Spatial Data Analysis and Geographic Information Systems for Urban Land Use

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 540

Special Issue Editors

Department of Geography, Faculty of Social Science and Public Policy, King's College London, London WC2R 2LS, UK
Interests: land use; spatial data analysis; geographic information systems (GIS); spatial perceptions

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Guest Editor
Bartlett School of Environment, Energy and Resources, University College London, London WC1H 0NN, UK
Interests: transport planning; sustainable transport development; transport and climate change; freight transport and logistics; statistics and transport modelling; social equity and well-being; travel behaviour; urban mobility; sustainable urban infrastructure and economics; urban planning and sustainable cities
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Special Issue Information

Dear Colleagues,

In the process of global urbanisation, some regions are experiencing rapid urban expansion, while others face the challenges of urban shrinkage, and, in this context, the precise quantification and scientific regulation of urban land use structures have become crucial in terms of enhancing the quality of life of residents and promoting sustainable spatial development. Traditional planning models often struggle to address the complex and dynamic demands of contemporary cities, hence spatial data analysis and geographic information systems (GISs), with their efficiency in dynamic monitoring and management, have become key research topics. Furthermore, the integration of GIS methods with emerging technological tools, such as large language models (LLMs) and machine/deep learning, provides cutting-edge support for detailed land use quantification and long-term change tracking. These advancements facilitate the rational allocation of land resources, improved urban environments, enhanced ecological protection, and ultimately, better living standards for residents.

This Special Issue aims to focus on cutting-edge research that uses spatial data analysis and GIS technology for the in-depth quantification and regulation of urban land use structures across diverse geographic contexts. The objective is to drive a paradigm shift in urban land use planning and spatial data processing, fostering an organic synthesis of land use theory and advanced technological methodologies. We particularly encourage studies that leverage state-of-the-art technological solutions such as high-resolution remote sensing imagery, multi-source data fusion techniques, and LLMs. These approaches can help uncover land use change patterns, optimise urban spatial structures, and inform evidence-based land use policymaking. Such research not only demands proficiency in new technological tools but also calls for refinements in land use policy frameworks and theoretical models.

We invite submissions on the follow themes: high-resolution remote sensing data analysis, spatial data fusion and multi-source GIS applications, land use change monitoring and modelling, urban ecological model development, and AI-driven urban data processing (e.g., machine learning, deep learning, and LLMs).

We also welcome theoretical discussions, methodological innovations, case studies, and interdisciplinary analyses that offer fresh perspectives and empirical insights into urban land use research.

Dr. Jin Rui
Dr. Mengqiu Cao
Guest Editors

Manuscript Submission Information

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Keywords

  • urban land use
  • spatial data analysis
  • geographic information systems (GISs)
  • remote sensing imagery
  • machine learning
  • large language models (LLMs)
  • big data
  • data-driven
  • urban planning
  • sustainable cities

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Published Papers (1 paper)

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Research

20 pages, 9502 KiB  
Article
Spatiotemporal Coupling Characteristics Between Urban Land Development Intensity and Population Density from a Building-Space Perspective: A Case Study of the Yangtze River Delta Urban Agglomeration
by Xiaozhou Wang, Lie You and Lin Wang
Land 2025, 14(7), 1459; https://doi.org/10.3390/land14071459 - 13 Jul 2025
Viewed by 342
Abstract
As China shifts from rapid to high-quality development, urban growth has exhibited allometric patterns. This study evaluated land use efficiency from the perspective of architectural space, focusing on 41 cities in the Yangtze River Delta urban agglomeration from 2010 to 2020. A land [...] Read more.
As China shifts from rapid to high-quality development, urban growth has exhibited allometric patterns. This study evaluated land use efficiency from the perspective of architectural space, focusing on 41 cities in the Yangtze River Delta urban agglomeration from 2010 to 2020. A land development intensity index was constructed at both the provincial and municipal levels using the entropy weight method, integrating floor area ratio, building density, and functional mix. The spatiotemporal characteristics of land development intensity and population density were analyzed, and a coordination coupling model was applied to identify mismatches between land and population. The results reveal: (1) Temporally, the imbalance of “more people, less land” in the Yangtze River Delta diminished. Spatially, leading regions exhibit a diffusion effect. Shanghai showed a decline in both population density and development intensity; Zhejiang maintained balanced development; Jiangsu experienced accelerated growth; and Anhui showed signs of catching up. (2) Although the two indicators showed a high coupling degree and strong correlation, the coordination degree remained low, indicating poor quality of correlation. The land-population relationship demonstrated a fluctuating pattern of “strengthening–weakening” over time. Shanghai exhibited the highest coordination, while more than half of the cities in Jiangsu, Zhejiang, and Anhui still needed optimization. (3) Unlike previous findings that linked such patterns to shrinking cities, in this transformation stage, the number of cities where land development intensity exceeded population density continued to grow in advanced regions. This study first applied 3D building data at the macro scale to support differentiated spatial policies. Full article
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