Search Results (1,874)

Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical resilience, decarbonization, and social/community engagement. The framework conceptualizes metropolitan and building scales as analytically distinct but operationally linked, allowing strategies to reflect the different systems, stakeholders, and capacities at each level. It also outlines a three-phase progression—Initial (assessment and goal setting), Readiness (planning and implementation), and Steady-State (monitoring and iterative adjustment)—to support staged, adaptive deployment. Each phase includes sample metrics and SMART goals that can be tailored to local context and tracked over time. By integrating theoretical insights with practical implementation tools, the framework offers a flexible yet rigorous approach for advancing urban sustainability. It emphasizes the importance of aligning technical interventions with institutional capacity and community participation to enhance effectiveness and equity. This model contributes to both planning theory and applied sustainability efforts by providing a structured pathway for cities to enhance climate readiness across systems and scales. Full article

High-rise building facades offer an alternative site for installing photovoltaic panels, which are traditionally placed on rooftops. The unique spatial configuration of high-rise buildings, characterized by a small footprint area relative to their height, supports the application of vertical facades for this purpose. Photovoltaic panels installed in these areas not only generate electricity but also enhance the aesthetic dimension of the urban landscape. The proposed methodology uses the EnergyPlus software to simulate the electricity generation of photovoltaic panels mounted on the walls of high-rise buildings in the city of Kragujevac, Serbia. A technical, economic, and environmental analysis was conducted for two scenarios: (1) photovoltaic panels installed on two facade areas with the highest solar potential, and (2) photovoltaic panels installed on all four available facade areas. In Scenario 1, the annual reduction in electricity consumption, annual cost savings in electricity consumption, and investment payback period range from 13 to 38%, 11 to 31%, and 8.4 to 10.6 years, respectively. In Scenario 2, these values range from 23 to 58%, 18 to 47%, and 10.9 to 12.9 years, respectively. The results indicate that southeast and southwest facades consistently achieve higher levels of electricity generation, underscoring the importance of prioritizing high-performing orientations rather than maximizing overall surface coverage. The methodology is particularly efficient for analyzing the solar potential of numerous buildings with comparable shapes, which is a characteristic commonly found in Eastern European architecture from the late 20th century. The study demonstrates the applicability of the proposed methodology as a practical and adaptable tool for assessing early-stage solar potential and providing decision support in urban energy planning. The approach addresses the identified methodological gap by offering a low-cost, flexible framework for assessing solar potential across diverse urban contexts and building typologies, while significantly simplifying the modeling process. Full article

The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence. Full article

Spatial accessibility is a key principle in urban studies, shaping how people reach amenities and services across cities. While most research concentrates on large metropolitan areas and central urban services, small and medium-sized cities and their main amenities remain less studied. To bridge this gap, this study explores spatial accessibility to public amenities in relation to population density in Odense, a medium-sized city known for its compact layout and robust infrastructure supporting walking, cycling, and public transport. Despite Odense’s proactive planning and multimodal transport network, marked accessibility inequalities still exist, especially in peripheral neighbourhoods. This research uses a data-driven approach combining network-based travel time analysis with grid-cell-based spatial visualisation. Additionally, a multi-criteria accessibility scoring framework is introduced, including indicators such as amenity density, diversity of services, temporal thresholds for walking and cycling, and population distribution. The results show an uneven accessibility landscape, with significant gaps in outer districts, highlighting the limitations of uniform planning thresholds. By applying spatial analytical principles, the study uncovers embedded socio-spatial inequalities in everyday urban access. These insights offer practical guidance for planners and policymakers, underscoring the importance of context-sensitive multimodal infrastructure and decentralised service provision to support sustainable urban growth. Full article

Within the context of urban sustainability, the renewal and activation of communities have received growing attention. Public art, as a common approach to community revitalization, has long been regarded as an effective means of addressing urban and community issues. Basic human senses serve as a bridge between residents and community spaces, offering an effective entry point for creating human-oriented spaces. This study addresses the challenge of insufficient spatial vitality in community spaces by examining how sensory interventions can enhance residents’ participation in public art and thereby contribute to the revitalization of communities. To guide this inquiry, a theoretical framework was constructed based on sensory marketing theory and the Stimulus–Organism–Response (SOR) model, focusing on three core dimensions: sensory stimuli, perceptual responses, and behavioral intention. The study further investigated the relationship between public art and residents’ willingness to participate through five types of sensory stimuli, using a measurement scale and Structural Equation Modeling (SEM), with eight public art installations in Shanghai serving as case references. It also assessed the relative strength of each effect. Participant interviews and non-participatory observations were subsequently conducted for validation and supplementary analysis. The results show that residents’ participation willingness in community public art is directly influenced by perceptual responses (emotional fluctuations, cognitive memory, and physiological responses), and indirectly influenced by different sensory stimuli. Cognitive memory, shaped mainly by olfactory and visual stimuli, emerged as the most important factor in encouraging participation. Participation willingness also varies across generations, and different sensory stimuli are associated with distinct participation patterns. Based on empirical data from Shanghai’s community activation practices, the study proposes implementation strategies guided by the Theory of Planned Behavior (TPB) to enhance spatial vitality, promote community activation, and support sustainable development. Full article

Urban planning in post-industrial cities is often limited by stakeholders’ understanding of the potential for intensifying mixed industrial and cultural uses, and the benefits to each of their co-location. In Brussels, differences in language and governance pose additional challenges to actors trying to bring together diverse stakeholders to cooperatively plan for urban regeneration. As part of a wider action research project on regeneration of the city, five co-design workshops were held across Brussels in 2017–2019 and interviews with participants were conducted. Later, in 2023, the researchers returned to the city to assess the practical outcomes of the project. This paper explores the role of the workshops in harnessing the untapped potential for integrating and expanding mixed industrial and cultural uses, and highlights the factors required to inspire and motivate diverse stakeholders to drive sustainable innovation and growth. Full article

This study focuses on the challenges of resident mobility in low-density areas. Amid China’s rapid urbanization, rural landscapes and travel patterns are undergoing significant transformation. Using Lanzhou’s rural areas as a representative case study, this research employs questionnaire surveys to collect data. It applies a multi-nominal logit (MNL) model to examine factors influencing travel mode choices and utilizes structural equation modeling (SEM) to assess travel satisfaction—a composite metric derived from residents’ subjective evaluations of convenience, cost, time, and comfort. Findings indicate that private cars and public transportation are the primary travel modes. The MNL model reveals that age and destination accessibility significantly influence travel choices. SEM path analysis further shows that annual household income has a direct positive effect on satisfaction, while age exerts an indirect negative influence through mediating variables. Female satisfaction levels were significantly lower than those of males. Both road density and perceived infrastructure quality significantly enhanced satisfaction, while destination accessibility may exert a slight negative indirect effect by increasing travel expectations. The study theoretically enriches research on rural travel patterns and provides practical insights into rural transportation planning and infrastructure development. Full article

Small landslides, though frequent, are often overlooked despite their significant potential impact on human-affected areas. This study presents an analysis of the Bella Orxeta landslide in Alicante, Spain, a rotational landslide event that occurred in March 2017 following intense and continued rainfall. Utilizing multitemporal datasets, including LiDAR from 2009 and 2016 and drone-based photogrammetry from 2021 and 2023, we generated high-resolution digital terrain models (DTMs) to assess morphological changes, estimate displaced volumes of approximately 3500 cubic meters, and monitor slope activity. Our analysis revealed substantial mass movement between 2016 and 2021, followed by relatively minor changes between 2021 and 2023, primarily related to fluvial erosion. This study demonstrates the effectiveness of UAV and DTM differencing techniques for landslide detection, volumetric analysis, and long-term monitoring in urbanized settings. Beyond its scientific contributions, the Bella Orxeta case offers pedagogical value across academic disciplines, supporting practical training in geomorphology, geotechnical assessment, GIS, and risk planning. It also highlights policy gaps in existing territorial risk plans, particularly regarding the integration of modern monitoring tools for small-scale but recurrent geohazards. Given climate change projections indicating more frequent high-intensity rainfall events in Mediterranean areas, the paper advocates for the systematic documentation of local landslide cases to improve hazard preparedness, urban resilience, and geoscience education. Full article

With the rapid urban aging trend in China, evaluating the age-friendliness of street environments is critical for inclusive urban planning. This study proposes the Age-Friendly Environment Assessment Tool (AFEAT) to assess street-level age-friendliness in high-density urban contexts, grounded in the World Health Organization’s (WHO) Global Age-Friendly Cities: A Guide and adapted to the spatial characteristics of Nanjing’s Qinhuai District. By integrating multi-source data such as street-view image segmentation, Point of Interest (POI)-based network accessibility, kernel density estimation, Analytic Hierarchy Process (AHP)-derived indicator weights, and Random Forest regression, the study develops a comprehensive and spatialized evaluation framework. The results reveal significant spatial disparities in age-friendliness across street segments, with Safe Mobility, Healthcare Services, and Walkable Environment identified as the most influential factors for older adults. High-performing areas are concentrated in the central urban core, while peripheral zones face challenges such as poor walkability, insufficient lighting, and a lack of facilities. The study recommends strengthening a walkability-based age-friendly safety and healthcare support system and optimizing the spatial distribution of recreational and medical facilities to address mismatches between supply and demand. These findings provide practical guidance for targeted, evidence-based interventions aimed at fostering equitable and resilient urban environments for aging populations. Full article

Forecasting pedestrian congestion in urban back streets is challenging due to “shadow areas” where CCTV coverage is absent and trajectory data cannot be directly collected. To address these gaps, we propose the Peak-aware Graph-attention Temporal Fusion Transformer (PGTFT), a lightweight hybrid model that extends the Temporal Fusion Transformer by integrating a non-parametric attention-based Graph Convolutional Network, a peak-aware Gated Residual Network, and a Peak-weighted Quantile Loss. The model leverages both physical connectivity and functional similarity between roads through a fused adjacency matrix, while enhancing sensitivity to high-congestion events. Using real-world trajectory data from 38 CCTVs in Anyang, South Korea, experiments show that PGTFT outperforms LSTM, TFT, and GCN-TFT across different sparsity settings. Under sparse 5 m neighbor conditions, the model achieved the lowest MAE (0.059) and RMSE (0.102), while under denser 30 m settings it maintained superior accuracy with standard quantile loss. Importantly, PGTFT requires only 1.54 million parameters—about half the size of conventional Transformer–GCN hybrids—while delivering equal or better predictive performance. These results demonstrate that PGTFT is both parameter-efficient and robust, offering strong potential for deployment in smart city monitoring, emergency response, and transportation planning, as well as a practical approach to addressing data sparsity in urban sensing systems. Full article

Rapid urbanization and climate change demand innovative green solutions in city planning. Tiny forests—small artificial wooded areas in urban or peri-urban settings—are gaining attention. This paper explores the use of the Miyawaki method to establish such forests in Italy, highlighting their environmental and educational benefits. The study defines micro-forests (100–200 m²) and mini-forests (200–2000 m²) per legislative standards and describes the qualitative features needed for self-sustaining ecosystems. Mimicking natural succession, these forests support biodiversity, reduce urban heat, improve air quality, and act as carbon sinks. Beyond ecological functions, they offer strong pedagogical value, fostering naturalistic intelligence and reconnecting people with natural rhythms and ecosystems. Case studies from Vigevano and Rome show practical applications, demonstrating how tiny forests can enhance sustainability, community well-being, and environmental awareness in cities. Full article

Given that the increasing non-agricultural conversion of cultivated land (NACCL) endangers food security, studying the spatial and temporal variation characteristics and driving mechanisms of NACCL in Sichuan Province can offer a scientific foundation for developing local farmland preservation measures and controlling further conversion. Guided by the theoretical framework of land use transition, this study utilizes land use datasets spanning multiple periods between 2000 and 2023. Comprehensively considering population scale factors, natural geographical factors, and socioeconomic factors, the county-level annual NACCL rate is calculated. Following this, the dynamic evolution and underlying driving forces of NACCL across 183 counties in Sichuan Province are examined through temporal and spatial dimensions, utilizing analytical tools including Nonparametric Kernel Density Estimation (KDE) and the Geographical Detector model with Optimal Parameters (OPGD). The study finds that: (1) Overall, NACCL in Sichuan Province exhibits phased temporal fluctuations characterized by “expansion—contraction—re-expansion—strict control,” with cultivated land mainly being converted into urban land, and the differences among counties gradually narrowing. (2) In Sichuan Province, the spatial configuration of NACCL is characterized by the expansion of high-value agglomerations alongside the dispersed and stable distribution of low-value areas. (3) Analysis through the OPGD model indicates that urban construction land dominates the NACCL process in Sichuan Province, and the driving dimension evolves from single to synergistic. The findings of this study offer a systematic examination of the spatiotemporal evolution and underlying drivers of NACCL in Sichuan Province. This analysis provides a scientific basis for formulating region-specific farmland protection policies and supports the optimization of territorial spatial planning systems. The results hold significant practical relevance for promoting the sustainable use of cultivated land resources. Full article

Understanding how urban environmental features shape physical activity is crucial for building health-supportive cities, especially under climate change pressures such as rising temperatures and extreme weather. Previous studies emphasized density and accessibility, but the spatial mechanisms driving facility usage remain understudied. This study investigates how land use diversity, the distribution of physical activity facilities, street network structure, and road accessibility shape physical activity behaviours at the neighbourhood scale. Using a 500 m × 500 m grid framework in Xiamen, China, a random forest model combined with Shapley Additive Explanations (SHAP) is employed to quantify the importance of environment indicators. The results demonstrate that road accessibility and street connectivity exert the strongest influence on physical activity facility use, followed by land use diversity and 15 min reachable residential Points of Interests (POIs). Spatial autocorrelation and cluster analysis further reveal that high-impact areas are concentrated in central and southern zones, whereas peripheral regions face accessibility deficits. These findings highlight the value of integrating transport planning and land use configuration to address spatial disparities in facility usage. The study contributes a replicable methodological framework and provides practical insights for advancing equitable and activity-friendly neighbourhood design. Full article

The cooling effect of urban forests has been widely investigated to support climate-adaptive spatial planning. However, studies on the impacts of key landscape drivers have often produced conflicting results, limiting their practical applicability. These inconsistencies may stem from an oversimplified focus on the global effects of individual factors, while neglecting non-linear threshold behaviors and pairwise interactions. To address this gap, this study employed an interpretable machine learning framework (XGBoost-SHAP) to quantify the seasonal non-linearities, thresholds, and interaction effects of landscape drivers on urban forest cooling in Suzhou, a subtropical Chinese city. The results indicate that the combined explanatory power of neighboring water body proportion (NWP), neighboring green space proportion (NGP), vegetation density (NDVI), spatial characteristics (Area, SHAPE), and elevation on the cooling intensity of urban forest patches was strongest in summer (R² = 0.615) and weakest in winter (R² = 0.316). Among these, NWP, NGP, and NDVI were the dominant drivers, while patch area and shape exhibited weaker marginal effects. NWP significantly enhances cooling only after exceeding seasonal critical thresholds (11%–15%). NGP contributed positively above ~40% in warm seasons but suppressed cooling above 37% in winter. Patch area exhibits a logarithmic relationship with cooling intensity, with a critical threshold of approximately 2.48 ha and saturation thresholds between 12 and 14 ha. SHAPE exerted positive effects in spring and winter, negative effects in summer, and a transition from negative to positive in autumn. Notably, significant, threshold-modulated interactions were identified, including those between NDVI and NWP, SHAPE and NDVI, SHAPE and NGP, NWP and NDVI, NWP and NGP, and NGP and NDVI. In each interaction, the first factor regulates and reverses the effect of the second once specific thresholds are exceeded. This study provides actionable, evidence-based guidance for the planning and optimized design of urban forests. Full article

Urbanization poses growing threats to archaeological heritage sites embedded within cities, necessitating innovative monitoring and documentation strategies. This study investigates the use of Unmanned Aerial Vehicle (UAV) photogrammetry for mapping and 3D modelling of urban archaeological landscapes, focusing on the Byzantine-era Didymoteicho Fortress in northern Greece. High-resolution aerial imagery was captured and processed into an orthophoto mosaic and a detailed 3D model of the site’s monuments and their urban surroundings. The UAV-based survey provided comprehensive, up-to-date spatial data that traditional ground methods could not easily achieve in dense urban settings. The results illustrate how UAV mapping can document complex heritage structures, detect risks (such as structural deterioration or encroachment by development), and inform preservation efforts. The discussion situates these findings within global heritage management practices, highlighting UAV technology as a cost-effective, accurate, and non-invasive tool for safeguarding cultural heritage in urban areas. The suggested methodology enhances heritage documentation and risk assessment, demonstrating strong potential for policy integration and proactive conservation planning in historic cities. Full article