensive interdisciplinary literature reviewConstruction and building technology11.780.63645
72018Deep Multi-View Spatial-Temporal Network for Taxi Demand PredictionComputer science; engineering; telecommunications-90.29632
82016The role of big data in smart cityInformation science and library science2158522
92016Internet of Things and big data Analytics for Smart and Connected CommunitiesComputer science; engineering; telecommunications3.958522
102016Urban planning and building smart cities based on the Internet of Things using big data analytics Computer science; engineering; telecommunications5.653.22479
112018Towards fog-driven loT eHealth: Promises and challenges of loT in medicine and healthcareComputer science7.564448
122018Machine learning for Internet of things data analysis: a surveyTelecommunications7.963.71446
132017Big loT Data Analytics: Architecture, Opportunities, and Open Research ChallengesComputer science; engineering; telecommunications3.955.63445
142018Distributed attack detection scheme using deep learning approach for Internet of ThingsComputer science7.563441
152015The ‘actually existing smart city’Development studies; business and economics; geography4.442.7427
Table 6. List of the most influential categories, 2015 to 2023.
Table 6. List of the most influential categories, 2015 to 2023.
No.Freq.CentralityCategoryYear
18050.13Computer science, information systems2015
27860.16Engineering, electrical, and electronic2015
36120.04Computer Science, theory and methods2015
45400.07Telecommunications2015
53690.10Computer science, AI2015
62910.05Green and sustainable science and technology2015
72510.32Computer science, interdisciplinary applications2015
82090.06Environmental sciences2015
92010.02Environmental studies2015
101780.09Urban studies2015
Table 7. List of keyword co-occurrence, 2015–2023.
Table 7. List of keyword co-occurrence, 2015–2023.
No.Freq.CentralityKeywordsYear
110590.03Big data2015
26300.01Smart city2015
34000.04City 2015
43610.05Internet2015
52880.04IoT2015
61960.02Challenges2016
71910.02Things2016
81810.03Framework2016
91800.05Management2015
101580.01Model2016
111300.00AI2019
121290.02System2015
131280.02Big data analytics2015
141250.02Machine learning2018
151060.05technology2015
161000.01Security2016
17890.01Future2017
18850.03Architecture2016
19830.03Cloud computing2015
20830.03Deep learning2018
Table 9. List of the cited clusters and the number of records contributing to big data and smart cities research, 2015–2023.
Table 9. List of the cited clusters and the number of records contributing to big data and smart cities research, 2015–2023.
Cluster IDSizeCentralityKeywordsYear
0770.805IoT (internet of Things) (113.66, 1.0 × 10−4); cloud computing (53.71, 1.0 × 10−4); edge computing (32.41, 1.0 × 10−4)2017
1760.654Digital transformation (19.39, 1.0 × 10−4); e-government (19.03, 1.0 × 10−4); sustainable city (18.21, 1.0 × 10−4); open data (17.68, 1.0 × 10−4); citizen participation (14.72, 0.001)2019
2530.595Energy consumption (25.02, 1.0 × 10−4); energy efficiency (23.54, 1.0 × 10−4); transfer learning (14.95, 0.001); neural networks (14.67, 0.001); smart grid (13.2, 0.001)2018
3530.734Deep learning (72.28, 1.0 × 10−4); machine learning (70.85, 1.0 × 10−4); intelligent transportation systems (27.21, 1.0 × 10−4); data models (25.15, 1.0 × 10−4); urban computing (21.1, 1.0 × 10−4)2019
4470.688City logistics (11.42, 0.001); differential privacy (10.51, 0.005); green parks (10.51, 0.005); community detection (10.51, 0.005); prediction (9.34, 0.005)2019
5470.555Big data architecture (17.33, 1.0 × 10−4); platformization (17.33, 1.0 × 10−4); electric vehicles (14.44, 0.001); sensor networks (13.9, 0.001); algorithmic governance (11.55, 0.001)2019
6450.708Circular economy (18.92, 1.0 × 10−4); urban metabolism (18.92, 1.0 × 10−4); sustainable development (13.54, 0.001); industry 4 (11.22, 0.001)2019
7380.852Smart card data (42.48, 1.0 × 10−4); data mining (34.17, 1.0 × 10−4); human mobility (33.33, 1.0 × 10−4); travel behavior (22.53, 1.0 × 10−4); mobile phone data (17.51, 1.0 × 10−4)2017
8350.750Smart mobility (19.71, 1.0 × 10−4); sustainable mobility (16.52, 1.0 × 10−4); traffic flow prediction (16.52, 1.0 × 10−4); AI (15.67, 1.0 × 10−4); urban mobility (14.66, 0.001)2019
9330.860Smart sustainable cities (33.79, 1.0 × 10−4); sustainable cities (23.57, 1.0 × 10−4); data-driven (23.45, 1.0 × 10−4); compact cities (23.4, 1.0 × 10−4); eco-cities (21.51, 1.0 × 10−4)2018
10240.829Data storage (14.45, 0.001); electronic heath records (14.03, 0.001); smart (14.03, 0.001); data sharing (14.03, 0.001); medical data (14.03, 0.001)2019
11200.915Social media (23.7, 1.0 × 10−4); operations management (11.52, 0.001); IoT (9.86, 0.005); digitalization (8.19, 0.005); resilience (7.82, 0.01)2017
Table 10. Top 20 keywords with the strongest citation bursts, 2015–2023.
Table 10. Top 20 keywords with the strongest citation bursts, 2015–2023.
KeywordsYearStrengthBeginEnd2015–2023
Networks20153.4420152016Buildings 14 03717 i001
Human mobility20152.8120152019Buildings 14 03717 i002
Business intelligence20152.5720152018Buildings 14 03717 i003
Vision20162.7320162019Buildings 14 03717 i004
Geography20162.7320162019Buildings 14 03717 i005
Smart card data20173.3520172020Buildings 14 03717 i006
Cloud20153.3220172018Buildings 14 03717 i007
Fog computing20173.220172018Buildings 14 03717 i008
Smart sustainable cities20172.7120172020Buildings 14 03717 i009
Things20162.7120172018Buildings 14 03717 i010
Data management20172.6920172018Buildings 14 03717 i011
Wireless sensor networks20172.5020172018Buildings 14 03717 i012
Urban forms20183.0420182019Buildings 14 03717 i013
Typology20183.0320182020Buildings 14 03717 i014
Issue20182.4220182020Buildings 14 03717 i015
Public participation20192.5920192020Buildings 14 03717 i016
Intelligent sensors20203.2120202021Buildings 14 03717 i017
Urban informatics20202.9220202021Buildings 14 03717 i018
Traffic flow prediction20212.6920212023Buildings 14 03717 i019
Industry 4.020212.6920212023Buildings 14 03717 i020
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Zhuang, Y.; Cenci, J.; Zhang, J. Review of Big Data Implementation and Expectations in Smart Cities. Buildings 2024, 14, 3717. https://doi.org/10.3390/buildings14123717

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Zhuang Y, Cenci J, Zhang J. Review of Big Data Implementation and Expectations in Smart Cities. Buildings. 2024; 14(12):3717. https://doi.org/10.3390/buildings14123717

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Zhuang, Yingnan, Jeremy Cenci, and Jiazhen Zhang. 2024. "Review of Big Data Implementation and Expectations in Smart Cities" Buildings 14, no. 12: 3717. https://doi.org/10.3390/buildings14123717

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Zhuang, Y., Cenci, J., & Zhang, J. (2024). Review of Big Data Implementation and Expectations in Smart Cities. Buildings, 14(12), 3717. https://doi.org/10.3390/buildings14123717

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