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Geographical Information Technology and Urban Sustainable Development
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Geographical Information Technology and Urban Sustainable Development

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 2270

Special Issue Editors

Dr. Jianbo Tang

E-Mail Website
Guest Editor
Department of Geo-Informatics, Central South University, Changsha 410086, China
Interests: geographical data analysis; crowdsourcing mapping; human mobility pattern; urban functional zone identification and sustainable development
Special Issues, Collections and Topics in MDPI journals
Dr. Xuexi Yang

E-Mail Website
Guest Editor
School of Geosciences and Info-physics, Central South University, Changsha 410012, China
Interests: spatiotemporal data mining; spatial interaction; social sensing; geographic big data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Wentao Yang

E-Mail Website
Guest Editor
National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China
Interests: spatiotemporal modeling and forecasting; urban and ecological environment assessment; GIS applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of big data, the development of geographical information technology provides important technical support that enables us to understand the spatiotemporal distribution patterns and evolution mechanisms of urban land use, transportation, the ecological environment and human activities. In particular, the application of GeoAI models offers new opportunities for dynamic simulation, intelligent detection and the prediction of urban development. Employing current GIS technologies and artificial intelligence models to empower decision-making regarding urban planning and sustainable development remains a hot topic and is worthy of in-depth discussion and research. This Special Issue aims to encourage researchers to publish their new ideas, models, methods and frameworks on geographical information technologies and urban sustainable development in order to promote the usage of GIS technologies in sustainable urban studies. In this Special Issue, original research articles and reviews are welcome. The scope of this Special Issue includes, but is not limited to, the following topics:

  • GIS techniques and GeoAI models for urban sustainable development
  • Geographical data mining and knowledge discovery
  • Multi-model spatiotemporal data fusion
  • Urban climate change, health analysis and risk assessment
  • Land use classification and change detection
  • Simulation and forecasting of urban development
  • Urban traffic analysis and human mobility patterns detection
  • Social Sensing for urban planning and management

Dr. Jianbo Tang
Dr. Xuexi Yang
Dr. Wentao Yang
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

  • geospatial data analysis
  • social sensing
  • sustainable cities
  • urban land use patterns
  • simulation and forecasting of urban development
  • GeoAI

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Published Papers (2 papers)

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Research

10 pages, 1649 KiB  
Article
The Poorer the Neighborhood, the Harder It Is to Reach the Park: A GIS Equity Analysis from Salt Lake City
by Ivis Garcia
Sustainability 2025, 17(9), 3774; https://doi.org/10.3390/su17093774 - 22 Apr 2025
Viewed by 834
Abstract
Inequitable access to parks persists in cities where race, income, and geography shape residents’ proximity to public green space. This study analyzes 20 parks in Salt Lake City—10 in the Eastside and 10 in the Westside—using demographic, housing, and transportation data drawn from [...] Read more.
Inequitable access to parks persists in cities where race, income, and geography shape residents’ proximity to public green space. This study analyzes 20 parks in Salt Lake City—10 in the Eastside and 10 in the Westside—using demographic, housing, and transportation data drawn from GIS tools and spatial platforms. By assessing indicators such as household income, racial composition, rent burden, walkability, and transit access, the findings confirm that Westside parks—located in lower-income and more racially diverse neighborhoods—are significantly less accessible. Eastside parks, by contrast, tend to serve higher-income, majority-white areas with better infrastructure. This paper illustrates how spatial inequality in surrounding conditions limits park accessibility, and it proposes GIS as a tool for diagnosing and addressing environmental injustice in urban planning. Full article
(This article belongs to the Special Issue Geographical Information Technology and Urban Sustainable Development)
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31 pages, 6453 KiB  
Article
Trajectory Forecasting for Human Mobility Considering Movement Patterns and the Heterogeneous Effects of Geographical Environments via Potential Fields
by Kaiqi Chen, Pingting Zhou, Jingyi Liu, Min Deng, Qi Guo, Chen Yao, Jinyong Chen and Xinyu Pei
Sustainability 2025, 17(4), 1483; https://doi.org/10.3390/su17041483 - 11 Feb 2025
Cited by 1 | Viewed by 860
Abstract
Trajectory forecasting for human mobility plays a critical role in the effective management and sustainable development of urban transportation, which aligns with the advocacy of Sustainable Development Goals (SDGs). Although several approaches have been developed in other trajectory forecasting applications, such as autonomous [...] Read more.
Trajectory forecasting for human mobility plays a critical role in the effective management and sustainable development of urban transportation, which aligns with the advocacy of Sustainable Development Goals (SDGs). Although several approaches have been developed in other trajectory forecasting applications, such as autonomous driving and intelligent robotics, there remain limitations in forecasting trajectories of human mobility. This is because they do not adequately consider the prior knowledge of human movement patterns and the heterogeneous effects of geographical environments. Therefore, in this study, we propose an environment-driven trajectory forecasting method that can adapt to distinct movement patterns. First, the indicator systems, which systematically summarize the heterogeneous effects of different environmental factors on human mobility, are, respectively, constructed for the convergence, divergence, and leadership patterns. Then, based on the corresponding indicator system, the potential field is generated, representing the calibrated probability of the human mobility direction under the environmental effects. A gradient descent algorithm is finally employed on the potential field to forecast the next-step mobility location. Extensive experiment results demonstrated the satisfactory performance of our proposed method under different movement patterns. Compared to other baselines, our proposed method also shows advantages in both long-term and real-time forecasting. Full article
(This article belongs to the Special Issue Geographical Information Technology and Urban Sustainable Development)
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