Smart City: A Bibliometric Analysis of Conceptual Dimensions and Areas

Abstract

In recent years, cities are expected to develop in line with the smart city (S.C.) idea. Cities, perceived as attractive places for people to live, must now meet many conditions. A city’s duty is, among others, to meet the needs and expectations of both, its residents and newcomers, offering a wide range of opportunities for entrepreneurs, caring for the natural environment, constantly taking on new challenges, and meeting them for its further development. Thus, the management of a city involves undertaking activities in line with the smart city idea. However, to implement them, they use widely available means, defined as intelligent solutions, e.g., modern ICT technologies. In line with the smart city idea, a city is a center inhabited by people who create a civic and participatory society, having the ability to use the latest ICT solutions and having access to a range of services and solutions offered by the city. Since the smart city idea is considered at many levels, it is still unknown what dimensions or areas related to this concept are the most valuable and stimulating for its implementation. Therefore, the purpose of this article is to study what dimensions or areas related to this idea are the most valuable and stimulating for its implementation. The applied methodology is based on a critical analysis of the available literature in the form of bibliometric and citation analysis and analysis of the keyword characteristics of S.C. co-occurrences. The result of the research is a set of keywords that enable identifying the key dimensions and main areas of the implementation of S.C.

Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

“…Concerning the first research question (i.e., the state-ofthe-art of the context-aware applications supported by smart cities' infrastructures), we may conclude that there is a current interest (i.e., 74% of the included studies were published since 2017) to develop applications to support public health, tourism experience, urban mobility, active citizenship, shopping experience, management of urban infrastructures, public alerts, recommenders, and smart environments. These purposes are also present in current smart city research, since urban mobility, healthcare, public safety, social, economic and environmental dimensions of sustainability, services' efficiency and governance, namely participatory governance involving the citizens, are important topics of the smart city research agenda [10,19,20]. In fact, except smart people, all the remainder smart city domains defined by Giffinger et al [5,6] (i.e., smart economy, smart governance, smart mobility, smart environment, and smart living) are present in the included studies.…”

“…However, a limitation can be foreseen in terms of only using the expression 'smart city' , since other expressions (e.g., 'digital city' or 'intelligent city') are also used in the scientific literature. Even so, considering bibliometric analysis (e.g., [10,20]) or literature reviews related to the definitions of smart cities (e.g., [8]) the expression 'smart city' is the most used one. Therefore, in terms of academic research, the small number of included studies might indicate that the research effort to take advantage of the smart cities infrastructures to implement concrete context-aware applications is not very representative within smart cities' research.…”

“…Given the range of topics covered under the smart city concept, it has become a challenge to find an adequate organizing taxonomy [1,8]. Several studies have focused on the various dimensions of smart cities: (1) smart economy (competitiveness), smart people (social and human capital), smart governance (citizens' participation), smart mobility (transport), smart environment (natural resources) and smart living (quality of life) [5,6]; (2) urban openness, service innovation, partnerships formation, urban proactiveness, smart city infrastructure integration, smart city governance [9,10]; (3) technology, people (creativity, diversity, and education), and institutions (governance and policy) [11]; (4) management and organizations, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment [12]; (5) architecture, governance, planning and management, facilities, data and knowledge, services, people, and environment [13]; (6) technology, organization, collaboration, and experiment [14]; (7) economy, people, governance, environment, living, mobility and data [15]; and (8) smart business, smart living, smart education, smart citizen, smart government, smart infrastructure, smart utility, smart mobility, and smart environment [16]. In turn, the IEEE smart cities initiative [17,18] proposed the smart water, smart energy, smart mobility, smart food and agriculture, smart health, and smart waste domains.…”

“…Several articles published in scientific journals [1, 3, 10, 25-28, 32, 33, 35-56] reported different types of reviews, including systematic literature reviews, related to various aspects of smart cities' implementation: (1) bibliometrics (e.g., bibliometric analysis of the literature of the first decades of research on smart cities [20,39] or identification of the conceptual dimensions and areas of smart cities based on bibliometric analysis [10]); (2) international standards (e.g., identification of relevant smart cities standards [48] or review of the standardisation activities that are being carried out by the United for Smart Sustainable Cities initiative [44]); (3) data analytics (e.g., the various data learning techniques being applied in smart cities [41] or big data applications to support urban environment, society, and sustainability [46]); (4) systems architectures (e.g., the contribution of the enterprise architecture towards digital transformation in smart cities [49]); (5) data security (e.g., identification of the barriers and hurdles in smart city and how these hurdles might be mitigated by the blockchain technology [45]); (6) IoT and data analytics (e.g., identification of key areas of application, current trends, data architectures, and ongoing challenges of the application of IoT and data analytics in smart cities [50]); (7) ontologies (e.g., how ontologies are being used to support smart city services [51]); (8) healthcare (e.g., relevant smart cities applications with impact in the provision of healthcare [42]); (9) energy efficiency (e.g., identification of the barriers to the progression to sustainable smart cities [52]), (10) citizenship (e.g., how public displays may foster citizen participation in addressing urban issues [23]); (11) smart city indicators (e.g., identification of potential indicators for implementing smart cities [22] or provision of a critical analysis of smart city assessment tools and their potential contribution to the evolution of the smart city movement [15]); (12) mobility (e.g., systematization of enabling technologies, current trends and solutions to support smart mobility [53] or smart city applications to facilitate the mobility of older adults in urban spaces [55]); and ...…”

Patterns of the Distribution of the Demand of End-Consumers among Retailers in the Zone of their Residence

“…In this regard, analysis of the purchase options has the following main goals and objectives: determining the type of competitive market (Tkachenko, et al, 2020); identification of actual and potential competitors, determination of the number, type and size of competing firms (Koval, et al, 2020); calculation of market share occupied by competitors; characteristics of the intensity and direction of competition (Balandina, et al, 2018); a management concept of a cities (Bajdor and Starostka-Patyk, 2021); a modeling framework to forecast urban goods flows (Comi, 2020); identifying the strengths and weaknesses, strategies and evaluating the competitiveness of their competitors' products (Fesenko, et al, 2018); analysis and forecasting of competitor's behavior in the market and his reaction to various marketing actions (Halkin, et al, 2019); and agent modeling of online store activities (Selivanova, et al, 2020). Certain parameters of existing models and methods do not provide a systematic approach to determining retailer visiting probability vs e-commerce in current market or trade zone.…”