Special Issue "Smart Sustainable Cities in the Era of Big Data"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 15 November 2021.

Special Issue Editor

Dr. Simon Elias Bibri
E-Mail Website
Guest Editor
Department of Computer and Information Science, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Interests: data-driven smart sustainable cities; sustainable cities (e.g., compact city, eco-city, green city, zero-carbon city, symbiotic city); smart cities (e.g., real-time city, data-driven city, ambient city, ubiquitous city, sentient city); ICT of ubiquitous computing (e.g., ambient intelligence, the IoT, sentient computing); urban green computing; urban artificial intelligence; big data science and analytics; sustainability transitions and socio-technical shifts; science and technology studies (STS); circular economy; technology and environmental policies
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Special Issue Information

Dear Colleagues,

This Special Issue focuses on data-driven smart sustainable cities. This is a new paradigm of urbanism that has recently materialized in the light of the big data revolution, or what has been termed as the fourth paradigm of science as enabled by big data science and analytics. This new area of science and technology (S&T) embodies an unprecedentedly transformative power—which is manifested not only in the form of revolutionizing science and transforming knowledge, but also in enhancing social practices, catalyzing major shifts, creating powerful discourses, and fostering societal transitions. Of particular relevance is instigating the massive changes in the way both sustainable cities and smart cities are studied, understood, planned, designed, controlled, managed, and governed in the face of the escalating urbanization trend. This relates to what has been dubbed data-driven smart sustainable urbanism, a new era wherein sustainable urbanism and smart urbanism processes and practices are becoming highly responsive to a form of data-driven urbanism. At the core of data-driven urbanism is a computational understanding of city systems and domains that reduces urban life to algorithmic and calculative procedures and thus transforms it into a haze of software instructions. This is informed by urban science—a field in which big data science and analytics is practiced, which is increasingly making both smart cities and sustainable cities more sustainable, resilient, efficient, equitable, and livable by rendering them more measurable, knowable, and tractable in terms of their operational functioning, planning, and development. The ultimate aim is to find more effective ways to improve, advance, and maintain the contribution of both smart cities and sustainable cities to the goals of sustainability.

Data-driven smart sustainable cities tend to take several forms in terms of combining the strengths of sustainable cities and smart cities and harnessing the synergy between their strategies and solutions based on how this combination and synergy can be conceptualized and operationalized. As a corollary of this, there is a host of unexplored opportunities to mitigate or overcome the extreme fragmentation of and the weak connection between sustainable cities and smart cities at the technical and policy levels thanks to the fast-flowing torrent of urban data. The vast deluge of contextual and actionable data being generated daily with its new and extensive sources hides the answers to the most challenging analytical questions, as well as the solutions to the most complex challenges pertaining to sustainability. It also provides raw ingredients to build tomorrow’s human engineered systems and plays a key role in understanding urban constituents as data agents. Indeed, numerous opportunities have recently been explored and could be realized in the ambit of data-driven smart sustainable cities.

This Special Issue of Sustainability aims to offer a platform for advancing sustainable cities and smart cities in terms of sustainability, and more importantly for integrating their strategies and solutions within the framework of smart sustainable cities based on data-driven technologies and solutions. The basic idea of data-driven smart sustainable cities as a holistic approach to urbanism is to explicitly bring together sustainable cities and smart cities as urban endeavors in order to build smart sustainable cities in ways that continuously assess, optimize, and enhance their performance with respect to the three dimensions of sustainability as well as their synergistic and balanced integration over the long run.

We encourage researchers, practitioners and scientists to submit original research articles, case studies, reviews, critical perspectives, and viewpoint articles on topics including but not limited to:

  • Smart cities and sustainability;
  • Data-driven urbanism and sustainable development;
  • Sustainable cities and smartness;
  • Urban sustainability and big data technology;
  • Data-driven smart solutions for environmental, economic, and/or social sustainability;
  • Data-driven smart solutions for resilient cities;
  • Environmental, economic, social, and institutional dimensions of smart sustainable cities;
  • Data-driven scientific urbanism for sustainability;
  • Practical examples and best practice insights in emerging data-driven smart sustainable urbanism;
  • IoT- and AI-enabled smart sustainable cities;
  • Socially responsible AI in smart cities and sustainable cities;
  • Data-driven smart urban planning and design;
  • Urban intelligence functions for sustainability;
  • Data-driven smart urban metabolism models;
  • Smart sustainable transport systems, energy systems, and waste systems;
  • Smart public safety and smart healthcare;
  • Opportunities for and challenges of promoting data-driven smart sustainable cities;
  • Physical, infrastructural, social, and institutional transformations needed for promoting data-driven smart sustainable cities.

Dr. Simon Elias Bibri
Guest Editor

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. Sustainability is an international peer-reviewed open access semimonthly 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 1900 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

  • data-driven smart sustainable cities
  • smart cities
  • sustainable cities
  • smart urbanism
  • data-driven urbanism
  • big data technologies
  • data-driven smart applications
  • urban analytics
  • computational urban science
  • urban intelligence
  • sustainability

Published Papers (1 paper)

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Research

Article
Computational Valuation Model of Housing Price Using Pseudo Self Comparison Method
Sustainability 2021, 13(20), 11489; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011489 (registering DOI) - 18 Oct 2021
Viewed by 116
Abstract
Hedonic pricing method (HPM), which is commonly used for estimating real estate property values, considers the property’s internal and external characteristics for its valuation. Despite its popularity, however, the method lacks the mechanism that directly reflects the target property’s price fluctuation and the [...] Read more.
Hedonic pricing method (HPM), which is commonly used for estimating real estate property values, considers the property’s internal and external characteristics for its valuation. Despite its popularity, however, the method lacks the mechanism that directly reflects the target property’s price fluctuation and the real estate market’s volatility over time. To overcome these limitations, we propose Pseudo Self Comparison Method (PSCM), which reduces the real estate valuation problem to finding a pseudo self, which is defined as a housing property that can most closely approximate the characteristics of the target housing property, and adjusting its previous transaction price to be in sync with the real estate market change. The proposed PSCM is tested for two scenarios in which the volatility of the real estate market varies greatly, using the transaction data compiled from Seoul, the capital of South Korea, and its surrounding region, Gyeonggi. The study results show almost five times lower estimation errors when predicting housing transaction prices using the PSCM compared to the HPM in both scenarios and in both areas. The proposed method is particularly useful for mass valuation of apartments or densely located housing units. Full article
(This article belongs to the Special Issue Smart Sustainable Cities in the Era of Big Data)
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