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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 December 2023) | Viewed by 5846

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Special Issue Editors

Prof. Dr. Xiaoguang Zhou

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Guest Editor

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: spatiotemporal data modelling and updating; crowdsourcing geographic information; topological relationship
Special Issues, Collections and Topics in MDPI journals

Dr. Yijiang Zhao

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Guest Editor

School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Interests: modelling and processing of crowdsourcing geographic data; spatiotemporal data mining

Dr. Fei Chen

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Guest Editor

School of Surveying and Mapping, East China University of Technology, Nanchang 330013, China
Interests: spatiotemporal data modelling; quality assessment and control

Special Issue Information

Dear Colleagues,

Today, more than half of the population lives in urbanized areas, and the convenience of living in urban areas is still attracting large rural populations in developing countries. Urbanization promotes industrial development, scientific and technological progress and cultural exchanges. However, rapid urbanization brings challenges, such as human settlement planning and management, disaster risk reduction, urban cultural and natural heritage protection, etc. A wide range of data is required to monitor, measure and present the progress being made in sustainable urban development. Spatiotemporal data describe almost every aspect of cities and plays an important role in supporting and tracking urban development over time and discovering patterns and potential problems. Therefore, the study of urban sustainability using spatiotemporal data has become increasingly important.

In the past decade, there have been big developments regarding spatiotemporal-data-related science and technology (e.g., collection, modelling and handling, etc.), including in crowdsourcing data, sensor networks and spatiotemporal knowledge graphs, AI (Artificial Intelligence), etc. These achievements provide opportunities to model and analyse phenomena in urban areas and to measure and monitor the SDGs related urban sustainability, e.g., SDG 11: sustainable cities and communities, etc. This Special Issue, entitled “Spatiotemporal Data and Urban Sustainability” aims to present the recent R&D progress in relevant topics and explore the opportunities and challenges related to spatiotemporal-data-enabled urban sustainability.

This Special Issue focuses on spatial data and urban sustainability. The emphasis of this issue includes, but is not limited to, significant improvements in the monitoring and assessment of urban sustainability, the modelling and representation of urban data, the capture and evaluation of urban reliable information, geo-computing algorithms and new methods for urban studies and analytical and quantitative methods for urban issues. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following topics:

Modelling and analysing urban phenomena;
The monitoring and assessment of SDG 11: sustainable cities and communities;
Reliability evaluation methods for urban information;
Spatiotemporal urban data model;
Updating the urban database;
Visualization methods for urban data;
Analytical and quantitative methods for urban phenomena;
AI applied to Earth Observation and geospatial data for urban studies;
Simulation models and techniques for urban analysis;
Urban data quality and uncertainty;
Algorithms and methods for deriving geospatial essential variables for SDGs, e.g., machine learning, big data analytics, cloud computing and other technologies;
Urban planning issues (community development, zoning, etc.);
Urban transportation and mobility (airports, subways, etc.);
Urban consumption and commodities;
Crowdsourcing-data-enabled urban sustainability;
Intelligent traffic systems;
Knowledge graphs.

We look forward to receiving your contributions.

Prof. Dr. Xiaoguang Zhou
Dr. Yijiang Zhao
Dr. Fei Chen
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

sustainability
urban
spatiotemporal data
SDGs
incremental updating
crowdsourcing data
indoor GIS
monitoring
spatial analysis
simulation

Published Papers (4 papers)

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Research

16 pages, 4033 KiB

Open AccessArticle

The Spatial Pattern and Influencing Factors of Urban Knowledge-Intensive Business Services: A Case Study of Wuhan Metropolitan Area, China

by Zilu Ma and Yaping Huang

Sustainability 2024, 16(3), 1110; https://doi.org/10.3390/su16031110 - 28 Jan 2024

Viewed by 658

Abstract

Knowledge-intensive business services (KIBSs) are key links in leading the sustainable development of cities. Studying the spatial pattern and influencing factors of urban KIBSs can help improve the utilization of KIBS resources. Taking the Wuhan metropolitan area as a case study, based on data from industrial and commercial registration enterprises, this study uses the multi-ring buffer zone analysis and kernel density estimation method to analyze the spatial pattern of KIBS, and uses a negative binomial regression model to detect the influencing factors of the spatial pattern of KIBS. The results show that: (1) KIBSs are mainly distributed in the inner suburbs, presenting a multi-center spatial pattern, exhibiting the law of agglomeration along entrepreneurial streets, headquarter bases, science and technology parks, university clusters, business centers, and industrial bases. Obvious differences exist in the spatial patterns of KIBS sub-sectors. (2) Land price, traffic conditions, office space, commercial environment, technology factors, industry diversity, incubation environment, investment environment, manufacturing foundation, agglomeration factors, and policy factors are the main factors affecting the spatial patterns of KIBSs. There are differences in the impact of influencing factors on KIBS sub-sectors. The results can provide a decision-making basis for the rational layout and planning of urban KIBSs in the post-industrial era. Full article

(This article belongs to the Special Issue Spatiotemporal Data and Urban Sustainability)

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17 pages, 3484 KiB

Open AccessArticle

MSC-DeepFM: OSM Road Type Prediction via Integrating Spatial Context Using DeepFM

by Yijiang Zhao, Yahan Ning, Haodong Li, Zhuhua Liao, Yizhi Liu and Feng Li

Sustainability 2023, 15(24), 16671; https://doi.org/10.3390/su152416671 - 08 Dec 2023

Viewed by 690

Abstract

The quality of OpenStreetMap (OSM) has been widely concerned as a valuable source for monitoring some sustainable development goals (SDG) indicators. Improving its semantic quality is still challenging. As a kind of solution, road type prediction plays an important role. However, most existing algorithms show low accuracy, owing to data sparseness and inaccurate description. To address these problems, we propose a novel OSM road type prediction approach via integrating multiple spatial contexts with DeepFM, named MSC-DeepFM. A deep learning model DeepFM is used for dealing with data sparseness. Moreover, multiple spatial contexts (MSC), including the features of intersecting roads, surrounding buildings, and points of interest (POIs), are distilled to describe multiple types of road more accurately. The MSC combined with geometric features and restricted features are put into DeepFM, in which the low-order and high-order features fully interact. And a multivariate classifier OneVsRest is adopted to predict road types. Experiments on OSM show that the proposed model MSC-DeepFM achieves excellent performance and outperforms some state-of-the-art methods. Full article

(This article belongs to the Special Issue Spatiotemporal Data and Urban Sustainability)

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18 pages, 8887 KiB

Open AccessArticle

Classification of Urban Green Space Types Using Machine Learning Optimized by Marine Predators Algorithm

by Jiayu Yan, Huiping Liu, Shangyuan Yu, Xiaowen Zong and Yao Shan

Sustainability 2023, 15(7), 5634; https://doi.org/10.3390/su15075634 - 23 Mar 2023

Cited by 2 | Viewed by 1627

Abstract

The accuracy of machine learning models is affected by hyperparameters when classifying different types of urban green spaces. To investigate the impact of hyperparametric algorithms on model optimization, this study used the Marine Predators Algorithm (MPA) to optimize three models: K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF). The feasibility of the algorithm was illustrated by extracting and analyzing park green space and attached green spaces within the fifth-ring road of Beijing. A dataset of urban green space type labels was constructed using SPOT6. Three optimized models, MPA-KNN, MPA-SVM and MPA-RF, were constructed. The optimum hyperparameter combination was chosen based on the accuracy of the validation set, and the three optimized models were compared in terms of the Area Under Curve (AUC) value, accuracy on the test set, and other indicators. The results showed that applying MPA improves the accuracy of the validation set of the KNN, SVM, and RF models by 4.2%, 2.2%, and 1.2%, respectively. The MPA-RF model had an AUC value of 0.983 and a test set accuracy of 89.93%, indicating that it was the most accurate of the three models. Full article

(This article belongs to the Special Issue Spatiotemporal Data and Urban Sustainability)

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18 pages, 1554 KiB

Open AccessArticle

Analysis of Residential Satisfaction Changes by the Land Bank Program Using Text Mining

by Seongbeom Park, Jaekyung Lee and Yunmi Park

Sustainability 2022, 14(18), 11707; https://doi.org/10.3390/su141811707 - 18 Sep 2022

Cited by 3 | Viewed by 1985

Abstract

Many American manufacturing cities have experienced depopulation and economic downturns over the past five decades, and various revitalization strategies have been suggested to overcome the decline issue—ranging from redevelopment to smart decline. However, while most land bank-related studies have focused on socioeconomic dynamics (income levels, unemployment rate, etc.) through the program, there is a lack of direct research on residential satisfaction changes. Additionally, surveys were frequently used in previous studies to evaluate residential satisfaction; however, this method has disadvantages, including constraints on time and cost, and the inability to take into account external factors that may affect residential satisfaction. Furthermore, most studies on urban decline have focused primarily on declining factors, and there have been few investigations into how cities change as urban regeneration strategies advance. Therefore, the primary purpose of this study is to identify the influence of the land bank program on residential satisfaction by using Twitter data. Approximately 300,000 Twitter posts containing location information generated within the city of Detroit were collected to determine the degree of sensitivity to each tweet and categorized into positive and negative emotions to determine the relationship between residential satisfaction and the land bank program. As a result, the increase in homeownership, built year, house value, and the number of land banking sold properties were found to have a negative effect on neighborhood satisfaction in Detroit. Although the research results indicated that while the land bank program did not significantly improve residential satisfaction in Detroit, it has made a partial contribution to improving living standards. These findings emphasize the importance of enhancing residential satisfaction and suggest the need for policy change. In response to the problem of urban contraction, it seems that indiscriminately distributing houses is not the only solution to prevent urban shrinkage. Furthermore, this study shows meaningful results on text mining and provides the possibility of developing research using social network services. Full article

(This article belongs to the Special Issue Spatiotemporal Data and Urban Sustainability)

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