Hasil untuk "Urban groups. The city. Urban sociology"

Bramantyo Eko Prabowo, Fitri Rachmayani, Glenn Adriel Adiguna et al.

Landslides and slope instability events in Indonesia frequently occur during the rainy season. The relationship between rainfall and landslide activity is closely linked to the ability of rainwater to infiltrate the soil, which in turn affects slope stability. The objective of this study is to assess the duration of water infiltration in unsaturated soil conditions. Soil samples were taken from the western region of Indonesia and classified as silty sand (SM). Advanced laboratory testing was conducted to obtain the unsaturated soil properties, including soil water characteristic curve (SWCC), shrinkage curve, unsaturated permeability, and unsaturated shear strength. Few studies have examined the influence of different rainfall durations on seepage and slope stability. In this study, numerical simulations include rainfall application on the ground surface for three different durations over 1 day, that is, 6-h, 12-h, and 24-h simulations. A groundwater table was located at a depth of 5 m from the surface. The simulation results reveal increases in the groundwater level and in pore-water pressure during infiltration. This event reduces the suction force in unsaturated silty sand soil, thereby decreasing the factor of safety (FoS) in slope stability. The most significant decrease in FoS occurs in the 6-h simulation, while the effect on the safety factor in the 24-h simulation is not significant. This occurs due to the high intensity of rain during the shorter rainy period. After the rainy conditions, the factor of safety gradually rises and stabilizes on the sixth day, reaching an FoS of 1.86. This work identifies areas where silt–sand lithology predominates, along with high rainfall intensity and landslide susceptibility, providing important information to guide mitigation measures.

Wei Chen, Yuqian Wu, Junle Chen et al.

Urban systems, as dynamic complex systems, continuously generate spatio-temporal data streams that encode the fundamental laws of human mobility and city evolution. While AI for Science has witnessed the transformative power of foundation models in disciplines like genomics and meteorology, urban computing remains fragmented due to "scenario-specific" models, which are overfitted to specific regions or tasks, hindering their generalizability. To bridge this gap and advance spatio-temporal foundation models for urban systems, we adopt scaling as the central perspective and systematically investigate two key questions: what to scale and how to scale. Grounded in first-principles analysis, we identify three critical dimensions: heterogeneity, correlation, and dynamics, aligning these principles with the fundamental scientific properties of urban spatio-temporal data. Specifically, to address heterogeneity through data scaling, we construct WorldST. This billion-scale corpus standardizes diverse physical signals, such as traffic flow and speed, from over 100 global cities into a unified data format. To enable computation scaling for modeling correlations, we introduce the MiniST unit, a novel split mechanism that discretizes continuous spatio-temporal fields into learnable computational units to unify representations of grid-based and sensor-based observations. Finally, addressing dynamics via architecture scaling, we propose UrbanFM, a minimalist self-attention architecture designed with limited inductive biases to autonomously learn dynamic spatio-temporal dependencies from massive data. Furthermore, we establish EvalST, the largest-scale urban spatio-temporal benchmark to date. Extensive experiments demonstrate that UrbanFM achieves remarkable zero-shot generalization across unseen cities and tasks, marking a pivotal first step toward large-scale urban spatio-temporal foundation models.

Di Mo, Mingyang Sun, Chengxiu Yin et al.

Urban design profoundly impacts public spaces and community engagement. Traditional top-down methods often overlook public input, creating a gap in design aspirations and reality. Recent advancements in digital tools, like City Information Modelling and augmented reality, have enabled a more participatory process involving more stakeholders in urban design. Further, deep learning and latent diffusion models have lowered barriers for design generation, providing even more opportunities for participatory urban design. Combining state-of-the-art latent diffusion models with interactive semantic segmentation, we propose RECITYGEN, a novel tool that allows users to interactively create variational street view images of urban environments using text prompts. In a pilot project in Beijing, users employed RECITYGEN to suggest improvements for an ongoing Urban Regeneration project. Despite some limitations, RECITYGEN has shown significant potential in aligning with public preferences, indicating a shift towards more dynamic and inclusive urban planning methods. The source code for the project can be found at RECITYGEN GitHub.

Irfan Haider Khan, Taiyaba Munawer

PurposeThis study aims to examine the thermal performance of vertical greenery systems (VGS) in composite climates with seasonal fluctuations, focusing on their impact on indoor thermal comfort in naturally ventilated buildings during the monsoon season.Design/methodology/approachA case–control experiment was conducted in Delhi, India, to compare the hygrothermal effects of a direct green facade (GF) against a bare wall in a naturally ventilated residential building. Data were collected throughout the monsoon season to evaluate the impact on surface temperatures, indoor air temperatures and humidity levels.FindingsThe GF reduced surface temperatures by up to 16.6°C and indoor air temperatures by up to 5°C, demonstrating significant cooling benefits. However, it also elevated the indoor humidity to 81%, which influenced the perceived comfort. Despite this, the system extended the thermal comfort hours owing to the reduction in air temperatures, highlighting its potential to enhance indoor thermal conditions in monsoon-dominated regions.Originality/valueThis study addresses a critical gap in the understanding of the dual effects of VGS on temperature and humidity in composite climates, specifically during high-humidity monsoon seasons. It provides empirical evidence of the benefits and challenges of implementing GFs in naturally ventilated residences, offering insights into their role in urban sustainability and thermal comfort. These findings advocate region-specific research and strategic integration of VGS into urban design to optimize their effectiveness across diverse climatic conditions.

Nikita Sharikov, Polina Poliakova, Arsenii Kudryavtsev

The article addresses the issue of spatial differentiation in the socio-economic development of Thailand’s provinces in the context of the national «Thailand 4.0» strategy. The research problem arises from the limited empirical evaluation of regional heterogeneity that integrates demographic, sectoral, and institutional dimensions. The objective is to identify structural patterns of provincial development and to propose a typology that may serve as a basis for differentiated regional policy. The study relies on provincial-level indicators for 2010 2021, including per capita gross regional product, labor migration, industrial investment, land use, and inbound tourism. The Williamson coefficient was applied to quantify inequality, revealing its growth over the past decade. Clustering was performed using k-means, hierarchical agglomerative methods, and DBSCAN in Python with scikit-learn. The k-means algorithm with three and four clusters produced the most robust results, isolating Bangkok as a distinct cluster. Three persistent groupings were identified: industrial centers in the central region, agricultural provinces of the northern and northeastern areas, and tourism-driven provinces in the south. The analysis also revealed β-convergence processes in several transitional provinces, suggesting gradual alignment of development trajectories. Policy recommendations emphasize modernization of agriculture, innovation support for industrial centers, and infrastructure projects in tourism-intensive provinces. The findings confirm the persistence of spatial polarization and highlight the utility of cluster analysis as a tool for refining Thailand’s regional development strategy.

Wenfeng Zhan, Benjamin Bechtel, Huilin Du et al.

Escalating urban heat, driven by the convergence of global warming and rapid urbanization, is a profound threat to billions of city dwellers. The science directing urban heat adaptation is strongly influenced by studies that use satellite-based land surface temperature (LST), which is readily available globally and address data gaps in cities, particularly in the Global South. LST, however, is a poor surrogate for near-surface air temperature, physiologically relevant human thermal comfort, or direct human heat exposure. This flawed practice leads to issues for several downstream use cases by inflating adaptation benefits, distorting the magnitude and variability of urban heat signals across scales, and thus misguiding urban adaptation policy. We argue that satellite-based LST must be treated as a distinct indicator of surface climate, which, though relevant to the urban surface energy budget, can be frequently decoupled from human-relevant thermal impacts especially during daytime. Only by a disciplined application of this variable, combined with complementary datasets, process-based and data-driven models, as well as interdisciplinary collaboration, can urban adaptation design and policy be effectively advanced.

Abdul Saboor, Zhuangzhuang Cui, Evgenii Vinogradov et al.

Accurate Probability of Line-of-Sight (PLoS) modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing PLoS models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined built-up parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban LoS Simulator (ULS) to model PLoS for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical PLoS for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate PLoS results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks.

Kerry A. Nice, Mark Stevenson

To measure access to social services (primary health care, early childhood care/education, and public transport), we create a social service access index (SSI) for Australian capital cities. We show that only two, Melbourne and Sydney, have some limited characteristics of a compact or 15-minute city, but only in city centres and inner cities where population densities are highest and have less low density housing types. In the outer suburban and peri-urban areas as well as across all of the remaining cities, proximity to social services is poor and residents suffer the consequences of spatial inequity.

Zhonghang Li, Lianghao Xia, Xubin Ren et al.

Urban computing has emerged as a multidisciplinary field that harnesses data-driven technologies to address challenges and improve urban living. Traditional approaches, while beneficial, often face challenges with generalization, scalability, and contextual understanding. The advent of Large Language Models (LLMs) offers transformative potential in this domain. This survey explores the intersection of LLMs and urban computing, emphasizing the impact of LLMs in processing and analyzing urban data, enhancing decision-making, and fostering citizen engagement. We provide a concise overview of the evolution and core technologies of LLMs. Additionally, we survey their applications across key urban domains, such as transportation, public safety, and environmental monitoring, summarizing essential tasks and prior works in various urban contexts, while highlighting LLMs' functional roles and implementation patterns. Building on this, we propose potential LLM-based solutions to address unresolved challenges. To facilitate in-depth research, we compile a list of available datasets and tools applicable to diverse urban scenarios. Finally, we discuss the limitations of current approaches and outline future directions for advancing LLMs in urban computing.

T. Kobashi, R. C. Mouli, J. Liu et al.

Urban decarbonization is central to meeting global climate goals, yet progress toward integrated low-carbon energy systems remains slow. The SolarEV City Concept, linking rooftop photovoltaics with electric vehicles as mobile storage offers a technically robust pathway for deep CO2 reduction, potentially meeting 60-95 percent of municipal electricity demand when deployed synergistically. Despite rapid global growth of PVs and EVs, integration through bidirectional Vehicle-to-Home and Vehicle-to-Grid systems has lagged, revealing a persistent SolarEV paradox. This review examines that paradox through a socio-technical framework across four dimensions, technology, economics, policy, and society. Cross-national comparison shows that while technical feasibility is well established, large-scale implementation is limited by fragmented charging-protocol standards, immature and often non-profitable V2G business models, regulatory misalignments between energy and transport sectors, and social-equity barriers that restrict participation mainly to high-income homeowners. Emerging national archetypes from Japans resilience-driven model to Europes regulation-first trajectory highlight strong path dependence in current integration strategies. The analysis concludes that advancing SolarEV Cities requires a shift from parallel PV-EV promotion toward coordinated policy frameworks, interoperable digital infrastructure, and inclusive market designs that distribute economic and resilience benefits more equitably. Achieving this integrated energy transition will require strategic collaboration among researchers, governments, industries, and communities to build adaptive, resilient, and socially just urban energy systems.

Nina Kreibig

Das Sterben ist eine alltägliche Praxis, nicht nur in den urbanen Zentren. Doch zeigen sich aus geschichtswissenschaftlicher Perspektive erhebliche Desiderate. Obgleich seit einigen Jahren eine „neue Sichtbarkeit des Todes“ postuliert wird, verweist diese neue Beschäftigung vielfach auf Vorstellungen über den Tod und das Sterben und weniger auf Realitäten des Todes. Der folgende Beitrag fokussiert auf die architektonischen und institutionellen städtischen Strukturen, die als Topoi des Todes mit Tod und Sterben verbunden sind. Unter dem Schlagwort „Recht auf Stadt“ wird an dieser Stelle auf Ansprüche, aktuelle Transformationen und historische Prozesse verwiesen und damit die Frage aufgeworfen, welchen Stellenwert die Thematik in unserer heutigen Gesellschaft einnimmt.

Zhuo Xu, Xiao Zhou

The explosion of massive urban data recently has provided us with a valuable opportunity to gain deeper insights into urban regions and the daily lives of residents. Urban region representation learning emerges as a crucial realm for fulfilling this task. Among deep learning approaches, graph neural networks (GNNs) have shown promise, given that city elements can be naturally represented as nodes with various connections between them as edges. However, many existing GNN approaches encounter challenges such as over-smoothing and limitations in capturing information from nodes in other regions, resulting in the loss of crucial urban information and a decline in region representation performance. To address these challenges, we leverage urban graph structure information and introduce a hierarchical graph pooling process called Coarsened Graph Attention Pooling (CGAP). CGAP features local attention units to create coarsened intermediate graphs and global features. Additionally, by incorporating urban region graphs and global features into a global attention layer, we harness relational information to enhance representation effectiveness. Furthermore, CGAP integrates region attributes such as Points of Interest (POIs) and inter-regional contexts like human mobility, enabling the exploitation of multi-modal urban data for more comprehensive representation learning. Experiments on three downstream tasks related to the UN Sustainable Development Goals validate the effectiveness of region representations learned by our approach. Experimental results and analyses demonstrate that CGAP excels in various socioeconomic prediction tasks compared to competitive baselines.

Jiahui Jin, Yifan Song, Dong Kan et al.

Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Existing work pays limited attention to the fine-grained functional layout semantics in urban regions, limiting their ability to capture transferable knowledge across regions. Further, inadequate handling of the unique features and relationships required for different downstream tasks may also hinder effective task adaptation. In this paper, we propose a $\textbf{G}$raph-based $\textbf{U}$rban $\textbf{R}$egion $\textbf{P}$re-training and $\textbf{P}$rompting framework ($\textbf{GURPP}$) for region representation learning. Specifically, we first construct an urban region graph and develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of entity interactions. This model pre-trains knowledge-rich region embeddings using contrastive learning and multi-view learning methods. To further refine these representations, we design two graph-based prompting methods: a manually-defined prompt to incorporate explicit task knowledge and a task-learnable prompt to discover hidden knowledge, which enhances the adaptability of these embeddings to different tasks. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our framework.

Zongrong Li, Yunlei Su, Hongrong Wang et al.

Urban Building Exteriors are increasingly important in urban analytics, driven by advancements in Street View Imagery and its integration with urban research. Multimodal Large Language Models (LLMs) offer powerful tools for urban annotation, enabling deeper insights into urban environments. However, challenges remain in creating accurate and detailed urban building exterior databases, identifying critical indicators for energy efficiency, environmental sustainability, and human-centric design, and systematically organizing these indicators. To address these challenges, we propose BuildingView, a novel approach that integrates high-resolution visual data from Google Street View with spatial information from OpenStreetMap via the Overpass API. This research improves the accuracy of urban building exterior data, identifies key sustainability and design indicators, and develops a framework for their extraction and categorization. Our methodology includes a systematic literature review, building and Street View sampling, and annotation using the ChatGPT-4O API. The resulting database, validated with data from New York City, Amsterdam, and Singapore, provides a comprehensive tool for urban studies, supporting informed decision-making in urban planning, architectural design, and environmental policy. The code for BuildingView is available at https://github.com/Jasper0122/BuildingView.

Angelica Orozco-Cejudo, Mireya Alicia Rosas-Lusett, María López de Asiain-Alberich

El presente trabajo tiene por objetivo determinar estrategias bioclimáticas adecuadas para el clima de Tampico y comprobar si fueron aplicadas en la vivienda media construida en la época del auge petrolero en la ciudad. Mediante la caracterización climática de Tampico y la revisión de recomendaciones realizadas por autores de arquitectura bioclimática, se establecen las estrategias aplicables al clima local.  Se realiza un catálogo de viviendas de la época con características bioclimáticas, obteniéndose acceso a cinco. Mediante entrevistas a los usuarios, se buscó conocer su percepción sobre el confort interior y para profundizar, se estudiaron exhaustivamente las viviendas en cuanto a la existencia o no de estrategias bioclimáticas. Se concluye que dichas viviendas sí cuentan con estrategias bioclimáticas adecuadas para el clima y que eran las mismas que las utilizadas en las viviendas de la época de referencia (auge petrolero) y que han ayudado en la mejora del confort interior de los edificios. Utilizarlas actualmente ayudará a minorar el calentamiento interior, el uso excesivo de energías no renovables y los altos costos por consumo energético.

Shengjie Hu, Zhenlei Yang, Sergio Andres Galindo Torres et al.

Urban land growth presents a major sustainability challenge, yet its growth patterns and dynamics remain unclear. We quantified urban land evolution by analyzing its statistical distribution in 14 regions and countries over 29 years. The results show a converging temporal trend in urban land expansion from sub-country to global scales, characterized by a coherent shift of urban area distributions from initial power law to exponential distributions, with the consequences of reduced system stability and resilience, and increased exposure of urban populations to extreme heat and air pollution. These changes are attributed to the increased influence from external economies of scale associated with globalization and are predicted to intensify in the future. The findings will advance urban science and direct current land urbanization practices toward sustainable development, especially in developing regions and medium-size cities.

Ami Wallis

<p><b>Increases in urban density, the need for lower carbon solutions and a developed understanding of the importance of nature in cities have highlighted the importance of indoor environments and the need to rethink food production methods. This research thesis seeks to question whether hydroponic indoor urban agriculture could be part of the solution. By developing effective architectural solutions for indoor urban agriculture, it explores the integration of indoor greenery as a union of the built and natural which offers opportunities for improvements in urban food supply and contributions to occupant wellbeing.</b></p> <p>Existing research indicates that the benefits from plants in building interiors could be extensive and research in this area is still developing. This work established the potential effectiveness of food production within apartments through a comparative analysis of existing interior applications of urban agriculture, concluding that kitchen hydroponic systems may be the most effective solution for indoor growing of edible plants. The research has investigated the use of natural materials to facilitate exploring methods of making with ceramics to reduce material toxicity.</p> <p>The work explores the integration of a functional model of food production into the interior realm of residential architecture. This is proposed through creating an architectural intervention to support urban agriculture, employ natural materials and integrate biophilic design.</p>

Conrad M Albrecht, Chenying Liu, Yi Wang et al.

We present and evaluate a weakly-supervised methodology to quantify the spatio-temporal distribution of urban forests based on remotely sensed data with close-to-zero human interaction. Successfully training machine learning models for semantic segmentation typically depends on the availability of high-quality labels. We evaluate the benefit of high-resolution, three-dimensional point cloud data (LiDAR) as source of noisy labels in order to train models for the localization of trees in orthophotos. As proof of concept we sense Hurricane Sandy's impact on urban forests in Coney Island, New York City (NYC) and reference it to less impacted urban space in Brooklyn, NYC.

Mauricio Hernández-Bonilla, Karla Lorena Lozano-Merino

En diversos foros especializados en el área de arquitectura y urbanismo se ha debatido sobre la calidad y las condiciones de habitabilidad tanto de la vivienda como del entorno inmediato ante el llamamiento al confinamiento debido a la pandemia generada por el COVID 19. Para el caso mexicano el “quédate en casa”, es una de las principales estrategias para mantenerse a salvo y evitar un posible contagio, pero la pregunta que surge es ¿Hasta qué punto nuestra vivienda y entorno urbano inmediato cuentan con las condiciones para poder permanecer en estos por tiempo prolongado? En este artículo, exponemos los resultados de una investigación realizada a través de una encuesta de opinión a los habitantes de la ciudad de Xalapa, Veracruz. Los resultados son reveladores con relación a la valoración del entorno habitacional y la vivienda en el contexto de la emergencia sanitaria por parte de sus usuarios, puesto que ha quedado en evidencia la existencia de deficiencias relevantes en cuanto a la habitabilidad en las viviendas y su entorno inmediato y en materia de espacio público, seguridad, cohesión social e incluso en la propia convivencia social dentro y fuera de las vivienda.

Mireia Yurrita, Arnaud Grignard, Luis Alonso et al.

As cities become increasingly populated, urban planning plays a key role in ensuring the equitable and inclusive development of metropolitan areas. MIT City Science group created a data-driven tangible platform, CityScope, to help different stakeholders, such as government representatives, urban planners, developers, and citizens, collaboratively shape the urban scenario through the real-time impact analysis of different urban interventions. This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice that will constitute the first step in the development of a dynamic incentive system for an open interactive governance process. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives that aim to promote urban characteristics such as equality, diversity, walkability, and efficiency. The calibration and validation of the model have been performed in a well-known geographic area for the Group: Kendall Square in Cambridge, MA.