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Special Issue "Big Data in a Sustainable Smart City"

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

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 18552

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Special Issue Editors

Dr. Abdellah Chehri

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Guest Editor

Département des Sciences Appliquées, Université de Québec à Chicoutimi, 555, boul. de l’Université, Chicoutimi, QC G7H 2B1, Canada
Interests: big data; smart and sustainable cities; urban innovation system; urban knowledge and innovation spaces; knowledge-based development
Special Issues, Collections and Topics in MDPI journals

Dr. Gwanggil Jeon

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Guest Editor

Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: remote sensing; deep learning; artificial intelligence; image processing; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart cities are meant to supervise available resources sustainably to enhance the economy and societal outcomes. Therefore, data from assorted resources are measured to be the most scalable property of a smart city. Research results on big data have been focused on enhancing the latter stages of processing colossal amounts of data. In smart cities, different villages and cities produce heterogeneous data with minimal or no coordination. The existing methods to address these problems in big data analytics for smart cities are still immature. Most of these methods are also time consuming, as they use traditional tools of data mining. In order to solve this issue, new and elegant methods are required to effectively control the big data generated from the sensors deployed in existing cities. This Special Issue aims to report the latest advances and trends concerning advanced machine learning techniques and time series remote sensing data processing issues. Papers addressing both theory and application are welcome, as well as contributions regarding new advanced machine learning techniques for the remote sensing research community. Major topics of interest include, but are not limited to, the following:

Smart cities based on big data;
Big data analytics;
Embedded sensing;
System design, modeling, and evaluation within a smart city;
Smart systems for sustainability.

Dr. Gwanggil Jeon
Dr. Abdellah Chehri
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 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. 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 2400 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

smart city
big data analytics
embedded sensing
smart system
system design
sustainability

Published Papers (5 papers)

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Research

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22 pages, 6724 KiB

Open AccessArticle

Open Data Based Urban For-Profit Music Venues Spatial Layout Pattern Discovery

Xueqi Wang

and

Zhichong Zou

Sustainability 2021, 13(11), 6226; https://doi.org/10.3390/su13116226 - 01 Jun 2021

Cited by 2 | Viewed by 2519

Abstract

The spatial pattern of music venues is one of the key decision-making factors for urban planning and development strategies. Understanding the current configurations and future demands of music venues is fundamental to scholars, planners, and designers. There is an urgent need to discover the spatial pattern of music venues nationwide with high precision. This paper aims at an open data solution to discover the hidden hierarchical structure of the for-profit music venues and their dynamic relationship with urban economies. Data collected from the largest two public ticketing websites are used for clustering-based ranking modeling and spatial pattern discovery of music venues in 28 cities as recorded. The model is based on a multi-stage hierarchical clustering algorithm to level those cities into four groups according to the website records which can be used to describe the total music industry scale and activity vitality of cities. Data collected from the 2018 China City Statistical Year Book, including the GDP per capita, disposable income per capita, the permanent population, and the number of patent applications, are used as socio-economic indicators for the city-level potential capability of music industry development ranking. The Spearman’s rank correlation coefficient and the Kendall rank correlation coefficient are applied to test the consistency of the above city-level rankings. The results are 0.782 and 0.744 respectively, which means there is a relatively significant correlation between the scale level of current music venue configuration and the potential to develop the music industry. Average nearest neighbor index (ANNI), quadrate analysis, and Moran’s I are used to identify the spatial patterns of music venues of individual cities. The results indicate that music venues in urban centers show more spatial aggregation, where the spatial accessibility of music activity services takes the lead significantly, while a certain amount of venues with high service capacity distribute in suburban areas. The findings can provide decision support for urban planners to formulate effective policies and rational site-selection schemes on urban cultural facilities, leading to smart city rational construction and sustainable economic benefit. Full article

(This article belongs to the Special Issue Big Data in a Sustainable Smart City)

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23 pages, 4119 KiB

Open AccessArticle

Influence of Built Environment on Street Vitality: A Case Study of West Nanjing Road in Shanghai Based on Mobile Location Data

Wanshu Wu

Xinyi Niu

and

Meng Li

Sustainability 2021, 13(4), 1840; https://doi.org/10.3390/su13041840 - 08 Feb 2021

Cited by 15 | Viewed by 2934

Abstract

A successful built environment is assumed to encourage street vitality in the time and space dimensions. The availability of mobile location data has made it possible to measure street vitality from a large-scale and multiperiod perspective. We used the mobile location data recorded in West Nanjing Road and the surrounding streets in Shanghai as a proxy for street activity, and introduced intensity and instability as indicators of street vitality to test whether there is still a correlation between street vitality and built environment in high-density cities, and whether there are applicable conditions. The results show that for spatial units with higher intensity, the street activities tend to be more unstable. It is more effective to promote street vitality by increasing the diversity of commercial formats. For the streets in high-intensity areas, increasing the mix degree of building functions and the development intensity of the surrounding blocks may not necessarily enhance the street vitality. The design of the external spaces is always an effective measure to maintain continuous vitality. Subway stations play a significant role in promoting street vitality. Full article

(This article belongs to the Special Issue Big Data in a Sustainable Smart City)

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18 pages, 11658 KiB

Open AccessArticle

Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

Jiayu Qin

Gang Mei

and

Lei Xiao

Sustainability 2021, 13(1), 266; https://doi.org/10.3390/su13010266 - 30 Dec 2020

Cited by 11 | Viewed by 2431

Abstract

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems. Full article

(This article belongs to the Special Issue Big Data in a Sustainable Smart City)

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14 pages, 5933 KiB

Open AccessArticle

Comparing Human Activity Density and Green Space Supply Using the Baidu Heat Map in Zhengzhou, China

and

Sustainability 2020, 12(17), 7075; https://doi.org/10.3390/su12177075 - 30 Aug 2020

Cited by 21 | Viewed by 2861

Abstract

Rapidly growing cities often struggle with insufficient green space, although information on when and where more green space is needed can be difficult to collect. Big data on the density of individuals in cities collected from mobile phones can estimate the usage intensity of urban green space. Taking Zhengzhou’s central city as an example, we combine the real-time human movement data provided by the Baidu Heat Map, which indicates the density of mobile phones, with vector overlays of different kinds of green space. We used the geographically weighted regression (GWR) method to estimate differentials in green space usage between weekdays and weekends, utilizing the location and the density of the aggregation of people with powered-up mobile phones. Compared with weekends, the aggregation of people in urban green spaces on workdays tends to vary more in time and be more concentrated in space, while the highest usage is more stable on weekends. More importantly, the percentage of weekday green space utilization is higher in small parks and green strips in the city, with the density increasing in those small areas, while the green space at a greater distance to the city center is underutilized. This study validates the potential of applying Baidu Heat Map data to provide a dynamic perspective of green space use, and highlights the need for more green space in city centers. Full article

(This article belongs to the Special Issue Big Data in a Sustainable Smart City)

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Review

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19 pages, 9735 KiB

Open AccessReview

Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

Abdellah Chehri

Issouf Fofana

and

Xiaomin Yang

Sustainability 2021, 13(6), 3196; https://doi.org/10.3390/su13063196 - 15 Mar 2021

Cited by 41 | Viewed by 6310

Abstract

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented. Full article

(This article belongs to the Special Issue Big Data in a Sustainable Smart City)

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