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Journals
Smart Cities
Special Issues
Geographic Knowledge Discovery and Big Data Analytics in Smart Cities
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Geographic Knowledge Discovery and Big Data Analytics in Smart Cities

A special issue of Smart Cities (ISSN 2624-6511).

Deadline for manuscript submissions: closed (31 January 2020) | Viewed by 3054

Special Issue Editor

Prof. Dr. Robert Laurini

E-Mail Website
Guest Editor
Knowledge Systems Institute and INSA Lyon, University of Lyon, Villeurbanne, France
Interests: theoretical aspects of GIS and knowledge engineering for urban applications, and more generally how to cross-fertilize artificial intelligence and urban and environmental planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A smart city can be defined with a digital layer which can be used to plan and manage. Now, regularly sensors are sending data in immense repository under the name of big data. Those data must be mined to discover knowledge which can be useful in daily practice and future planning. Those knowledge chunks can come from data mining or deep learning.

The scope of this special issue is to regroup first class papers dealing with knowledge discovery in smart cities by presenting methodologies not only to extract knowledge chunks, but also to model them. We are overall looking for case studies and novel experiences in this domain linked with practical applications.

Prof. Dr. Robert Laurini
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 submissions that pass pre-check are 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 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 2000 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

  • big data
  • knowledge discover and modelling
  • integration of knowledge in planning and management
  • case studies

Published Papers (1 paper)

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Review

10 pages, 267 KiB  
Review
Big Data for Natural Disasters in an Urban Railroad Neighborhood: A Systematic Review
by Thaís P. Correia, Alessandra C. Corsi and José A. Quintanilha
Smart Cities 2020, 3(2), 202-211; https://doi.org/10.3390/smartcities3020012 - 01 Apr 2020
Cited by 4 | Viewed by 2622
Abstract
Landslides and floods are among the most common disasters in Brazil and are responsible for losses on social, environmental, and economic scales, even resulting in deaths. Floods can negatively affect the structure and operations of a railway network, causing travel delays, train service [...] Read more.
Landslides and floods are among the most common disasters in Brazil and are responsible for losses on social, environmental, and economic scales, even resulting in deaths. Floods can negatively affect the structure and operations of a railway network, causing travel delays, train service cancellations, and major fines for the railway. The objective of this article is to conduct a bibliographic review of what is available in publications on natural disasters, particularly landslides and floods, big data techniques, and railroads, at international and national levels. A bibliometric analysis was carried out according to the “PRISMA Flow Diagram” guidelines. The analysis in this study was conducted through searches of the following reference databases: Scopus, Web of Science, Scielo, and Google Scholar. After the keyword search was completed, the absence of available data and references relating to Brazil was verified. This justified the development of this and other related papers, and the efforts necessary to turn these data into useful information for the managers of cities and environmental institutions. The aim of this study is to fill the gap in the research, focusing on Brazil, related to big data, smart cities, and natural disasters (particularly, landslides and floods), and to propose other papers that can be developed in this subject area. Full article
(This article belongs to the Special Issue Geographic Knowledge Discovery and Big Data Analytics in Smart Cities)
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