Special Issue "Understanding and Personalising Smart City Services Using Big Data and Machine Learning"

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

Deadline for manuscript submissions: closed (30 June 2021).

Special Issue Editor

Dr. Xiaojiang Li
E-Mail Website
Guest Editor
Assistant professor, Department of Geography and Urban Studies, Temple University, Philadelphia, PA 19122, USA
Interests: urban analytics; urban environmental informatics; spatial data science; Urban Sustainability; Urban Science

Special Issue Information

Dear Colleagues,

The “smart city” concept has been proposed to make cities more efficient through the increased use of intelligent technology. For many years, the smart city concept has been narrowly practiced using information technologies to optimize the efficiency of the city. Though more efficient cities can produce economic benefits and conveniences for urban residents, cities are not just machines and good life quality is not identical to efficiency. More efficient cities may lead to people being vulnerable to public health threats, disasters, and personal privacy violations and could also be less environmentally friendly.

Smart cities should not just be efficient and intelligent but more human-centered. This Special Issue encourages research using urban big data, machine learning, urban analytics, and network analysis to provide more human-centered solutions for building more human-friendly and human-centered city intelligence. We invite you to submit work on the most recent advancements in (but not limited to) the following topics:

  1. Urban big data and urban analytics
  2. Urban sensing methods
  3. Urban environment health
  4. Urban analytics for environmental sustainability
  5. Ethnics of urban big data
  6. Human-centered urban computing
  7. Energy and sustainability
  8. Sustainable transportation
  9. Intelligent building system for urban sustainability
  10. Privacy protection

Dr. Xiaojiang Li
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. 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

  • Urban big data
  • Urban analytics
  • Urban intelligence

Published Papers (2 papers)

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Research

Article
Direct Passive Participation: Aiming for Accuracy and Citizen Safety in the Era of Big Data and the Smart City
Smart Cities 2021, 4(1), 336-348; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010020 - 10 Mar 2021
Viewed by 952
Abstract
The public services in our smart cities should enable our citizens to live sustainable, safe and healthy lifestyles and they should be designed inclusively. This article examines emerging data-driven methods of citizen engagement that promise to deliver effortless engagement and discusses their suitability [...] Read more.
The public services in our smart cities should enable our citizens to live sustainable, safe and healthy lifestyles and they should be designed inclusively. This article examines emerging data-driven methods of citizen engagement that promise to deliver effortless engagement and discusses their suitability for the task at hand. Passive participation views citizens as sensors and data mining is used to elicit meaning from the vast amounts of data generated in a city. Direct passive participation has a clear link between the creation and the use of the data whereas indirect passive participation does not require a link between creation and use. The Helsinki city bike share scheme has been selected as a case study to further explore the concept of direct passive participation. The case study shows that passive user generated data is a strong indicator of optimum city bike station sizing relative to the existing methods that are already in use. Indirect passive participation is an important area of development; however, it still needs to be developed further. In the meantime, direct passive participation can be one of the tools used to design inclusive services in a way that is safe and an accurate representation of the citizens’ needs. Full article
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Article
Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case
Smart Cities 2021, 4(1), 286-313; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010018 - 28 Feb 2021
Viewed by 1233
Abstract
Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of [...] Read more.
Interest in smart cities (SCs) and big data analytics (BDA) has increased in recent years, revealing the bond between the two fields. An SC is characterized as a complex system of systems involving various stakeholders, from planners to citizens. Within the context of SCs, BDA offers potential as a data-driven decision-making enabler. Although there are abundant articles in the literature addressing BDA as a decision-making enabler in SCs, mainstream research addressing BDA and SCs focuses on either the technical aspects or smartening specific SC domains. A small fraction of these articles addresses the proposition of developing domain-independent BDA frameworks. This paper aims to answer the following research question: how can BDA be used as a data-driven decision-making enabler in SCs? Answering this requires us to also address the traits of domain-independent BDA frameworks in the SC context and the practical considerations in implementing a BDA framework for SCs’ decision-making. This paper’s main contribution is providing influential design considerations for BDA frameworks based on empirical foundations. These foundations are concluded through a use case of applying a BDA framework in an SC’s healthcare setting. The results reveal the ability of the BDA framework to support data-driven decision making in an SC. Full article
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