Untangling Urban Analysis Using Geographic Data and GIS Technologies

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: 25 September 2025 | Viewed by 417

Special Issue Editors

School of Geosciences, University of South Florida, 4202 East Fowler Ave, Tampa, FL 33620, USA
Interests: GIScience; food environment; health geography; big data; urban planning; spatial data mining; spatial statistics; geovisualization

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Guest Editor
School of Geosciences, University of South Florida, 4202 East Fowler Ave, Tampa, FL 33620, USA
Interests: GIScience; wildlife ecology; health; transportation
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Special Issue Information

Dear Colleagues,

Urbanization is a major global trend profoundly affecting our societies and economies. The rise of widespread urban living has facilitated economic development, technological innovation, cultural exchange, and enhanced access to healthcare and education. Nonetheless, it also brings challenges such as overcrowding, insufficient infrastructure, housing shortages, social inequity, environmental degradation, etc. There is an urgent need to address these challenges and promote sustainable urbanization on a global scale. Concomitantly, with further development of urbanization, geographic data and geographic information systems (GIS) technologies have emerged as powerful tools for untangling urban analysis. By using these technologies, urbanists and policymakers can find effective ways to address critical issues, including those of population growth, transportation, land use, public health, environmental sustainability, and socio-economic development. As cities continue to evolve and face new challenges, these geographic information technologies will remain integral in shaping urban landscapes, enhancing quality of life, and facilitating the development of intelligent and resilient cities for the future.

The goal of this Special Issue is to collect papers (original research articles and review papers) which give insight on untangling urban analysis using geographic data and GIS technologies. Papers submitted to this Special Issue should focus on, but are not limited to, the following themes:

  • Innovative methodologies for urban analysis with GIS/remote sensing;
  • Urban landscapes and ecosystems;
  • Predictive analysis of urbanization for policy, planning, or designing;
  • Urban green space monitoring;
  • Spatial–temporal analysis of urbanization and urban evolution;
  • Uncertainties in urban environments with GIS/remote sensing;
  • Spatial and social inequalities in urban development.

We look forward to receiving your original research articles and reviews.

Dr. He Jin
Prof. Dr. Joni Downs Firat
Guest Editors

Manuscript Submission Information

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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. Land is an international peer-reviewed open access monthly 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 2600 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 planning
  • urban environment
  • GIS
  • geographic data
  • spatial or spatial-temporal analysis

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Published Papers (1 paper)

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Research

24 pages, 4456 KiB  
Article
Applying Machine Learning Algorithms for Spatial Modeling of Flood Susceptibility Prediction over São Paulo Sub-Region
by Temitope Seun Oluwadare, Marina Pannunzio Ribeiro, Dongmei Chen, Masoud Babadi Ataabadi, Saba Hosseini Tabesh and Abiodun Esau Daomi
Land 2025, 14(5), 985; https://doi.org/10.3390/land14050985 - 2 May 2025
Viewed by 309
Abstract
Floods are among the most destructive natural hazards globally, necessitating the identification of flood-prone areas for effective disaster risk management and sustainable urban development. Advanced data-driven techniques, including machine learning (ML), are increasingly used to map and mitigate flood risks. However, ML applications [...] Read more.
Floods are among the most destructive natural hazards globally, necessitating the identification of flood-prone areas for effective disaster risk management and sustainable urban development. Advanced data-driven techniques, including machine learning (ML), are increasingly used to map and mitigate flood risks. However, ML applications for flood risk assessment remain limited in Sorocaba, a sub-region of São Paulo, Brazil. This study employs four ML algorithms—differential evolution (DE), naïve Bayes (NB), random forest (RF), and support vector machines (SVMs)—to develop flood susceptibility models using 16 predictor variables. Key categorical factors influencing flood susceptibility included topographical, anthropogenic, and hydrometeorological, particularly elevation, slope, NDVI, NDWI, and distance to roads. Performance metrics (F1-score and AUC) showed strong results, ranging from 0.94 to 1.00, with the DE and RF models excelling in training, testing, and external datasets. The study highlights model transferability, demonstrating applicability to other regions. Findings reveal that 41% to 50% of Sorocaba is at high flood risk. The explainable artificial intelligence technique Shapley additive explanations (SHAP) further identified moisture and the stream power index (SPI) as significant factors influencing flood occurrence. The study underscores the ML-based model’s potential in highlighting flood-vulnerable areas and guiding flood mitigation strategies, land-use planning, and infrastructure resilience. Full article
(This article belongs to the Special Issue Untangling Urban Analysis Using Geographic Data and GIS Technologies)
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