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Land
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Untangling Urban Analysis Using Geographic Data and GIS Technologies
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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: closed (25 September 2025) | Viewed by 8933

Special Issue Editors

Dr. He Jin

E-Mail Website
Guest Editor
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
Prof. Dr. Joni Downs Firat

E-Mail Website
Guest Editor
School of Geosciences, University of South Florida, 4202 East Fowler Ave, Tampa, FL 33620, USA
Interests: GIScience; wildlife ecology; health; transportation
Special Issues, Collections and Topics in MDPI journals

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

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 250 words) can be sent to the Editorial Office for assessment.

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 (5 papers)

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Research

29 pages, 8191 KB  
Article
Driving Mechanisms and Spatial Governance Strategies for Urban–Water Synergy Systems
by Yan Feng, Chongyu Tong and Qiunan Chen
Land 2026, 15(1), 76; https://doi.org/10.3390/land15010076 - 31 Dec 2025
Viewed by 556
Abstract
This study examines urban–water synergy as the spatial coordination between urban expansion and water systems. Using land-use data from 2000 to 2020, the central urban areas of Jingzhou and Anqing are analyzed as representative small and medium-sized cities. Urban–water synergy is assessed across [...] Read more.
This study examines urban–water synergy as the spatial coordination between urban expansion and water systems. Using land-use data from 2000 to 2020, the central urban areas of Jingzhou and Anqing are analyzed as representative small and medium-sized cities. Urban–water synergy is assessed across three dimensions: land-use synergy, pathway synergy, and directional synergy. These dimensions are quantified using four indicators: Urban–Water Interaction Intensity (UWII), Urban–Water Interaction Displacement (UWID), Spatial Path Alignment Distance (SPAD), and Directional Alignment Angle (DAA). The results show that Jingzhou and Anqing exhibit two distinct urban–water synergy modes: a convergent interaction mode characterized by increasing alignment in land-use interactions, spatial pathways, and directional tendencies, and a divergent synergy mode characterized by persistent separation across these dimensions. Differences between these synergy modes are associated with expansion pressure, physical template, and institutional mechanisms. Spearman rank correlation and principal component analysis suggest that institutional mechanisms constitute an independent analytical dimension and may be relevant for interpreting potential non-linear changes in urban–water interaction patterns. Based on these findings, this study discusses governance implications centered on institutional effectiveness, supported by spatial restoration and expansion regulation, for informing urban–water synergy governance in small and medium-sized cities. Full article
(This article belongs to the Special Issue Untangling Urban Analysis Using Geographic Data and GIS Technologies)
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39 pages, 4823 KB  
Article
Multi-Dimensional Driving Mechanisms and Scenario Simulation of Production-Living-Ecological Space Evolution in Urban Agglomerations of China: Evidence from the Guanzhong Plain
by Chao Gao, Shasha Li, Hanchuan Bao and Yilin Zhang
Land 2025, 14(11), 2201; https://doi.org/10.3390/land14112201 - 5 Nov 2025
Cited by 1 | Viewed by 1624
Abstract
The coordinated development of Production-Living-Ecological (PLE) spaces has emerged as a core challenge for regional sustainability amid rapid urbanization processes. This study examines the Guanzhong Plain Urban Agglomeration (2001–2021) using an integrated Markov-PLUS model coupled with Random Forest algorithms and 17 driving factors [...] Read more.
The coordinated development of Production-Living-Ecological (PLE) spaces has emerged as a core challenge for regional sustainability amid rapid urbanization processes. This study examines the Guanzhong Plain Urban Agglomeration (2001–2021) using an integrated Markov-PLUS model coupled with Random Forest algorithms and 17 driving factors to construct 4 policy scenarios for future projections. The results reveal dramatic spatial restructuring: living space expanded 73.89% while production and ecological spaces contracted 7.47% and 8.94%. Evolution occurred through four distinct phases—rapid expansion, structural adjustment, quality improvement, and green transformation—each corresponding to national policy transitions with regional lags. Driving mechanism analysis identified environmental factors contributing 45–55% of variance, population density driving 24.2% of living space expansion, and elevation thresholds constraining urban growth above 1000 m. Multi-scenario simulations revealed fundamental trade-offs: urban development scenarios achieved 55.34% built-up expansion but sacrificed 15.4% ecological space, while ecological protection scenarios maintained 92% food production capacity with optimal connectivity (0.63) and maximum carbon storage (1287 Mt C). Model validation achieved exceptional accuracy (Kappa = 0.91, FoM = 0.24). This research emphasizes three strategic imperatives: (1) differentiated spatial governance (urban priority in cores, farmland protection in plains, ecological restoration in mountains); (2) temporal coordination mechanisms accounting for 3–5-year policy transmission lags; (3) adaptive management approaches addressing nonlinear evolution characteristics. This framework provides scientific foundations for balancing economic development, food security, and ecological protection in rapidly urbanizing regions. Full article
(This article belongs to the Special Issue Untangling Urban Analysis Using Geographic Data and GIS Technologies)
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37 pages, 22486 KB  
Article
A National-Scale Evaluation of Eco-City Development in China: Spatial Heterogeneity, Obstacle Factors, and Relationship with Carbon Intensity
by Yuhui Wu, Deqin Fan, Yajun Cui, Shouhang Du, Wenbin Sun, Liyuan Guo and Chunhuan Liu
Land 2025, 14(11), 2146; https://doi.org/10.3390/land14112146 - 28 Oct 2025
Viewed by 919
Abstract
Under the national “dual-carbon goal” and the pressing demand for sustainable development, eco-city construction and carbon reduction have become critical issues on China’s urban development agenda, closely aligned with the United Nations Sustainable Development Goals (SDGs). However, most studies focus on regional assessments, [...] Read more.
Under the national “dual-carbon goal” and the pressing demand for sustainable development, eco-city construction and carbon reduction have become critical issues on China’s urban development agenda, closely aligned with the United Nations Sustainable Development Goals (SDGs). However, most studies focus on regional assessments, lacking national-scale evaluations and spatial heterogeneity analysis of obstacles. This study analyzes 280 Chinese cities using a multi-level evaluation system. Analytic hierarchy process (AHP) and entropy weight methods determine index weights, while the comprehensive evaluation method assesses ecological levels. The obstacle diagnosis model identifies key obstacle factors, and geographically weighted regression (GWR) analyzes spatial heterogeneity, computing carbon intensity to explore relationships with eco-cities development. The findings reveal that (1) the ecological level of Chinese cities exhibits a regional pattern of “high in the east, low in the west”; (2) the primary index-level obstacle factors include total per capita water resources, per capita green space area, college full-time faculty per 10,000 people, the proportion of tertiary industries in gross domestic product (GDP), and college students per 10,000 people; at the element level, the main obstacles are environmental bases, social services, economic potential, and innovative capacity; (3) the GWR model reveals that eastern regions should increase water resources, central regions expand green space, and western and northeastern regions enhance innovative capacity and social services to foster balanced development; and (4) carbon intensity follows a “low in the east, high in the west” pattern, with eco-cities scores significantly negatively correlated with carbon intensity (r = −0.235, p < 0.01). This study provides the first comprehensive national-scale evaluation of eco-cities development, providing reference for the construction of eco-cities. Full article
(This article belongs to the Special Issue Untangling Urban Analysis Using Geographic Data and GIS Technologies)
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19 pages, 6262 KB  
Article
“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China
by Quanchuan Fu, Jingyuan Zhu, Xiaodi Zheng, Zhengxiang Li, Maini Chen and Yuyuwei He
Land 2025, 14(6), 1213; https://doi.org/10.3390/land14061213 - 5 Jun 2025
Cited by 1 | Viewed by 1330
Abstract
Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses [...] Read more.
Brownfields are abundant, widely dispersed, and subject to complex contamination, resulting in waste land, ecological degradation, and barriers to economic growth. The accurate identification of brownfield sites is key to formulating effective remediation and reuse strategies. However, the heterogeneity of surface features poses significant challenges for identifying various types of brownfields across entire urban areas. To address these challenges, this study proposes a “Target–Classification–Modification” (TCM) method for brownfield identification, which was applied to Tangshan City, China. This method consists of a three-stage process: target area localization, visual interpretation and classification, and site-level modification. It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. The TCM method improved the identification efficiency by 34.7% through precise target-area localization. It offers well-defined criteria to distinguish different brownfield subtypes. In addition, it employs a multi-approach strategy to determine the abandonment status, further enhancing accuracy. This method is scalable and widely applicable, providing support for urban-scale brownfield research and practice. Full article
(This article belongs to the Special Issue Untangling Urban Analysis Using Geographic Data and GIS Technologies)
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24 pages, 4456 KB  
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
Cited by 3 | Viewed by 3714
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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