Search Results (11,598)

The concept of the 15 minutes city is increasingly present in the structure of spatial planning for large urban centers, with the main goal of improving quality of life by facilitating access to basic necessities for the population. This study aims to provide an integrated assessment of spatial accessibility for two urban centers that differ in structure and organization, with the main goal of identifying best practices that can be borrowed from one urban center to another in order to streamline sustainable spatial planning based on the strategic concept of the 15 minutes city. The entire research process is based on the development of a completely new and innovative GIS spatial analysis model that will add value to the specialized literature both through the geoinformational approach to the analysis, integration and through the exclusive use the freely available GIS databases (using the OpenStreetMap database), functionally integrated through network analysis and equations weighing the importance of accessibility needs for the population. For the analysis of pedestrian accessibility, in minutes, a total of 4826 locations were used for Cluj–Napoca and 5050 for Pecs, which were structured into 12 subclasses and five main classes (Recreational and Cultural, Public Services and Safety, Education and Health, Commercial, and Public Transport) established in accordance with the main requirements of the 15 minutes city development methodology. The integration of subclasses and accessibility classes was achieved by weighting their importance according to the responses obtained after the implementation of questionnaires to identify the working population’s perception of accessibility in their daily routine. The comparative analysis of the intermediate and final results of the proposed model leads to the establishment of directions and decision-making in the territorial planning process through the transfer of knowledge, solutions, and techniques between the two urban centers to eliminate or reduce negative hotspots and develop a more sustainable urban center in terms of accessibility and as close as possible to a 15 minutes city. Full article

Urban mobility is increasingly affected by air pollution and traffic congestion caused by conventional private vehicles, as well as by insufficient flexibility of public transport. Micromobility devices (MMDs) can mitigate these and other negative impacts on quality of life due to their distinctive characteristics, the significance of which is investigated in this research. To address these challenges facing the modern city, a system of 15 hierarchically unstructured criteria influencing the deployment of MMDs in urban areas was established. The relative weights of these criteria were calculated based on the assessments of 16 experts and the criterion weights were determined using four multi-criteria decision-making (MCDM) methods: ARTIW-L (Average Rank Transformation into Weight—Linear), ARTIW-N (Average Rank Transformation into Weight—Non-Linear), DPW (Direct Percentage Weight), and AHP (Analytic Hierarchy Process). The results indicate that the expert judgments are consistent, as Kendall’s coefficient of concordance 0.406 is 3.8 times greater than the minimum value of 0.106 (at a significance level 0.05 and 14 degrees of freedom). In addition, the consistency ratios (C.R.) calculated from the AHP pairwise comparison matrices were below 0.1. The demonstrated consistency of the expert judgements and the compatibility of all matrices justify adopting the average of the relative weights obtained using the four MCDM methods as the final solution. According to the experts, the most important criteria for MMD deployment are travel safety (0.1336), travel duration (0.1302), the influence of infrastructure quality on comfort (0.0841), impact on health (0.0805), and the cost of purchasing an MMD (0.0713), while the remaining criteria are of lower significance. Based on the research results it is expected that the identified micromobility implementation measures will be useful for decision-makers and urban development planners. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

City–port thresholds are increasingly exposed to multi-risk, including climate change impacts, pollution, and obsolescence of buildings and infrastructure as well as socio-economic marginalization. This paper aims to understand what role co-design—and more generally collaborative planning processes—can play in enabling communities and institutions to learn how to live with risk when managing water, city–port interfaces, and coastal public spaces. To do so, this paper analyses the experience of a co-design workshop held in Castellammare di Stabia, in the Metropolitan Area of Naples, organized within the framework of the research MIRACLE and SPArTaCHus. The results of the workshop show that co-design can act as an effective instrument for developing strategies aimed at the regeneration and valorization of underused, abandoned, or polluted spaces in the coastal thresholds of City–Port areas—wastescapes—that are exposed to multiple risks. In these complex territories new methods are needed to understand, describe and interpret the fuzzy boundaries between the city and the port to collaboratively envision sustainable strategies for urban regeneration of coastal wastescapes. Full article

This study evaluates the ability of three classification procedures to distinguish areas with different levels of atmospheric pollution, based on biomonitoring carried out by analyzing the color and spectral characteristics of mulberry (Morus L.) and linden (Tilia L.) leaves. Sampling was carried out in areas that were grouped into four classes according to the concentrations of fine particulate matter (PM2.5, PM10) and gaseous pollutants (TVOC, NOx, SOx, CO, and eCO₂), measured using a specialized multisensor device. A total of 57 informative features were analyzed, representing indices obtained from two color models (RGB and Lab), as well as from VIS and NIR spectral characteristics measured for the adaxial and abaxial leaf surfaces. The data processing methodology includes feature selection using the ReliefF method and a comparative analysis between two approaches to dimensionality reduction—principal components (PC) and latent variables (LV). The results indicate that data reduction using PC provides significantly higher accuracy and better class separability, regardless of the classifier used, compared to LV, where errors exceed 40%. The comparison between classifiers shows a clear superiority of nonlinear models. While linear discriminant analysis demonstrates low efficiency, quadratic discriminant analysis (Q and DQ) and SVM with radial basis function (RBF) achieve high accuracy of class separability, reaching 100% in the SVM-RBF model for both tree species. The study also reveals functional asymmetry: the adaxial side of the leaves is more informative for spectral indices, while the abaxial side is more sensitive to color changes. The results confirm that the combined optical characteristics obtained from the leaf surface of bioindicators form a reliable method for ecological monitoring of air quality in urban areas. Full article

The call to decolonize our teaching, research, and universities is gaining momentum, and change begins with our everyday actions. In this concept paper, I advance critical reflective praxis—grounded in critical race theory, decolonial thought, and Indigenous studies—as a heuristic for identifying and challenging colonialism, Eurocentrism, racism, and other biases and systems of power across the entire research process, and for moving beyond critique into praxis. I also advance research as a site of praxis, and I argue for reconceptualizing praxis as praxis-in-motion, and for diagnostically evaluating praxis rather than assuming it is inherently ethical. To exemplify the process of critical reflective praxis, I evaluate a travel-based study I conducted about urban health and sustainable development in northeast Thailand that utilized the Moving Worlds Framework (also known as the travelogue methodology), a critical and decolonial approach to research that positions travel as a dynamic condition of knowledge production. In this evaluation, critical reflective praxis is operationalized as a whole-of-process intervention, embedding critical analysis, reflexivity, accountability, and praxis throughout the research process, based on social justice perspectives. My analysis demonstrates how bias can infiltrate research planning, design, methods, representation, and publication, even within decolonial methodological approaches. Critical reflective praxis is proposed as an evaluative and diagnostic tool for evaluating research and praxis. Full article

Urbanization alters the hydrological and structural functioning of tropical urban streams, influencing organic matter transport and retention processes. This study investigated leaf litter retention dynamics in the Bindá Stream in central Amazonia. A six-month leaf release experiment (100 leaves per 12 trial; 1200 leaves total) was conducted alongside hydrological monitoring and floristic surveys of riparian vegetation (adult and regeneration strata). Leaf retention remained consistently low (<33%) across sampling periods. Generalized linear models indicated that flow velocity and discharge were the primary predictors of retention probability, with higher hydrodynamic intensity significantly reducing in-stream storage. Riparian vegetation exhibited moderate structural complexity (Shannon H′ = 1.80; Structural Complexity Index = 3.80), yet limited channel roughness and physical obstructions constrained retention efficiency. Anthropogenic debris locally increased retention, but represents a structurally altered retention mechanism. Hydrodynamic forcing, rather than precipitation totals alone, governed organic matter transport dynamics. Reduced retention capacity suggests limited buffering of downstream material export under high-flow conditions. Although direct water-quality or epidemiological indicators were not measured, findings align with ecohydrological frameworks linking structural simplification and flow flashiness to diminished ecosystem regulation. These results inform riparian restoration and urban stormwater management strategies aimed at enhancing ecosystem regulation and water-quality buffering in tropical cities. Full article

Comamonas testosteroni is an aerobic, Gram-negative bacterium belonging to the class of β-proteobacteria that is naturally present in soils, wastewater and sludge. It has recently gained popularity for its ability to act as a biocatalyst for the degradation of microplastics and other complex organics. Microplastics are globally considered as ubiquitous pollutants due to the increased use of polymers (plastics) which break down over time. In the urban water cycle, the drinking water treatment plants and the wastewater treatment plants are the first and last barriers to microplastics pollution, respectively. While conventional water and wastewater treatment has seen continuous technological improvements in producing cleaner effluents, industry technology adoption for the targeted removal of microplastics has been minimal. Therefore, the treatment of microplastics in soils and wastewater is of growing interest, and understanding C. testosteroni may provide insight into biological treatment and degradation of these pollutants. This review provides a summary of (1) favorable microbiological and environmental properties of C. testosteroni that lend themselves to bioremediation; (2) evidence of the bacterium’s ability to degrade microplastics, steroids, and organic pollutants; (3) implementation potential in the wastewater treatment process train; and (4) challenges and limitations in its application for microplastics biodegradation. Overall, while treatment applications of C. testosteroni through inoculation of media such as soil and wastewater are mentioned, further research into C. testosteroni concentrations found typically at wastewater treatment facilities would be beneficial. Full article

In this paper, we present a curated dataset derived from continuous multi-object tracking observations over a two-year period from a signalized urban intersection in Salzburg, Austria. The dataset includes time-resolved trajectories of multimodal road users, post-processed object attributes, movement relations, and Post-Encroachment Time values computed for a fixed set of eight predefined multimodal traffic conflict scenarios. Moreover, traffic signal data are included and can be used as contextual information. A temporal six-month subset is published via Zenodo including usage examples written in python. The full dataset can be provided on request. Potential applications include traffic safety analysis, behavioral modeling, method development for interaction detection, and educational use in data-driven traffic research. Full article

Mapping land cover, monitoring its changes, and simulating future alterations are essential tasks for sustainable land management. These processes enable accurate assessment of environmental impacts, support informed policymaking, and assist in the planning needed to mitigate risks related to urban expansion, deforestation, and climate change. This study proposes a GeoAI-based framework leveraging Multilayer Perceptron (MLP), a class of Artificial Neural Networks (ANNs), to predict land cover changes in the Aosta Valley region (NW Italy). The model uses Copernicus Earth Observation data, specifically Sentinel-1 and Sentinel-2 imagery, and is trained and validated on land cover maps derived from different time periods previously validated with ground truth data. The objective is to provide a predictive tool capable of simulating potential future landscape configurations, supporting proactive regional land use planning including regulatory constraints under the current land use plan. Model performance is evaluated using accuracy metrics. The land cover classification methodology follows established approaches in the scientific literature, adapted to the specific geomorphological characteristics of the Aosta Valley. To explore and visualize potential future land cover transitions, Sankey and chord diagrams are used in combination with zonal statistics and thematic plots. These provide detailed insights into the intensity, direction, and magnitude of landscape dynamics. Training data were stratified-sampled across the study area, covering a diverse set of land cover classes to ensure robustness and generalization of the MLP model. This GeoAI approach offers a scalable and replicable methodology for anticipating land cover dynamics, identifying vulnerable areas, and informing adaptive environmental management strategies at the regional scale, while simultaneously considering the latest urban planning regulations. Full article

Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling geographic predictors for whether emergency vehicles successfully arrived at incidents in the city of Dallas within the city’s eight-minute benchmark. Using 250,647 incidents and 56 million GPS points along emergency dispatch routes in 2016, we compiled fourteen spatial and operational variables for every incident to train a Bayesian-optimized random forest classifier. The fourteen variables characterized street network topology, roadway attributes, land use, and socioeconomic status, and the model achieved an accuracy of 77.26% in predicting whether emergency response arrived at an incident within eight minutes. A longer distance to dispatch stations, dispatching from non-nearest stations, and low street–network integration were the strongest predictors of unsuccessful responses. Higher-income areas showed slightly elevated unsuccessful rates linked to frequent construction-related disruptions. These findings highlight emergency response as a coupled spatial–operational–temporal process and underscore the need for context-sensitive dispatch strategies and coordinated urban planning. Full article

Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article

Black Modernist architecture offers a powerful yet underexamined pathway for advancing restorative capacity in American cities. This paper argues that Black Modernism functions as a restorative design methodology, addressing social, economic, and ecological harm imposed on Black communities through slavery, racial capitalism, urban renewal, and infrastructural violence. Grounded in the restorative economics framework pioneered by O’Hara, the paper explores the role Black Modernism plays in sustaining sink capacities defined as the social, ecological, and emotional processes that absorb stress, pollution, waste, and trauma. Conventional economic models ignore these capacities, despite their necessity for economic productivity. Black communities, like all marginalized communities, have historically been forced to provide them without compensation. Situating Black Modernist architecture within this framework, the paper demonstrates how Black architects have designed buildings and landscapes that restore dignity, memory, health, and cultural identity, thereby expanding community sink capacities. Drawing on the works of various scholars, the paper examines case studies from Washington, DC, Atlanta, and Chicago, which reveal how Black communities have borne the burden of unremunerated restorative labor while shaping the American built environment. The paper positions Black Modernism as both a design language and a political–economic intervention, challenging architectural value systems that privilege monumental production over community restoration. It concludes by proposing a Restorative Design Framework that integrates Black Modernist principles with restorative economics, offering policy and planning pathways that recognize cultural labor, emotional restoration, and community well-being as essential components of sustainable urban development. Full article

Accurate building roof classification from airborne LiDAR point clouds is fundamental to reliable three-dimensional (3D) urban reconstruction. While supervoxel-based methods offer efficiency and resilience to uneven point density, their performance is critically undermined by cross-boundary segmentation errors—a direct consequence of random seed initialization that merges geometrically similar yet semantically distinct objects. To address this root cause, this study proposes Interior-Anchored Mean-Shift (IAMS), a novel supervoxel segmentation framework that rethinks seed placement as a geometry-aware interior localization problem. By integrating local geometric consistency point density, and spatial correlation into a unified kernel density estimator, supplemented by density-adaptive voxel weighting and a semi-variogram-driven bandwidth, IAMS reliably anchors seeds within object interiors, yielding highly homogeneous supervoxels without post-processing. Extensive experiments on three diverse airborne LiDAR datasets demonstrated that IAMS consistently outperformed state-of-the-art baselines. On the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen benchmark, our approach improved roof classification completeness, correctness, and quality by up to 7.1% (per-object) over the conventional Voxel Cloud Connectivity Segmentation (VCCS) algorithm while being significantly faster than recent boundary-preserving alternatives. Critically, IAMS maintains robust performance under challenging conditions, including sparse sampling and dense vegetation occlusion, making it a practical solution for real-world urban remote sensing. Full article

The dynamics of the processes shaping the quality of rainwater discharged by sewer systems is very complex. The use of hydrodynamic models to simulate surface runoff and the dynamics of changes in pollutants, including heavy metal (HM) concentrations, requires the collection of a lot of data that is difficult to obtain, and model calibration is complex and time-consuming. This paper presents a machine learning model and investigates the possibility of applying data mining methods to simulate changes in the concentrations of selected heavy metals (Ni, Cu, Cr, Zn and Pb) based on rainwater quality studies conducted in three urban catchments located in Kielce, southern Poland, with the aim of developing a model with broader applicability. Simulations of HM content in rainwater were performed using regression and classification trees (RF), neural networks (MLP) and support vector machines (SVMs). The MLP (MAPE ≤ 21.6) and SVM (MAPE ≤ 23.5) methods were shown to have the highest accuracy in simulating HM content. These models produced satisfactory simulation results based on rainfall amount and meteorological conditions, and they had relatively simple model structures and short simulation time. The study demonstrated that the proposed approach provides a transferable tool for estimating HM content in rainwater based on air quality, expressed in terms of visibility, and the type of catchment development. Full article