Urban Land Use Research Articles

Spatiotemporal Analysis of the Coupling Relationship Between Urban Infrastructure and Land Utilization in a Shrinking City: A Case Study of Hegang, Northeast China

Globally, urbanization is accelerating, with China witnessing a significant 40% rise in urbanization rate over the past 4 decades. However, the dynamic changes in the spatial coupling between infrastructure and utilization intensity during the early, middle, and late stages of urbanization are not clear. The trajectory of development and coupling within the urbanization process is crucial for understanding issues such as urban over-saturation and urban shrinkage. Using Hegang in Northeastern China as an example, we utilized high-resolution remote sensing data, examined the construction intensity of urban land use, analyzed the degree of coupling with utilization efficiency, and clarified the dynamic evolution of the binary relationship system between development and coupling. Results show that Hegang’s construction intensity has seen a continuous rise from 1992 to 2000, with a 200.06% increase over 28 years, while its coupling with utilization efficiency has experienced a significant drop in the 21st century, suggesting a persistent decline in the utilization of buildings and a notable urban shrinkage phenomenon. Considering development status and coupling degree, we delineate a characteristic urbanization state curve for Hegang, reflecting its progression through stages of “Underdeveloped, Highly coupled,” to “Underdeveloped, Weakly coupled”, and finally to “Highly developed, Weakly coupled”, offering insights into its urban development path. This research not only establishes a foundational data groundwork for future land-use planning in Hegang but also presents a replicable template for urbanization path analysis in other cities, contributing to a broader understanding of urban development dynamics.

URBAN SATURATION IN ALGERIAN CITIES: FACTORS AND TREATMENT METHODS WITHIN THE FRAMEWORK OF URBAN SUSTAINABILITY: A CASE STUDY OF THE CITY OF TÉBESSA

Throughout history, cities have undergone transformations due to social, urban, environmental, and economic changes, which have contributed to altering their shape, and growth patterns. This evolution has led to continuous urban expansion in response to the increasing needs of the population, resulting in boundless urban sprawl. The constant effort to provide urban services has driven the integration of new land areas within the framework of urban expansion, often at the expense of the surrounding rural areas, which serve as the city's main life source. These rural areas are the primary base for urban expansion and simultaneously the main supplier of labor and raw materials. Currently, urban expansion in Algerian cities, in general, and in Tebessa in particular, poses challenges in urban development and planning. This situation calls for exploring alternative approaches when natural or human barriers prevent the optimal use of urban land in alignment with the city’s needs, leading to urban saturation. These approaches should consider the principles of sustainable urban development and rely on tools focused on protecting natural resources within and around the city, as cities are not only the main threat to the natural environment but also the first victims of its degradation.

Biomagnetic Monitoring of Urban Pollution: The Case of Aburrá Valley, Colombia

This study aims to identify the most polluted areas and sites using the magnetic signal of ornamental plant leaves as an indicator of environmental pollution. Systematic sampling was conducted with 98 sampling sites described according to urban land use, such as road hierarchy and road surface, soil group, collected plant species, and municipality. The magnetic parameters analyzed were low- and high-frequency magnetic susceptibility and the isothermal remanent magnetization acquisition curves in order to calculate the magnetic enhancement factor. For the analysis of variance, a Kruskal–Wallis test was performed to compare urban land uses. Subsequently, the magnetic enhancement factor in dust and surface soil was used to prepare maps of environmental pollution for each urban area. Analyses of the different magnetic parameters of the dust deposited on leaves show that low-coercivity ferrimagnetic minerals dominated the magnetic signal, probably magnetite of anthropic origin, and were closely linked to vehicular traffic and, to a lesser extent, industrial activities.

Similarities and Dichotomies Between Nature-Based Solutions and Green Infrastructure: a Systematic Literature Review

Objective: The study seeks to address the following question: What are the similarities and dichotomies between green infrastructure (GI) and nature-based solutions (NBS) from the perspective of Brazilian researchers, and how do the identified solutions mitigate the depletion of urban ecosystem services? Method: A Systematic Literature Review (SLR) was conducted using the PRISMA protocol in research within the CAPES Journal database. The inclusion and exclusion criteria were original texts, peer-reviewed, published in national and international journals in Portuguese, covering the themes of NBS, GI, informal settlements, and public open space. After reviewing the 90 identified texts, a final selection of 41 texts was made for the SLR. Originality/Value: These texts were analyzed from a novel perspective, bringing together the themes of GI, NBS, ecosystem services protection, and urban land use. Results and Discussion: The study identified the authors’ views on the pressures of urbanization on ecosystem services and the solutions for their protection. It was concluded that the concepts of NBS and GI are often conflated in most approaches and that adopting urbanization associated with nature faces conceptual, operational, and managerial challenges. Research Implications: The research provides insights into the integration of GI and NBS in urban planning to address the challenges of uncontrolled urbanization and the degradation of ecosystem services.

Exploring Spatial Differentiation in Cultural Urban Land Use in China’s Ancient and Emerging Cities: A Comparative Case Study of Xi’an and Shenzhen

Exploring Spatial Differentiation in Cultural Urban Land Use in China’s Ancient and Emerging Cities: A Comparative Case Study of Xi’an and Shenzhen

Spatiotemporal Dynamics and Prediction of Habitat Quality Based on Land Use and Cover Change in Jiangsu, China

Analyzing the spatiotemporal evolution characteristics of urban land use and habitat quality is crucial for the sustainable development of urban ecological environments. This study utilizes the land use data of Jiangsu Province for the years 2000, 2010, and 2020, applying the FLUS model to investigate the driving force behind land expansion and to simulate a prediction for the land use of 2030. By integrating the InVEST model and landscape pattern indices, this study analyzes the spatiotemporal evolution characteristics of habitat quality in Jiangsu Province and uses geographical detector analysis to examine the synergistic effects of influencing factors. The results indicate that, from 2000 to 2020, habitat degradation in Jiangsu Province progressively increased, with the spatial distribution of degradation levels showing a gradual change. Under the ecological protection scenario in 2030, habitat fragmentation was alleviated. Conversely, under the economic development scenario, habitat quality further deteriorated, resulting in the largest area of low-quality regions. Minimal changes occurred under the natural development scenario. (2) The landscape indices in Jiangsu Province experienced significant changes from 2000 to 2020. The continuous expansion of urban land into other land use types led to a trend of fragmentation, with a clear increasing trend in dispersion, sprawl, and Shannon’s diversity index, accompanied by a decrease in cohesion. (3) The dominant interacting factors affecting habitat quality were combinations of socioeconomic factors with other factors, indicating that the economy largely determines the spatial distribution pattern of habitat quality. The findings of this study provide optimization strategies for future spatial planning of land use types in Jiangsu Province and offer references for habitat quality restoration efforts in the region.

Evolving road networks and urban landscape transformation in the Himalayan foothills, India.

The amalgamation of Himalayan and Indo-Burmese biodiversity has made the state of Manipur, India, a unique ecosystem. In addition, the region is a strategic place for the country to establish an economic corridor to Southeast Asia. In recent times, the region has witnessed tremendous infrastructural/road development. Subsequently, forest fragmentation related to urbanization and road expansion has emerged in the Himalayan foothills. The development of roads brought rapid changes in land use and land cover (LULC) and thus subsequent environmental degradation. The current study attempts to understand how the development of road networks has impacted the natural cover in Manipur, India. A spatio-temporal analysis was performed to assess the relationship between the development of road networks and LULC changes using the Landsat satellite images over a decade (2012-2022). The results showed significant changes in the area coverage of LULC categories such as agricultural land, built-up areas, forest, and water bodies with the increase in road density. To have a holistic view, the study area was segregated into three functional zones based on their urban land use pattern, i.e., urban center, peri-urban, and urban peripheral fringes. Urban sprawl in the urban center has led to the rapid conversion of forested lands into built-up areas and agricultural lands in the peri-urban and urban peripheral fringes, respectively. The decline of forest areas to urbanization in peri-urban and urban peripheral fringes calls for conservation and restoration initiatives. The study also emphasizes how different stakeholders can be involved and empowered to strengthen public-private partnerships for conservation and restoration in such sensitive ecosystems. Urban planners and developers should be critical in making informed decisions through understanding ecological concerns in tandem with infrastructural development.

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities.Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Spatial suitability of urban land use models for poverty alleviation in the cities of Nigeria

Many models depicting the spatial dimensions of poverty are based on cities in developed countries, raising questions about their appropriateness for urban spaces in developing countries. This study evaluates the spatial suitability of these models in understanding poverty distribution and informing alleviation efforts related to Sustainable Development Goal (SDG). One in cities of developing nations. A grid sampling technique in ArcGIS 10.8 was used to select 1098 households across 56 wards in three mid-sized Nigerian cities. Household data were collected through structured questionnaires using GeoODK, and Maxar high-resolution imagery was used to assess urban building density. The Foster-Greer-Thorbecke (FGT) poverty indices and Pearson correlation were applied for analysis. Results showed that over 29 wards had a poverty incidence and gap index greater than 0.20, while poverty severity remained below 0.20 in most wards. High building density was more prevalent in the city’s interior. Significant correlations were found between the income poverty gap (p = 0.005, r = 0.367), poverty severity (p = 0.009, r = 0.346), and building density. The study concludes that these models are suitable for informing poverty alleviation policies in mid-sized cities of developing countries, especially Nigeria.

Meadows or asphalt road – Does momentary affective well-being vary with immediate physical environment? Results from a geographic ecological momentary assessment study in three metropolitan areas in Germany

We investigated the association between the immediate physical environment of individuals and individual fluctuations of momentary affective well-being in everyday life with a focus on medium sized metropolitan areas in Europe representing a typical living environment of a large proportion of the world’s population. The sample comprised 365 individuals (54.8 % female) with participants ranging from 14.08 to 88.27 years of age (M = 43.9, SD = 20.9). In an geographic ecological momentary assessment (GEMA), participants were prompted six times a day on at least 9 days over the course of 3 weeks, covering a total of six weekdays and three weekend days on mobile phones to report their momentary affective well-being. Urban land use categories (forest, water, urban green) were assessed with GPS-localization as environmental variables. Additionally, sunshine, rainfall, whether participants were inside or outside as well as whether they were alone or had company were included into the analyses.We used dynamic structural equation modelling to model the inter- and intraindividual differences as well as fluctuations and assess potential covariates while acknowledging the autoregressive nature of affect.The results showed that on individual level, fluctuations of momentary affective well-being were associated with sunshine, having company and travelling. No significant association emerged for urban green, forest, and water neither within individuals nor between. Methodological as well as conceptual implications are discussed and an interpretation of the present findings are provided.

Impact of Short-Haul High Speed Rail Commuting on Land Use and Spatial Structure in a Multi-Centre Metropolitan Area

Owing to the enhanced accessibility afforded by high-speed rail, the relationship between megacities and their neighboring cities has grown closer. Megacities exert a significant influence on the land use of neighboring cities. This study employs a spatial econometric model to investigate the direct impact and spatial spillover effects of high-speed rail on urban land values, based on urban panel data from China’s Yangtze River Delta region (2006-2020). The results revealed that shorter commuting times and improved frequency of service had a more substantial effect. Additionally, due to alterations in land values, there is a distinct preference tendency for urban land use attributes. Therefore, the city’s spatial organization has transformed, and its center of gravity has shifted as well. This study provides crucial theoretical justifications and empirical evidence to fill the gaps left by previous research on the effects of short-haul high-speed rail commuting on urban housing prices and land use. The study also offers valuable insights for strategic planning and implementation aimed at promoting urban development and urban agglomeration construction through HSR construction.

Unlocking the Power of Economic Agglomeration: How Digital Finance Enhances Urban Land Use Efficiency Through Innovation Ability and Rationalization of Industrial Structure in China

Unlocking the Power of Economic Agglomeration: How Digital Finance Enhances Urban Land Use Efficiency Through Innovation Ability and Rationalization of Industrial Structure in China

Urban Land Use Classification Model Fusing Multimodal Deep Features

Urban land use classification plays a significant role in urban studies and provides key guidance for urban development. However, existing methods predominantly rely on either raster structure deep features through convolutional neural networks (CNNs) or topological structure deep features through graph neural networks (GNNs), making it challenging to comprehensively capture the rich semantic information in remote sensing images. To address this limitation, we propose a novel urban land use classification model by integrating both raster and topological structure deep features to enhance the accuracy and robustness of the classification model. First, we divide the urban area into block units based on road network data and further subdivide these units using the fractal network evolution algorithm (FNEA). Next, the K-nearest neighbors (KNN) graph construction method with adaptive fusion coefficients is employed to generate both global and local graphs of the blocks and sub-units. The spectral features and subgraph features are then constructed, and a graph convolutional network (GCN) is utilized to extract the node relational features from both the global and local graphs, forming the topological structure deep features while aggregating local features into global ones. Subsequently, VGG-16 (Visual Geometry Group 16) is used to extract the image convolutional features of the block units, obtaining the raster structure deep features. Finally, the transformer is used to fuse both topological and raster structure deep features, and land use classification is completed using the softmax function. Experiments were conducted using high-resolution Google images and Open Street Map (OSM) data, with study areas on the third ring road of Shenyang and the fourth ring road of Chengdu. The results demonstrate that the proposed method improves the overall accuracy and Kappa coefficient by 9.32% and 0.17, respectively, compared to single deep learning models. Incorporating subgraph structure features further enhances the overall accuracy and Kappa by 1.13% and 0.1. The adaptive KNN graph construction method achieves accuracy comparable to that of the empirical threshold method. This study enables accurate large-scale urban land use classification with reduced manual intervention, improving urban planning efficiency. The experimental results verify the effectiveness of the proposed method, particularly in terms of classification accuracy and feature representation completeness.

Mutual Causality Between Urban Transport Superiority Degree and Urban Land Use Efficiency: Insights from County Cities in Gansu Province Under the Belt and Road Initiative

Exploring the coupled coordination and interaction between urban transport superiority degree (UTSD) and urban land use efficiency (ULUE) is the key to promoting efficient land use in cities and coordinated development. This paper adopts the improved UTSD model, super-efficiency slack-based measure–undesirable output model, coupling coordination degree model (CCDM), panel Granger causality test, random forest model, and the mixed geographically and temporally weighted regression model to reveal the spatial and temporal evolution and coupling characteristics of UTSD and ULUE in Gansu from 2005 to 2020 and to validate and explore the interaction mechanism between UTSD and ULUE. The results show that (1), from 2005 to 2020, the average UTSD in Gansu increased from 0.56 to 1.01 and the Belt and Road Initiative accelerated the construction of the transportation network in Gansu. The average ULUE increased from 0.52 to 0.62; the spatial distribution of ULUE was high in the west and north and low in the east and south. (2) From 2005 to 2020, the average CCDM of UTSD and ULUE in Gansu increased from slightly unbalanced (0.37) to slightly balanced (0.52). A spatially high UTSD and high ULUE agglomeration area can be found along the transportation arteries. (3) The UTSD and ULUE were mutually causal, with the degree of transportation arterial influence degree being the strongest driver of ULUE among the components of UTSD (30.41% contribution) and tax revenue being the strongest driver of UTSD among the components of ULUE (15.10% contribution). Overall, the connotation of ULUE puts forward the demand for improving the transportation infrastructure and, at the same time, provides the guarantee for UTSD upgrading, which in turn affects the ULUE. In the future, the Xinan region of Gansu should prioritize planning and construction of a transportation network. The results of this study can provide a scientific basis for the construction of transportation networks and the efficient use of urban land in Gansu and other regions.

Spatial Politics of Cultural Production: Negotiating Workspaces and Resisting Displacement at Industrial Heritage Sites in Berlin

Derelict industrial spaces have been crucial spatial resources for artists and cultural production for decades, often forming vibrant cultural clusters. However, these spaces are increasingly threatened by speculative real-estate development and displacement through more “productive” creative industries. The case of Alte Münze in Berlin provides empirical insight into the politics, practices, and strategies essential for preserving these heritage sites in the long term for cultural use. This research underscores the need for supportive planning regulations that combine cultural policy with urban planning, advocate for public-civic partnerships, and promote public or community-based ownership models. The findings extend to small-scale manufacturers and businesses facing similar challenges in maintaining workspaces amid competitive urban land use pressures.

A Computational Model for Water Quality Analysis and Assessment in Tanzania

Research on water quality has received much attention in both developing and developed countries. This is because of the fact that, the effects of poor quality of water are detrimental to human beings, animals and the environment. This study is about a computational model for water quality analysis and assessment in Tanzania. Water quality can be understood as the measure of suitability of water based on physical, chemical and biological attributes. Water quality analysis and assessment face several challenges due to population growth, urban land use, agricultural activities, and industrialization. Besides, attempts have been made by the scholars to address the challenges. However, the tools used like titrimetric, electrometric, pH-meter, thermometer and turbidity meter are yet to come up with effective solutions. Because of these, the researcher was compelled to adopt computational model which uses Statistical Analysis System (SAS) software in order to come up with effective solutions concerning water quality analysis and assessment. In this study therefore, the secondary data were collected from Lake Victoria littoral stations under the auspices of the Ministry of Water in Tanzania with the objective to get sufficient information concerning water quality analysis and assessment. Additionally, the collected data were coded in SAS software to analyse independent and dependent variables. SAS software therefore, was employed to obtain central tendency and dispersion as benchmarks in determining quality of water. Also, the Multivariate Linear Regression Model was run to obtain coefficients of estimation, 95% confident limits and p-value. Statistical findings from central tendency and dispersion indicate that, the mean for potential of Hydrogen (pH) was 8.165; for total suspended solids was 3.065 mg/l; chloride displayed a mean of 6.494 mg/l; calcium displayed a mean of 6.421 mg/l; iron had a mean of 0.188 mg/l; magnesium displayed a mean of 3.331 mg/l and sulphate had mean of 2.326 mg/l. Looking closely at all of the above-mentioned water quality parameters, they all align with a Tanzania Bureau of Standards (TBS) and World Health Organization (WHO) as shown on table 1. Findings from the Multivariate linear regression model shows that: First, iron had a p-value of 0.0153, magnesium 0.0347 and total hardness had a p-value of 0.001. All of these were statistically significant in the analysis and assessment of water quality as shown on table 2. The study concludes that, the water quality in Lake Victoria complies with both TBS and WHO standards as explained above.

A Nature-based Solutions Framework for Embedding Climate Change Mitigation and Adaptation into Urban Land Use Plans through Strategic Environmental Assessment (SEA).

Climate change impacts comprise a particular challenge for authorities when reconciling the implications of land use planning decisions. Whilst Strategic Environmental Assessment (SEA) is typically applied to the development of urban land use plans, the selection of mitigation and adaptation strategies for climate change impacts can have knock-on effects on nature. However, Nature-based Solutions (NbSs) could provide an innovative means of addressing climate change mitigation and adaptation without these knock-on effects. The main aim of this research is therefore to propose a conceptual framework for embedding NbSs into the main stages of the SEA process to potentially enhance climate change mitigation and adaptation in urban land use planning. This is achieved through a systematic literature review of academic and grey literature sources, with subsequent content analysis. This study demonstrates the value of matching these manifold NbS approaches to climate change impacts potentially addressed in SEA process stages and suggests how this might be achieved in practice focusing on urban land use plans.

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.

A bottom-up nationwide analysis of sectoral land use reveals spatial heterogeneity across the United States

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

Monitoring and assessing of sustainable development in the urban area of Ouagadougou based on SDG 11.3.1 indicator and the city biodiversity index

Urban growth can induce changes in the use and transformation of urban land. In the context of rapid urbanization, monitoring key indicators of sustainable development is essential. The aim of this study was to monitor and assess the level of sustainable development in the city of Ouagadougou between 2002 and 2022, based on global indicators such as Sustainable Development Goal 11.3.1 indicator and the Singapore City Biodiversity Index. A remote sensing and Geographic Information System approach was used to acquire and analyze open-source geospatial data. The results show an increasing inefficiency in land use, with excessive consumption to population growth until 2013. Between 2013 and 2022, this trend declined, but urban biodiversity nevertheless decreased significantly, with a 40% loss of natural areas between 2002 and 2022. Fragmentation of natural areas, due to infrastructure development, has amplified this degradation, posing ecological risks to the city. This study underlines the importance of strengthening the ecological connectivity of urban spaces to improve their sustainability and resilience. It also recommends reorienting urban strategies by integrating nature-based solutions and placing biodiversity preservation at the heart of sustainable urban development policies.