Search Results (92)

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological data to generate timely, actionable recommendations for conservation interventions. These may include pre-emptive shutter closure during heatwaves, activation of ventilation under elevated humidity, or intensified monitoring of structurally sensitive zones during heavy precipitation. By coupling historical datasets with real-time telemetry and calibrated predictive models, GEPReS addresses the distinctive vulnerabilities of heritage structures, which arise from material sensitivity, conservation constraints, and operational limitations under contemporary climatic conditions. The architecture combines spatial analysis, typology-aware risk assessment, and reproducible modelling practices to ensure interpretability and compliance with conservation principles. Designed for scalability and online implementation, the system provides a modular framework capable of adapting to diverse building typologies and resource environments. The paper details the system architecture, data sources, modelling approach, and implementation challenges, supported by empirical evidence from multi-site pilot deployments. Full article

This work illustrates a machine learning methodology to forecast pipe failure frequencies in drinking water systems to enhance asset management and operational planning. Three supervised regression models—Random Forest Regressor (RFR), Extreme Gradient Boosting (XGB), and Multi-Layer Perceptron (MLP)—were developed and evaluated using historical failure data from Malatya, Türkiye. The primary predictive variables identified were pipe diameter, pipe type, pipe age, and seasonal average ambient air temperature. The MLP demonstrated superior performance compared to the other models, attaining the lowest RMSE (1.48) and the highest R² (0.993) with respect to the training data, effectively capturing the nonlinear characteristics and failure patterns. The MLP was validated using two datasets from 24 District Metered Areas (DMAs) in Sakarya and Kayseri, Türkiye. The model’s anticipated failure frequencies exhibited strong concordance with the observed failure frequencies, even in regions of elevated failure density, indicating the model’s proficiency in identifying high-risk locations and facilitating the prioritization of maintenance activities. The work demonstrates the potential of machine learning in water infrastructure management. It emphasizes the importance of employing a hybrid method with Geographic Information Systems (GISs) in future research to enhance forecast accuracy and spatial analysis. Full article

Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable transportation in Indian urban neighborhoods, with ‘Accessibility’ identified as a crucial subtheme. Recent advancements in Geographic Information Systems (GISs) and urban data analysis tools have enabled accessibility assessments of parks and playgrounds at a neighborhood scale, yet the OSMnx approach has been only marginally explored and compared in the literature. This study addresses this gap by comparing three tools—the Quantum Geographic Information System (QGIS), OSMnx, and Space Syntax—for accessibility assessments of parks and playgrounds in Ward 60 of Kalyan Dombivli city, based on the 15-Minute City concept. Accessibility was evaluated using 25 m and 100 m grid resolutions under peak and non-peak conditions across public and private transportation modes. The findings show that QGIS offers highly consistent results at micro-scale (25 m), while OSMnx provides better accuracy at coarser scales (100 m+). The results were validated with space syntax through integration and choice values. The comparison highlights spatial disparities in accessibility across different tools and transportation modes, including Intermediate Public Transport (IPT), which remains underexplored despite its crucial role in last-mile connectivity. The presented approach can support municipal authorities in optimizing neighborhood mobility and is transferable for applying the SusTAIN framework in other urban contexts. Full article

The Dhaka District is experiencing rapid industrial growth alongside uncontrolled urban expansion, leading to significant land-use conflicts and environmental pressures. This study investigates how to identify the optimal sites for industrial development that support sustainable urban growth by leveraging Geographic Information Systems (GISs), combined with a structured decision-making approach. The analysis incorporates key environmental and infrastructural factors to guide responsible planning aligned with global sustainability objectives. This study integrates spatial variables such as transport accessibility, land use, environmental sensitivity, and infrastructure presence. Up-to-date satellite imagery and land-use information from recent years ensure relevant and precise analysis. The findings indicate that roughly 10–15% of Dhaka District is suitable for industrial activities, predominantly the western and northwestern edges of the district. However, a considerable portion of existing industries are situated outside the officially designated zones, with nearly 9% infringing on protected environments, pointing to gaps in land management policies. Additionally, industrial expansion resulted in the conversion of over thousands of hectares of natural land, underscoring urgent ecological concerns. Scenario modeling further demonstrates how strategic land allocation can balance industrial growth with environmental conservation. This research highlights the value of integrating a GIS with multi-criteria evaluation using Analytical Hierarchy Process (AHP) to provide a flexible, data-driven framework for sustainable industrial land-use planning. Full article

This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial analysis tasks. We designed and implemented a system capable of interpreting natural language instructions provided by users and translating them into automated GIS workflows through dynamically generated Python scripts. An interactive graphical user interface (GUI), built using CustomTkinter, was developed to enable intuitive user interaction with GIS data and processes, reducing the need for advanced programming or technical expertise. We conducted an empirical evaluation of this approach through a comparative case study involving typical GIS tasks such as spatial data validation, data merging, buffer analysis, and thematic mapping using urban datasets from Pesaro, Italy. The performance of our automated system was directly compared against traditional manual workflows executed by 10 experienced GIS analysts. The results from this evaluation indicate a substantial reduction in task completion time, decreasing from approximately 1 h and 45 min in the manual approach to roughly 27 min using our LLM-driven automation, without compromising analytical quality or accuracy. Furthermore, we systematically evaluated the system’s factual reliability using a diverse set of geospatial queries, confirming robust performance for practical GIS tasks. Additionally, qualitative feedback emphasized improved usability and accessibility, particularly for users without specialized GIS training. These findings highlight the significant potential of integrating LLMs into GISs, demonstrating clear advantages in workflow automation, user-friendliness, and broader adoption of advanced spatial analysis methodologies. Full article

Urban sprawl is a growing issue in developing countries such as Jordan, where urban populations continue to expand rapidly and are projected to reach 70% of the global population by 2050. This urbanization creates significant challenges, particularly the depletion of natural resources and the reduction in green areas. This study proposes an approach to improve the selection of open green space locations by integrating Geographic Information Systems (GISs) with Nature-based Solutions (NbSs) for urban sustainability and resilience. Using Al-Sarih, Jordan, as a case study, GIS was applied to analyze environmental factors, including soil, meteorological, and geological data, through a weighted overlay analysis to assess potential park sites. The results indicated that most parks are situated in areas with suitable geological and soil conditions. However, their distribution is uneven, with dense coverage in the northern region and limited availability in southern and western parts. This imbalance highlights the need for equitable green space planning to ensure accessibility for all residents. This study underscores the value of integrating GIS and NbS in optimizing green infrastructure, providing a scientific framework for sustainable urban planning. It further emphasizes the importance of spatial and natural data interactions to support resilient city development. Full article

Heavy metal contamination of aquatic systems represents a critical environmental and public health concern due to the persistence, toxicity, and bioaccumulative potential of these elements. Geographic information systems (GISs) have emerged as indispensable tools for the spatial assessment and management of heavy metals (HMs) in water resources. This review systematically synthesizes current research on GIS applications in detecting, monitoring, and modeling heavy metal pollution in surface and groundwater. A bibliometric analysis highlights five principal research directions: (i) global research trends on GISs and heavy metals in water, (ii) occurrence of HMs in relation to World Health Organization (WHO) permissible limits, (iii) GIS-based modeling frameworks for contamination assessment, (iv) identification of pollution sources, and (v) health risk evaluations through geospatial analyses. Case studies demonstrate the adaptability of GISs across multiple spatial scales, ranging from localized aquifers and river basins to regional hydrological systems, with frequent integration of advanced statistical techniques, remote sensing data, and machine learning approaches. Evidence indicates that concentrations of some HMs often surpass WHO thresholds, posing substantial risks to human health and aquatic ecosystems. Furthermore, GIS-supported analyses increasingly function as decision support systems, providing actionable insights for policymakers, environmental managers, and public health authorities. The synthesis presented herein confirms that the GIS is evolving beyond a descriptive mapping tool into a predictive, integrative framework for environmental governance. Future research directions should focus on coupling GISs with real-time monitoring networks, artificial intelligence, and transdisciplinary collaborations to enhance the precision, accessibility, and policy relevance of heavy metal risk assessments in water resources. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

The article examines the role of Geographic Information Systems (GIS) as a tool for environmental management and for the planning and development of renewable energy sources (RES). Based on a review of the literature, it is demonstrated that GIS support key managerial functions, including planning, monitoring, decision-making, and communication, by enabling comprehensive spatial analysis and the integration of environmental data. The study emphasizes the importance of GIS in facilitating a systemic and interdisciplinary approach to environmental governance. The paper examines how GIS can help with environmental management, specifically in locating high-risk areas and strategically placing energy investments. Examining GIS’s organizational, technological, and legal facets, it emphasizes how it is increasingly collaborating with cutting-edge decision-support technologies like artificial intelligence (AI), the Internet of Things (IoT), remote sensing, and big data. The analysis emphasizes how GIS help achieve sustainable development’s objectives and tasks. Full article

Wetlands are natural ecosystems of great ecological and economic value. They provide undeniable ecosystem services that contribute to promoting sustainable development. Exposed to different pressures, these limnic ecosystems are particularly vulnerable to climate change. Thus, assessing wetland vulnerability is of utmost importance. Based on a systematic selection of relevant peer-reviewed studies, this paper helps to develop a general vision of the methods used to assess wetland vulnerability in different contexts, emphasizing the use of advanced computational approaches. Hence, an overview of different cases of wetlands all across the five continents and of different types of habitats is presented. Whether the wetland is permanently or seasonally flooded, coastal, or tropical, this study enables the analysis of diverse, already established vulnerability evaluation index systems. Some of these indices were computed using geographic information systems (GISs), artificial intelligence (AI), machine learning (ML), spatial principal component analysis (SPCA) and driver–pressure–state–impact–response (DPSIR) as evaluation models. Indeed, given the adoption of different methods, diverse models, and analytical approaches under different scenarios, the vulnerability assessment process should be seen as an iterative rather than a definitive process. An accurate wetland vulnerability assessment is essential for ensuring the sustainability of wetland ecosystems and for informing effective conservation and management strategies. Full article

Drones have rapidly emerged as transformative tools in landscape research, enabling high-resolution spatial data acquisition, real-time environmental monitoring, and advanced modelling that surpass the limitations of traditional methodologies. This scoping review systematically explores and synthesises the technological applications of drones within the context of landscape studies, addressing a significant gap in the integration of Uncrewed Aerial Systems (UASs) into environmental and spatial planning disciplines. The study investigates the typologies of drone platforms—including fixed-wing, rotary-wing, and hybrid systems—alongside a detailed examination of sensor technologies such as RGB, LiDAR, multispectral, and hyperspectral imaging. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, a comprehensive literature search was conducted across Scopus, Web of Science, and Google Scholar, utilising predefined inclusion and exclusion criteria. The findings reveal that drone technologies are predominantly applied in mapping and modelling, vegetation and biodiversity analysis, water resource management, urban planning, cultural heritage documentation, and sustainable tourism development. Notably, vegetation analysis and water management have shown a remarkable surge in application over the past five years, highlighting global shifts towards sustainability-focused landscape interventions. These applications are critically evaluated in terms of spatial efficiency, operational flexibility, and interdisciplinary relevance. This review concludes that integrating drones with Geographic Information Systems (GISs), artificial intelligence (AI), and remote sensing frameworks substantially enhances analytical capacity, supports climate-resilient landscape planning, and offers novel pathways for multi-scalar environmental research and practice. Full article

Community-scale biomass power plants (CSBPPs) offer a decentralized approach for electricity generation by utilizing locally available biomass while delivering socioeconomic benefits. Site selection plays a critical role in the success of CSBPPs and requires the consideration of diverse spatial and non-spatial factors. This study presents a spatial decision-support tool for identifying suitable CSBPP sites in Thailand’s Eastern Economic Corridor (EEC), which comprises the Chachoengsao, Chonburi, and Rayong provinces. A geoprocessing workflow integrating Geographic Information Systems (GISs), Multi-Criteria Decision-Making (MCDM), and the Analytic Hierarchy Process (AHP) was developed using ModelBuilder tools in ArcGIS Pro (version 3.0.2). Thirteen sub-criteria related to geographical, infrastructural, and socioeconomic–cultural dimensions, along with exclusion zones, were evaluated by 15 experts from diverse stakeholder groups. Biomass availability from five major economic crops was combined with other spatial data layers, incorporating expert-assigned weights and suitability scores. The findings indicated a remaining biomass energy potential was 34,156 TJ, with sugarcane residues contributing over 80%. Approximately 20% of the EEC area (about 0.262 million hectares) was classified as highly suitable for CSBPP development, revealing several viable site options. The proposed model offers a flexible and replicable framework for regional biomass planning and can be adapted to other locations by adjusting the criteria and integrating optimization techniques. Full article

The spatial distribution mechanism of industrial agglomeration has long been a central topic in urban economic geography. With the increasing availability of street view imagery and built environment data, effectively integrating multi-source spatial information to identify key drivers of firm clustering has become a pressing research challenge. Taking Shanghai as a case study, this paper constructs a street-level Built Environment (BE) database and proposes an interpretable spatial analysis framework that integrates SHapley Additive exPlanations with Multi-Scale Geographically Weighted Regression. The findings reveal that: (1) building morphology, streetscape characteristics, and perceived greenness significantly influence firm agglomeration, exhibiting nonlinear threshold effects; (2) spatial heterogeneity is evident in the underlying mechanisms, with localized trade-offs between morphological and perceptual factors; and (3) BE features are as important as macroeconomic factors in shaping agglomeration patterns, with notable interaction effects across space, while streetscape perception variables play a relatively secondary role. This study advances the understanding of how micro-scale built environments shape industrial spatial structures and offers both theoretical and empirical support for optimizing urban industrial layouts and promoting high-quality regional economic development. Full article

This paper examines Krivelj, a copper mining village in Serbia, as a critical yet overlooked node within global extractive networks. Despite supplying copper essential for renewable energy and sustainable architecture, Krivelj experiences severe ecological disruption, forced relocations, and socio-spatial destabilization, becoming a “sacrifice zone”—an area deliberately subjected to harm for broader economic interests. Employing a hybrid methodology that combines ethnographic fieldwork with Geographic Information Systems (GISs), this study spatializes narratives of extractive violence collected from residents through walking interviews, field sketches, and annotated aerial imagery. By integrating satellite data, legal documents, environmental sensors, and lived testimonies, it uncovers the concept of “slow violence,” where incremental harm occurs through bureaucratic neglect, ambient pollution, and legal ambiguity. Critiquing the abstraction of Planetary Urbanization theory, this research employs countertopography and forensic spatial analysis to propose a counter-cartographic framework that integrates geospatial analysis with local narratives. It demonstrates how global mining finance manifests locally through tangible experiences, such as respiratory illnesses and disrupted community relationships, emphasizing the potential of counter-cartography as a tool for visualizing and contesting systemic injustice. Full article