Special Issue "Smart Cities and Data-driven Innovative Solutions"

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Smart Data".

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Prof. Dr. Seunghwan Myeong
E-Mail Website
Guest Editor
Department of Public Administration, Inha University, Inharo 100, Nam-gu, Incheon 402-751, Korea
Interests: electronic government; smart governance; smart cities; information management in public organizations; social capital; government innovation and project management; public cloud and security; industrial security; blockchain and AI in the public sector
Special Issues and Collections in MDPI journals
Dr. Younhee Kim
E-Mail Website
Guest Editor
School of Public Affairs, Pennsylvania State University Harrisburg, Middletown, PA, USA
Interests: smart cities; e-governance; public management; performance measurement; public entrepreneurship; citizen participation; information technology policy; research methodology
Special Issues and Collections in MDPI journals
Dr. Michale J. Ahn
E-Mail Website
Guest Editor
Department of Public Policy and Public Affairs, University of Massachusetts Boston, Boston, MA, USA
Interests: electronic government; smart cities; AI in the public sector; technology innovations; social media; public management; public administration education; quantitative and qualitative research methodology
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Innovations in information and communication technologies have made cities smarter at being able to run their systems more efficiently and effectively, connecting them with their residents and communities. The word “smart” is interchangeable with virtual, intelligent, digital, or ubiquitous, to connect a wide range of city- and citizen-related issues based on data-driven technologies. Physical infrastructures and big data analytics have led to smart city projects, however the development of smart cities should also be justified in order to explaine how local govenments envision smart city initiatives in management, organization, governane, policy, technology, and environment.  

This Special Issue is devoted to analytical, descriptive, and explanatory research that broadens the courses of action and practice for the development of smart cities, and that closes the gap in the literature about smart cities spanning across multidisciplinary areas. This Special Issue focuses on policy initiatives, case studies, empirical evidence, and novel insights to promote data-driven innovations for building smart cities in a national, comparative, or international context.

Prof. Seunghwan Myeong
Dr. Younhee Kim
Dr. Michale J. Ahn
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Best practices of the smart city development
  • Big data collection for smart cities
  • Blockchain and AI applications for smart governance
  • Internet of Things, clouds, and mobile networks for smart cities
  • Policy initiatives for smart cities
  • Privacy and security in building smart cities
  • Smart administraion and management innovation in smart city governance
  • Smart city partnerships
  • Sustainable strategies and approaches for smart cities
  • Technological and nontechnological dimensions of smart cities
  • Urban planning for smart cities

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Smart Data-Driven Policy on Unmanned Aircraft Systems (UAS): Analysis of Drone Users in U.S. Cities
Smart Cities 2021, 4(1), 78-92; https://doi.org/10.3390/smartcities4010005 - 07 Jan 2021
Viewed by 748
Abstract
Realizing the benefits of drones while minimizing public concerns requires development and implementation of drone use policies that are grounded in an understanding of drone users and their behavior. This study aims to contribute to data-driven smart cities by filling our gap in [...] Read more.
Realizing the benefits of drones while minimizing public concerns requires development and implementation of drone use policies that are grounded in an understanding of drone users and their behavior. This study aims to contribute to data-driven smart cities by filling our gap in knowledge about city drone users and their compliance behavior. The literature review has identified the main factors affecting drone policy compliance. This study collects data via a national survey of adults on drone behavior and focuses on city drone users. The results show that city drone users are younger with more dispersed educational backgrounds and income distribution than those in the general population. Moreover, civic duty, trust in government, and knowledge about regulatory requirements are motivators for drone users to comply with drone regulation. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Show Figures

Graphical abstract

Article
Smart City Strategies—Technology Push or Culture Pull? A Case Study Exploration of Gimpo and Namyangju, South Korea
Smart Cities 2021, 4(1), 41-53; https://doi.org/10.3390/smartcities4010003 - 24 Dec 2020
Cited by 4 | Viewed by 1710
Abstract
This study aims to address strategies, models, and the motivation behind smart cities by analyzing two smart city project cases in medium-sized cities, i.e., Gimpo and Namyangju in South Korea. The case of Smartopia Gimpo represents a top-down, infrastructure-focused smart city innovation that [...] Read more.
This study aims to address strategies, models, and the motivation behind smart cities by analyzing two smart city project cases in medium-sized cities, i.e., Gimpo and Namyangju in South Korea. The case of Smartopia Gimpo represents a top-down, infrastructure-focused smart city innovation that invested in building state-of-the-art big data infrastructure for crime prevention, traffic alleviation, environmental preservation, and disaster management. On the other hand, Namyangju 4.0 represents a strategy focused on internal process innovation through extensive employee training and education regarding smart city concepts and emphasizing data-driven (rather than infrastructure-driven) policy decision making. This study explores two smart city strategies and how they resulted in distinctively different outcomes. We found that instilling a culture of innovation through the training of government managers and frontline workers is a critical component in achieving a holistic and sustainable smart city transformation that can survive leadership changes. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Show Figures

Figure 1

Article
Shortening the Last Mile in Urban Areas: Optimizing a Smart Logistics Concept for E-Grocery Operations
Smart Cities 2020, 3(3), 585-603; https://doi.org/10.3390/smartcities3030031 - 02 Jul 2020
Cited by 3 | Viewed by 1849
Abstract
Urbanization, the corresponding road traffic, and increasing e-grocery markets require efficient and at the same time eco-friendly transport solutions. In contrast to traditional food procurement at local grocery stores, e-grocery, i.e., online ordered goods, are transported directly to end customers. We develop and [...] Read more.
Urbanization, the corresponding road traffic, and increasing e-grocery markets require efficient and at the same time eco-friendly transport solutions. In contrast to traditional food procurement at local grocery stores, e-grocery, i.e., online ordered goods, are transported directly to end customers. We develop and discuss an optimization approach to assist the planning of e-grocery deliveries in smart cities introducing a new last mile concept for the urban food supply chain. To supply city dwellers with their ordered products, a network of refrigerated grocery lockers is optimized to temporarily store the corresponding goods within urban areas. Customers either collect their orders by themselves or the products are delivered with electric cargo bicycles (ECBs). We propose a multi-echelon optimization model that minimizes the overall costs while consecutively determining optimal grocery locker locations, van routes from a depot to opened lockers, and ECB routes from lockers to customers. With our approach, we present an advanced concept for grocery deliveries in urban areas to shorten last mile distances, enhancing sustainable transportation by avoiding road traffic and emissions. Therefore, the concept is described as a smart transport system. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Show Figures

Figure 1

Article
RSSI-Based for Device-Free Localization Using Deep Learning Technique
Smart Cities 2020, 3(2), 444-455; https://doi.org/10.3390/smartcities3020024 - 01 Jun 2020
Viewed by 1097
Abstract
Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and [...] Read more.
Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Show Figures

Graphical abstract

Article
A Conflict-Detecting and Early-Warning System for Multi-Plan Integration in Small Cities and Towns Based on Cloud Service Platform
Smart Cities 2019, 2(3), 388-401; https://doi.org/10.3390/smartcities2030024 - 01 Aug 2019
Cited by 1 | Viewed by 1211
Abstract
Multi-plan integration (MPI) is a major effort initiated by China’s State Council for the purpose of streamlining development plans made by various public agencies in provincial and city governments. Small cities and towns are facing challenges to achieve MPI goals due to lack [...] Read more.
Multi-plan integration (MPI) is a major effort initiated by China’s State Council for the purpose of streamlining development plans made by various public agencies in provincial and city governments. Small cities and towns are facing challenges to achieve MPI goals due to lack of technological infrastructure and professional expertise. This article presents a system to assist small cities and towns to carry out their MPI tasks. The system, named conflict-detecting and early-warning for MPI (CDEW4MPI) is developed based on a cloud service platform. CDEW4MPI enables small cities and towns in remote locations to detect inconsistency and conflicts among multiple plans. The system includes two modules. One is conflict-detecting, which identifies spatial conflicts in boundary designation among different plans from different agencies. The other is early-warning, which analyzes and reports potential encroachment of proposed local plans to urban growth boundary, the baseline for ecological protection, and the farmland under permanent preservation. CDEW4MPI was implemented as a demo project in Shennongjia Forestry District, a municipality in the western mountainous region of Hubei Province, China. The paper presents the design of CDEW4MPI and its implementation in Shennongjia and draws lessons from the Shennongjia case for broad interests in smart management of spatial resources. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Show Figures

Figure 1

Article
The Sciences Underlying Smart Sustainable Urbanism: Unprecedented Paradigmatic and Scholarly Shifts in Light of Big Data Science and Analytics
Smart Cities 2019, 2(2), 179-213; https://doi.org/10.3390/smartcities2020013 - 23 May 2019
Cited by 15 | Viewed by 1688
Abstract
As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—which is manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, catalyzing major shifts, and fostering [...] Read more.
As a new area of science and technology (S&T), big data science and analytics embodies an unprecedentedly transformative power—which is manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are understood, studied, planned, operated, and managed to improve and maintain sustainability in the face of expanding urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach that is based on a computational understanding of city systems that reduces urban life to logical and algorithmic rules and procedures, as well as employs a new scientific method based on data-intensive science, while also harnessing urban big data to provide a more holistic and integrated view and synoptic intelligence of the city. This paper examines the unprecedented paradigmatic and scholarly shifts that the sciences underlying smart sustainable urbanism are undergoing in light of big data science and analytics and the underlying enabling technologies, as well as discusses how these shifts intertwine with and affect one another in the context of sustainability. I argue that data-intensive science, as a new epistemological shift, is fundamentally changing the scientific and practical foundations of urban sustainability. In specific terms, the new urban science—as underpinned by sustainability science and urban sustainability—is increasingly making cities more sustainable, resilient, efficient, and livable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, management, planning, design, and development. Full article
(This article belongs to the Special Issue Smart Cities and Data-driven Innovative Solutions)
Show Figures

Figure 1

Back to TopTop