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Surveys of urban history from ancient times to the present reveal a continuum of collective responses to pandemics ranging from quarantine facilities and monitoring the spread of disease to building new wastewater networks. The contemporary COVID-19 pandemic includes new digital tools and techniques that supplement (and sometimes replace) the existing analogue responses, while raising new ethical issues with respect to privacy. A typology of pandemic responses in cities is created, based on human–building interaction (HBI) principles. This typology can be used to compare and contrast analogue and digital responses relating to distancing, monitoring and sanitising. It provides a summary of a wide range of individual and collective implications of pandemics and demonstrates the indelible connections between pandemics and the built environment. In addition, the typology provides a tool to interpret some of the opportunities and drawbacks of digitalising cities. PRACTICE RELEVANCE The COVID-19 pandemic has demonstrated the enduring co-evolution of cities and disease through history. This study aims to inform future pandemic preparedness by providing a framework for designers, managers and users of public spaces to evaluate the multiple implications of emerging technologies that are integrated within the urban fabric. While the rapid rise of digitalisation to advance urban health agendas continues to raise new questions relating to individual and civic freedoms, HBI qualitatively provides a lens through which to examine the overlapping spatial, ethical, and temporal consequences for humans and the built environment. Urban planning researchers and designers can use HBI principles to humanise the sustainable smart city. © 2023 The Author(s).
ABSTRACT
The chapter examines the role and potential inherent in surveillance systems in smart cities today. The Covid-19 pandemic and the resultant restrictions to mobility, on the one hand, and the need for strengthened enforcement measures highlighted the already existing weaknesses and contingencies besetting surveillance in smart cities. The chapter makes a case that the adoption of smart city surveillance and infrastructure management systems may contribute to the improvement of safety and security in the smart city as well as to an overall enhancement of the smart city's resilience. The discussion in this chapter focuses on the complex processes of data acquisition, data sharing, and data utilization to explain in which ways they all add to smart surveillance systems that-while aware of individual freedoms and privacy issues-contribute to the process of making a smart city resilient. To showcase the applicability of these findings, a wireless mesh network (WMN) surveillance system is presented. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
PurposeCitizens often perceive surveillance by government authorities as oppressive and, hence, demonstrate reluctance in value co-creation from such services. This study aims to investigate the challenges and benefits of citizen empowerment through technology-driven surveillance or "smart surveillance.”Design/methodology/approachGuided by Dynamic Capability theory, the authors conduct in-depth interviews with officers in-charge of surveillance in smart cities. Given the contemporary advancements, this approach allows a retrospective and real-time understanding of interviewees' experiences with smart surveillance.FindingsThe authors develop five propositions for citizen empowerment through smart surveillance to summarize the findings of this study.Research limitations/implicationsThis study advances the relevance of Dynamic Capability in public administration.Practical implicationsSmart city authorities and policymakers may leverage the insights provided in this study to design appropriate policies for smart surveillance.Originality/valueThe authors find that factors such as digital technology and infrastructure, information management, skill divide and perceived return on investment may influence citizen empowerment through smart surveillance.
ABSTRACT
The integration of AI and the IoT in education has the potential to revolutionize the way we learn. Personalized learning, real-time feedback and support, and immersive learning experiences are some of the benefits that AI and the IoT can bring to the education system. In this regard, this research paper aims to investigate how AI and the IoT can be integrated into sustainable education in order to provide students with personalized and immersive learning experiences during pandemics, such as COVID-19, for smart cities. The study's key findings report that AI can be employed in sustainable education through personalized learning. AI-powered algorithms can be used to analyze student data and create personalized learning experiences for each student. This includes providing students with tailored content, assessments, and feedback that align with their unique learning style and pace. Additionally, AI can be used to communicate with students in a more natural and human-like way, making the learning experience more engaging and interactive. Another key aspect of the integration of AI and the IoT in education obtained from this research is the ability to provide real-time feedback and support. IoT-enabled devices, such as smart cameras and microphones, can be used to monitor student engagement and provide real-time feedback. AI algorithms can then use these data to adapt the learning experience in real time. IoT-enabled devices, such as tablets and laptops, can be used to collect and process student work, allowing for the automatic grading of assignments and assessments. Additionally, IoT technology can facilitate remote monitoring and grading of student work, which would be particularly useful for students who cannot attend traditional classroom settings. Furthermore, AI and the IoT can also be used to create intelligent personal learning environments (PLEs) that provide students with personalized, adaptive, and engaging learning experiences. IoT-enabled devices, such as smart cameras and microphones, combined with AI-powered algorithms, can provide real-time feedback and support, allowing the PLE to adapt to the student's needs and preferences. It is concluded that integrating AI and the IoT in sustainable education can revolutionize the way people learn, providing students with personalized, real-time feedback and support and opening up new opportunities for remote and disadvantaged students. However, it will be important to ensure that the use of AI and the IoT in education is ethical and responsible to ensure that all students have equal access to the benefits of these technologies.
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Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.
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The study explores the enabling factors of resilience as a key lever to address complexity, challenge technological evolution and foster growth in urban contexts. The goal is to explore the determinants of resilience to understand how smart cities can seize opportunities for innovation starting from disruptive event through the right combination of technology, human and social capital. Resilient smart cities can challenge environmental changes and develop proactive behaviors that encourage the attainment of social, economic and environmental well-being. Therefore, being resilient entails the redefinition of orientation, business models, technology and resource allocation to turn a crisis (such as Covid-19 pandemic) into an opportunity for development. Given the need to clarify the key determinants of resilience in contemporary cities, this article aims at exploring: (1) the main drivers for resilience to challenge a crisis;(2) how the drivers o resilience can be activated and combined to create opportunities for innovation. The empirical research is based on a content analysis that explores the case study of the Italian smart city of Turin. The results allow the identification of the determinants of resilience according to an exploratory approach, in which the smart projects and the set of technologies, resources and institutions exchanged and co-developed by Turin smart city are classified to obtain some enabling dimensions for resilience. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Research trust in e-government has increased through the decades. Trust in e-government is an important instrument for citizens who intend to use digital services. The aim of this research is to measure and provide an overview of how the level of people trust Jakarta Kini super-application as an e-Government in DKI Jakarta. This study uses a factor score analysis with the Mplus program and OECD method to characterize the trust level of the valid instruments SRMR, CFI, and TLI values. The pre-test results on the validity and reliability of Confirmatory Factor Analysis (CFA) with 1687 respondents. The results of this study indicate that the level of trust of the people in DKI Jakarta is in the medium to high category. It was found based on the factor score value in accordance with the average level of trust in the DKI Jakarta Provincial Government, which was 7.5 out of 10, with a scale of 1 indicating no trust at all, to a scale of 10 indicating full trust from the entire population. This level belongs to the medium to full trust level category. The lesson learned is that the government must take action to increase trust, such as identifying factors affecting the public use of the Jakarta Kini super-application. This study provides a discussion of the findings, and limitations and further proposes potential directions for future research.
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With ninety percent of COVID-19 reported cases from urban areas, the urban world became an epicenter of the pandemic. A technology-driven approach was followed by Chinese cities had managed to keep the transmission in control. However, in Western countries, a human-driven approach was followed to combat the pandemic. The cities of global south were relatively more challenged in terms of technology as well as human-driven approaches. Likewise Indian cities too faced additional problems like inadequate infrastructure facilities. The pandemic aftermath did lay bare the disparities among different population groups, particularly the vulnerable ones who had been dually hit by low income as well as weak social and economic coverage. Though a multitude of research has been done on the general impact of pandemic, a limited number of research outpourings were observed on the impact of pandemic on the ‘third space'. As a critical component of social distancing all the ‘communal hangout spots' like park, local markets and gyms were forced to shut down during a pandemic which critically altered the social infrastructure. The study aimed to understand of the impact of pandemics on urban life, with a focus on the ‘third spaces', in the selected Indian cities. The methodology was integrated research review which included research output from popularly used database of scientific articles as well as government reports, documents, etc. This chapter questions how these spaces are perceived and how the perception changed during the outbreak. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools.
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The novel coronavirus that triggered the COVID-19 outburst is still active around the globe. By now, COVID-19 has affected practically every facet of progress, most importantly, it has shaken the healthcare system like never before. At its peak, it forced Governments throughout the world into lockdowns to limit the reach of the epidemic. Based on early advisories of the World Health Organization (WHO), the only method of safeguarding oneself from being infected was to wear a face mask. Even today, with fewer cases being reported, masking oneself remains the single most effective and cheap means of prevention. As urban areas continue to grow, effective city management is essential for mitigating the increase of the deadly COVID-19 disease. The success of smart cities depends on significant upgrades to public transportation, highways, companies, homes, and municipal streets. There is room for improvement in the public bus transportation system now in place, and one of those improvements would be to use artificial intelligence. To determine if the person is wearing a face mask, you need an autonomous mask detection and alert system. Therefore, this study introduced a deep learning-based design that combines the attention-based generative adversarial network (ABGAN) with the multi-objective interactive honeybee mating optimization (MOIHBMO) approach to create an automated face mask recognition system. A set of 1386 images has been used to create a real-time dataset. This database contains 690 pictures without face masks and 686 images with them. The suggested algorithm ABGAN-MOIHBMO is compared to other traditional methods for detection of face masks, such as DL, AI, and DNN. The performance indicators used are error rate, inference speed, precision, recall, accuracy, and over fitting assessments. The results demonstrate that the proposed ABGAN-MOIHBMO outperforms the existing methodologies. It provides 96% of precision, 86% of recall, 93% for the f1 score, which are higher/better than the other, traditional methods. The error rate in ABGAN-MOIHBMO is a low 1.1%, which is lower other approaches. To predict and underline the significance of face mask use, the face mask detection technique may be employed in the future at Saudi airports, shopping centers, and other congested locations. On a larger platform, our research will be an effective instrument in helping many nations throughout the globe combat the rapid spread of this contagious illness. © 2023 NSP Natural Sciences Publishing Cor.
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Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.
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There is a proliferation of digitalisation of urban and health services in India under the Smart City and Digital Health missions, respectively. This study brings digital and feminist geographies together to understand the role of technologies in urban areas, particularly in health service delivery and how healthcare workers mediate these health platforms. Using a case study of Varanasi city in Uttar Pradesh, India this study documents whether-and to what extent-digital technologies and services enable citizens and service providers to access and improve their lived experiences. The findings indicate a top-down, innovation-focussed model is adopted which excludes and alienates different user groups and citizens shaping their interaction and access to these services.
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Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always following a rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is the expression of a continuous updating of emerging topics concerning relationships among urban planning, mobility and environment, through a collection of short scientific papers. The Review Notes are made of five parts. Each section examines a specific aspect of the broader information storage within the main interests of TeMA Journal.This section of the Review Notes deals with the new frontiers of urban development through the lenses of the European program NextGenerationEU.In particular, this contribution deepens the topic of digitalization in urban areas within the framework of the Italian National Recovery and Resilience Plan. It provides an overview of the proposed reforms, strategies and interventions to boost the digital economy and digitalize public services within the urban context.
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significant recommender systems (RS) development has occurred along with the Internet of Things (IoT) development in recent years. Recommender systems have been widely spread across diverse fields, including environmental preservation, e-commerce, healthcare, social and governance systems. There has been a growing focus on e-government as part of smart city initiatives in today's world of connected devices and infrastructure, especially after the COVID-19 pandemic. With the use of information and communication technologies (ICTs), the government can enhance the delivery of public services, increase transparency, accountability, and credibility, as well as engage citizens in the decision-making process. To facilitate 'smart' governance, one of a smart city initiative's objectives is integrating e-government into the city's governance framework. The lack of personalized services for particular stakeholders is one of the most significant limitations of e-governance. There are a number of open challenges coupled with interesting opportunities, making this a very promising and exciting area for research to shape recommendation systems for urban environments. Considering the overwhelming amount of information, services, and tasks available through smart government applications, it is a greater chance of providing personalized recommendations for different stakeholders and tasks within multi-faceted and multi-dimension. There is still a lot of research to be done on recommendation systems in the context of smart cities or smart government. This paper survey the existing studies on recommendation systems for smart governance. The study aims to address smart city challenges to considered when designing and implementing recommendations for e-governance and the target stakeholder's interests. © 2022 IEEE.
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The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.
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The "Smart City" concept and "smart digitalization" represent implementation of information and communication technologies in local government units. This is a new approach to local governance in managing various local government services and delivery of goods. Local government represents a form of political and administrative territorial organization, with specific local tasks and services regarding the local community. It has a separate jurisdiction and specific autonomy and functions mostly independently of the central government administration. Different administrative and political systems have different models of local government organization. The position of local government units depends on the degree of centralization present in the political system. Local government organization and public authorities are focused on delivery of goods and maintaining various public services for the local community, and their services usually have a local character. Their radius of influence is territorially limited in local units and social communities connected with these units. Services and tasks provided from local government units are specific because they usually influence daily life and quality of living in the local community. The concept of "Smart City" and implementation of "smart digitalization" in managing local public tasks and delivery of local services and goods can improve local governance and help in establishing an efficient model of local government administration. In this paper a comparative and deductive approach is used to explain main elements of the "Smart City" concept and their application to local government tasks and obligations. Second, it uses a synthetic approach to explore how implementation of "smart digitalization" and the "Smart City" concept can be used as an efficient tool for social, economic, and political challenges in the post-Covid era.
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Current awareness of epidemic threats and critical experiences of the COVID-19 pandemic require extension of the management model in the smart city, especially in the field of mobility and transport services, with monitoring of epidemic hazards. This paper addresses the issue of epidemic hazards, a new challenge in smart cities, and customer delivery services. The novel DHI methodology for epidemic hazards assessment is presented and applied to compare customer delivery services in aspects of SARS-CoV-2 epidemic hazards. The case studies presented a detailed analysis of epidemic hazards on the basis of process algorithms and dedicated quantitative scales to assess factors influencing the mechanisms of virus transmission. The developed DHI methodology and the results obtained for transport services constitute important cognitive knowledge for the administrative personnel in smart city.
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In order to create a sustainable future for the urban environment in s=Smart cities, it is necessary to develop a concept of urban transport, partially reduce the use of traditional transport, primarily cars, as well as the environmental pressure on society, which is essential to move to a sustainable urban future. In the latest discussions on the future of the urban transport system, the quality of the environment, and the possibility of its improvement are discussed, this issue became especially relevant with the onset of the pandemic, when the lockdowns were introduced. The problem of sustainable transport in urban areas has been recognized in academic studies, searching for appropriate models and solutions. The article presents the latest literature review and illustrates the newest trends with several examples. VOS Viewer software has been used to classify the different keywords, according to their co-citation, following clustering techniques. By analyzing the research conducted by other researchers, it has been possible to structure the ecosystem and trends in the Urban Transportation Concept, also mentioning likely future trends. Based on the literature analysis of the Sustainable Urban Transport, the authors of the study found that a large group of researchers deal with technical solutions and innovative business models, while the essential behavioral aspects are examined in less detail. Extensive literature analysis allowed the authors to select several solutions to achieve the transformation towards sustainable transportation in urban areas: new vehicle technologies and their environmental factors' analysis, geographic information systems, the analytic hierarchy process method, the time series analysis of road traffic accidents using multiplicative models, electrification and use of Friedman Analysis of Variance by Ranks, as well as innovations in sharing mobility.
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Understanding the residents’routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities. IEEE