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Land, Volume 14, Issue 11 (November 2025) – 5 articles

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19 pages, 2289 KB

Open AccessArticle

From “Policy-Driven” to “Park Clustering”: Evolution and Attribution of Location Selection for Pollution-Intensive Industries in the Beijing–Tianjin–Hebei Urban Agglomeration

by Huixin Zhou, Ziqing Tang, Yumeng Luo, Dingyang Zhou and Guanghui Jiang

Land 2025, 14(11), 2103; https://doi.org/10.3390/land14112103 (registering DOI) - 22 Oct 2025

Abstract

Pollution-intensive industries (PIIs) generate substantial economic benefits while posing serious environmental challenges, making the optimization of their spatial distribution a critical issue for sustainable development. Understanding the spatiotemporal dynamics behind PII location patterns is essential for effective land-use planning and industrial policy. This study investigates the location patterns of newly established PIIs in the Beijing–Tianjin–Hebei urban agglomeration of China between 2007 and 2019. By integrating principal component analysis with a geographically and temporally weighted regression model, the research explores how key drivers influence PII distribution across both spatial and temporal dimensions. The results indicate that government intervention has historically been the most significant factor shaping PII distribution, although its influence has gradually declined due to increasing marketization and technological progress. PIIs are more likely to cluster in areas with moderate levels of economic development, as both very high and very low development levels tend to discourage agglomeration. Over time, improvements in infrastructure, transportation and market conditions have enabled PIIs to overcome geographical constraints. Moreover, industrial parks have emerged as a critical factor by offering cost-efficiency and resource optimization, thereby attracting new PII investment. These findings underscore the importance of accounting for spatiotemporal heterogeneity when analyzing industrial distribution. The study provides policy-relevant insights into industrial land-use planning, highlighting the need for differentiated land supply strategies and the strategic development of industrial parks. It also offers useful references for other developing countries facing similar challenges amid the ongoing restructuring of global manufacturing. Full article

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18 pages, 6899 KB

Open AccessArticle

Beyond Proximity: Assessing Social Equity in Park Accessibility for Older Adults Using an Improved Gaussian 2SFCA Method

by Yi Huang, Wenjun Wu, Zhenhong Shen, Jie Zhu and Hui Chen

Land 2025, 14(11), 2102; https://doi.org/10.3390/land14112102 (registering DOI) - 22 Oct 2025

Abstract

Urban park green spaces (UPGSs) play a critical role in enhancing residents’ quality of life, particularly for older adults. However, inequities in accessibility and resource distribution remain persistent challenges in aging urban areas. To address this issue, this study takes Gulou District, Nanjing City, as an example and proposes a comprehensive framework to evaluate the overall quality of UPGSs. Furthermore, an enhanced Gaussian two-step floating catchment area (2SFCA) method is introduced that incorporates (1) a multidimensional park quality score derived from an objective evaluation system encompassing ecological conditions, service quality, age-friendly facilities, and basic infrastructure; and (2) a Gaussian distance decay function calibrated to reflect the walking and public transit mobility patterns of the older adults in the study area. The improved method calculates the accessibility values of UPGSs for older adults living in residential communities under the walking and public transportation scenarios. Finally, factors influencing the social equity of UPGSs are analyzed using Pearson correlation coefficients. The experimental results demonstrate that (1) high-accessibility service areas exhibit clustered distributions, with significant differences in accessibility levels across the transportation modes and clear spatial gradient disparities. Specifically, traditional residential neighborhoods often present accessibility blind spots under the walking scenario, accounting for 50.8%, which leads to insufficient accessibility to public green spaces. (2) Structural imbalance and inequities in public service provision have resulted in barriers to UPGS utilization for older adults in certain communities. On this basis, targeted improvement strategies based on accessibility characteristics under different transportation modes are proposed, including the establishment of multi-tiered networked UPGSs and the upgrading of slow-moving transportation infrastructure. The research findings can enhance service efficiency through evidence-based spatial resource reallocation, offering actionable insights for optimizing the spatial layout of UPGSs and advancing the equitable distribution of public services in urban core areas. Full article

(This article belongs to the Special Issue Land, Ageing, Gender and Environment: Problems and Challenges from Different Disciplines II)

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29 pages, 19209 KB

Open AccessArticle

Multi-Scale Spatiotemporal Dynamics of Ecosystem Services and Detection of Their Driving Mechanisms in Southeast Coastal China

by Haoran Zhang, Xin Fu, Jin Huang, Zhenghe Xu and Yu Wu

Land 2025, 14(11), 2101; https://doi.org/10.3390/land14112101 (registering DOI) - 22 Oct 2025

Abstract

Intensive human interference has severely disrupted the natural and ecological environments of coastal areas, threatening ecosystem services (ESs). Meanwhile, the relationships between ESs exhibit certain variations across different spatial scales. Therefore, identifying the scale effects of interrelationships among ESs and their underlying driving mechanisms will better support scientific decision-making for the hierarchical and sustainable management of coastal ecosystems. Therefore, employing the Integrated Valuation of ESs and Tradeoffs (InVEST) model combined with GIS spatial visualization techniques, this investigation systematically examined the spatiotemporal distribution of four ESs across three scales (grid, county, and city) during 2000–2020. Complementary statistical approaches (Spearman’s correlation analysis and bivariate Moran’s I) were integrated to systematically quantify evolving ES trade-off/synergy patterns and reveal their spatial self-correlation characteristics. The geographical detector model (GeoDetector) was used to identify the main driving factors affecting ESs at different scales, and combined with bivariate Moran’s I to further visualize the spatial differentiation patterns of these key drivers. The results indicated that: (1) ESs (except for Water yield) generally increased from coastal regions to inland areas, and their spatial distribution tended to become more clustered as the scale increased. (2) Relationships between ESs became stronger at larger scales across all three study levels. These ESs connections showed stronger links at the middle scale (county). (3) Natural factors had the greatest impact on ESs than anthropogenic factors, with both demonstrating increased explanatory power as the scale enlarges. The interactions between factors of the same type generally yield stronger explanatory power than any single factor alone. (4) The spatial aggregation patterns of ESs with different driving factors varied significantly, while the spatial aggregation patterns of ESs with the same driving factor were highly similar across different spatial scales. These findings confirm that natural and social factors exhibit scale dependency and spatial heterogeneity, emphasizing the need for policies to be tailored to specific scales and adapted to local conditions. It provides a basis for future research on multi-scale and region-specific precision regulation of ecosystems. Full article

(This article belongs to the Special Issue Assessing Habitat Risk from Ecosystem Service Impacts Driven by Land Use and Landscape Pattern Changes)

40 pages, 33354 KB

Open AccessReview

Artificial Intelligence in Urban Planning: A Bibliometric Analysis and Hotspot Prediction

by Shuyu Si, Yeduozi Yao and Jing Wu

Land 2025, 14(11), 2100; https://doi.org/10.3390/land14112100 (registering DOI) - 22 Oct 2025

Abstract

The accelerating global urbanization process has posed new challenges to urban planning. With the rapid advancement of artificial intelligence (AI) technology, the application of AI in urban planning has gradually emerged as a prominent research focus. This study systematically reviews the current state, development trends, and challenges of AI applications in urban planning through a combination of bibliometric analysis using Citespace, AI-assisted reading based on generative models, and predictive analysis via support vector machine (SVM) algorithms. The findings reveal the following: (1) The application of AI in urban planning has undergone three stages—namely, the budding stage (January 1984 to January 2017), the rapid development stage (January 2017 to January 2023), and the explosive growth stage (January 2023 to January 2025). (2) Research hotspots have shifted from early-stage basic data integration and fundamental technology exploration to a continuous fusion and iteration of foundational and emerging technologies. (3) Globally, China, the United States, and India are the leading contributors to research in this field, with inter-country collaborations demonstrating regional clustering. (4) High-frequency keywords such as “deep learning,” “machine learning,” and “smart city” are prevalent in the literature, reflecting the application of AI technologies across both macro and micro urban planning scenarios. (5) Based on current research and predictive analysis, the application scenarios of technologies like deep learning and machine learning are expected to continue expanding. At the same time, emerging technologies, including generative AI and explainable AI, are also projected to become focal points of future research. This study offers a technical application guide for urban planning, promotes the scientific integration of AI technologies within the field, and provides both theoretical support and practical guidance for achieving efficient and sustainable urban development. Full article

(This article belongs to the Section Land Innovations – Data and Machine Learning)

19 pages, 11009 KB

Open AccessArticle

The Application of CA–MLP–ANN in Assessing Urbanisation in Quaternary Catchment X22J of Mpumalanga, South Africa

by Mary Nkosi and Fhumulani I. Mathivha

Land 2025, 14(11), 2099; https://doi.org/10.3390/land14112099 (registering DOI) - 22 Oct 2025

Abstract

Quaternary catchment X22J boasts ecological biodiversity, making ecotourism one of the thriving industries in the catchment. However, recent population growth and the migration from rural areas to urban areas have increased urbanisation. Therefore, this study aimed to assess and predict the trajectory of urban growth. Through the random forest algorithm in Google Earth Engine, this study analysed urban use in 1990, 2007 and 2024. The classification achieved an overall score of 0.89, 0.96 and 0.91 for 1990, 2007 and 2024, respectively. In addition, the Kappa coefficient varied between 0.85, 0.83 and 0.87 for 1990, 2007 and 2024. The CA–MLP–ANN algorithm was applied for the prediction of 2040 urban changes, leading to the model achieving a score of an overall Kappa coefficient of 0.52 and 74% correctness. Overall, the study predicted an increase of 4.01% in built-up areas from 2024 to 2040, maintaining the increasing trend from 1990. Consequently, a loss of 11% was observed in agricultural lands and a loss of 0.17 in waterbodies by 2040. Full article

(This article belongs to the Special Issue Land Use and Land Cover Change Analysis in Dynamic Landscapes)

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