Search Results (337)

Shared cargo bike services hold significant potential for promoting sustainable urban mobility, yet their adoption remains limited—especially for private, everyday use. This study investigates how such systems can be more effectively integrated into urban transport by identifying key enablers and operationalizing them through a GIS-based multi-criteria analysis (MCA). Using a mixed-methods approach, expert interviews were conducted to explore success factors and barriers. Results highlight the dual function of shared cargo bikes: enabling occasional use while increasing long-term uptake by fostering trial and visibility. The study identifies both spatial and non-spatial enablers. Key spatial factors include high visibility, pedestrian flows, access to public transport and cycling networks, and placement in mixed-use areas. Non-spatial enablers include technical reliability, ease of use, strong visual identity, subsidies, and trial opportunities. The spatial enablers were operationalized into seven criteria in the MCA. Based on qualitative expert interviews and thematic analysis, the highest weights were assigned to visibility and pedestrian flows, followed by proximity to public transport and local centers, while lower weights were given to proximity to residences, population density, and access to cycle paths. The results offer guidance for station placement and demonstrate the role of shared cargo bikes in sustainable urban transport. Full article

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

This study presents a novel methodology for mapping Fault- and Thrust-based Structural Lineaments (FT-SL) in the rugged and inaccessible Oued-Laou watershed of the Rif Belt, Morocco. Combining optical (Landsat-8 OLI, Sentinel-2 MSI) and radar (Sentinel-1 SAR) remote sensing data, the research employs manual, semi-automatic, and automatic extraction methods enhanced by spatial filtering (Sobel, Laplacian, Kuan). A Knowledge-Based System (KBS) integrated with Multi-Criteria Decision Analysis (MCDA) evaluates the effectiveness of these methods, focusing on lineament statistics, orientation, density distribution, and correlation with existing geological maps. The results highlight Sentinel-1 SAR’s superior performance in detecting subsurface structures, while manual extraction yields the highest accuracy. This study also demonstrates the potential for generalizing this approach to other Alpine orogenic regions, such as the Alps, due to shared geological characteristics. The findings provide a robust framework for structural lineament mapping in mountainous terrains, addressing challenges of accessibility and data scarcity. Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

In the developing countries (e.g., Palestine) a reliable assessment of climate vulnerability, exposure, and consequently risk is a key step in developing successful adaptation and mitigation plans. This study aims to examine the spatial distribution of climate risk across the different governorates of the West Bank (Palestine) by assessing climate-risk exposure. A GIS-based Multi-Criteria Decision Analysis approach was employed to estimate climate exposure across the West Bank governorates. Additionally, sensitivity analysis is used to explore the impact of indicator weight on the final climate-risk map. The climate-risk map was subsequently developed based on the exposure map, classifying the governorates into five risk categories: very high, high, moderate, low, and very low. This analysis revealed that 42% of the West Bank population resides in areas classified as having high to very high climate exposure, which corresponds to approximately 39% of the total land area. Conversely, about 21% of the West Bank area is categorized under low to very low risk conditions. By measuring risk based on this exposure, and considering vulnerability, it was determined that 82% of the population lives within areas identified as high to very high zones, underscoring the significant climate risk of populated regions. This study offers the first spatially explicit climate-risk assessment for the West Bank, applying a widely accepted approach that integrates vulnerability and exposure components. The results provide critical insights to inform targeted adaptation and mitigation efforts, supporting decision-makers in enhancing climate resilience across the region. 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

This study applies a multicriteria decision analysis to explore how experts from different backgrounds assess traditional and emerging criteria for urban cycling. A hierarchical model with 7 main criteria and 31 subcriteria was evaluated by 30 specialists from academic, technical, and user-focused groups. Using pairwise comparisons and aggregated judgments, this study reveals points of agreement and divergence among expert priorities. Safety and infrastructure were rated as the most important factors. In contrast, contextual and technological aspects, such as Multimodality, Environmental Quality, Shared Systems, and Digital Solutions, received moderate to lower weights, with differences linked to expert profiles. These results highlight how different disciplinary perspectives influence the understanding of bikeability-related factors. Conceptually, the findings support a broader view of cycling conditions that incorporates both established and emerging criteria. Methodologically, this study demonstrates the value of the Analytic Hierarchy Process (AHP) as a participatory and transparent tool to integrate diverse stakeholder opinions into a structured evaluation model. This approach can support cycling mobility planning and policymaking. Future applications may include case studies in specific cities, combining expert-based priorities with local spatial data, as well as longitudinal research to track changes in cycling conditions over time. Full article

Water and chemical use in textile dye production are exacerbating water pollution and extraction across Dhaka, Narayanganj, and Gazipur in Bangladesh, where these industries are concentrated. However, the ability to cope with water-related challenges is influenced by multiple factors. This study applies descriptive spatial analysis to map textile dye clusters, river pollution, and water insecurity. As vulnerability is multidimensional and fluctuates across subdistricts, this study develops a Water Vulnerability Index (WVI) consisting of 25 indicators across demographics, socioeconomics, gender, health, WASH, and climate dimensions. The index is based on Multidimensional Vulnerability Assessment (MDVA) and constructed through multicriteria analysis (MCA). The study highlights that the Shitalakhya, Turag-Tongi Khal, Buriganga, and Balu Rivers are highly polluted, with average biochemical oxygen demand (BOD), chemical oxygen demand (COD), and dissolved oxygen (DO) levels exceeding safe limits. Central Dhaka is identified as being extremely water insecure, characterized by significant inequalities in water insecurity across subdistricts. The WVI finds that Gazipur Sadar and Kaliakair subdistricts, housing several textile dye factories, face the highest water vulnerability of the 57 subdistricts. This study furthers the case that Dhaka, Narayanganj, and Gazipur host numerous textile hubs, confront serious water challenges, such as river pollution and water insecurity, and are marked by significant spatial disparities in vulnerability. By exploring anthropogenic pollution alongside multidimensional water vulnerability, this study can inform targeted policy responses, such as stricter regulatory limits, more frequent monitoring and enforcement, and tailored support in high-vulnerability areas. Full article

This study proposes an integrated computational framework that combines deep learning-based visual perception analysis with multi-criteria decision making to optimize indoor architectural layouts in terms of both visual coherence and sustainability. The framework initially employs a deep learning method leveraging edge pixel feature recombination to extract critical spatial layout features and determine key visual focal points. A fusion model is then constructed to preprocess visual representations of interior layouts. Subsequently, an evolutionary deep learning algorithm is adopted to optimize parameter convergence and enhance feature extraction accuracy. To support comprehensive evaluation and decision making, an improved Analytic Hierarchy Process (AHP) is integrated with the entropy weight method, enabling the fusion of objective, data-driven weights with subjective expert judgments. This dual-focus framework addresses two pressing challenges in architectural optimization: sensitivity to building-specific spatial features and the traditional disconnect between perceptual analysis and sustainability metrics. Experimental results on a dataset of 25,400 building images demonstrate that the proposed method achieves a feature detection accuracy of 92.3%, surpassing CNN (73.6%), RNN (68.2%), and LSTM (75.1%) baselines, while reducing the processing time to under 0.95 s and lowering the carbon footprint to 17.8% of conventional methods. These findings underscore the effectiveness and practicality of the proposed model in facilitating intelligent, sustainable architectural design. Full article

Hydrologically vulnerable areas should be identified for sustainable urban watershed management, flood mitigation, and climate-resilient infrastructure planning. However, assessing hydrological vulnerability in complex urban environments requires a comprehensive framework that integrates hydrological components and considers spatial heterogeneity. Thus, this study proposes an objective, data-driven method for identifying hydrologically vulnerable areas in urban regions using multicriteria decision-making (MCDM). The MCDM technique is used to rank the hydrological health of subwatersheds in an urbanizing watershed. Entropy-based weights are assigned to key hydrological indicators, which are computed using the soil and water assessment tool. Entropy-based weighting reveals that groundwater-related components contribute more to overall vulnerability than surface runoff. According to initial MCDM analysis, the most vulnerable areas are those in the upper reaches of the watershed, where steep slopes accelerate runoff and limit infiltration. This confounding influence of elevation is addressed by implementing topographic normalization and reevaluating subwatershed vulnerability while controlling for elevation bias. The findings underscore the importance of incorporating both hydrological and topographical factors into urban watershed vulnerability assessment and demonstrate the applicability of entropy-weighted MCDM to complex, data-scarce urban environments. The proposed framework is a replicable decision support tool for prioritizing hydrologically sensitive areas in intervention planning. Full article

In highly urbanised and industrialised settings, managing environmental pressures and enhancing urban resilience demand integrated, spatially explicit approaches. This study presents a methodological framework that integrates topographic data, land cover information, and open geodata to produce a high-resolution vulnerability map. A multi-criteria analysis was performed using indicators such as land use, population density, proximity to emission sources, vegetation cover, and sensitive services (e.g., schools and hospitals). The result is a high-resolution vulnerability map that classifies the urban, peri-urban, and coastal zones into five levels of environmental risk. These evaluation levels are derived from geospatial analyses combining pollutant dispersion modelling with land-use classification, enabling the identification of the most vulnerable urban zones. These findings support evidence-based planning and can guide local governments and environmental agencies in prioritising Nature-based Solutions (NBSs), enhancing ecological connectivity, and reducing exposure for vulnerable populations. Full article

The intensification of anthropogenic pressures, particularly those related to agriculture driven by increasing demands for food and cash crops, generates negative environmental externalities. Assessing these externalities is essential to better identify and implement measures that promote the environmental sustainability of rural landscapes. This study aims to develop a multi-criteria assessment method of the negative environmental externalities of rural landscapes in the northern Benin agricultural basin, based on satellite-derived data. Starting from a 12-class land cover map produced through satellite image classification, the evaluation was conducted in three steps. First, the 12 land cover classes were reclassified into Human Disturbance Coefficients (HDCs) via a weighted sum model multi-criteria analysis based on nine criteria related to the negative environmental externalities of anthropogenic activities. Second, the HDC classes were spatially aggregated using a regular grid of 1 km² landscape cells to produce the Landscape Environmental Sustainability Index (LESI). Finally, various discretization methods were applied to the LESI for cartographic representation, enhancing spatial interpretation. Results indicate that most areas exhibit moderate environmental externalities (HDC and LESI values between 2.5 and 3.5), covering 63–75% (HDC) and 83–94% (LESI) of the respective sites. Areas of low environmental externalities (values between 1.5 and 2.5) account for 20–24% (HDC) and 5–13% (LESI). The LESI, derived from accessible and cost-effective satellite data, offers a scalable, reproducible, and spatially explicit tool for monitoring landscape sustainability. It holds potential for guiding territorial governance and supporting transitions towards more sustainable land management practices. Future improvements may include, among others, refining the evaluation criteria and introducing variable criteria weighting schemes depending on land cover or region. Full article

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

This research aims to identify and prioritize key planning elements for the redevelopment of such housing complexes by incorporating perspectives from both experts (supply-side) and residents (demand-side). To achieve this, a hybrid multi-criteria decision-making framework was developed by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). A total of 25 planning elements were identified through Focus Group Interviews and organized into five domains: legal and institutional reforms, project feasibility, residential conditions, social integration, and complex design. The AHP was used to assess the relative importance of each element based on responses from 30 experts and 130 residents. The analysis revealed a clear divergence in priorities: experts emphasized feasibility and regulatory considerations, while residents prioritized livability and spatial quality. Subsequently, the TOPSIS method was applied to evaluate four real-world redevelopment cases. From the supply-side perspective, Seoul A District received the highest score (0.58), whereas from the demand-side perspective, Gyeonggi D District ranked highest (0.69), illustrating the differing priorities of stakeholders. Overall, Gyeonggi D District emerged as the most favorable option in the combined evaluation. This research contributes a structured and inclusive decision-making framework for the regeneration of public housing. By explicitly comparing and quantifying the contrasting preferences of key stakeholders, it underscores the critical need to balance technical feasibility with resident-centered values in future redevelopment initiatives. Full article

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article