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

Lin Chen, Fengli Xu, Esteban Moro et al.

Cities thrive on social interactions that foster well-being, innovation, and prosperity; yet, exogenous shocks such as pandemics, hurricanes, and wildfires can severely disrupt them. Different urban venues exhibit widely divergent response patterns, raising key questions about what factors contribute to these differences and how we can anticipate and respond. Understanding these questions is crucial for safeguarding social resilience, the capacity of urban venues to maintain both visitation and diversity. In this study, we analyze large-scale human mobility data from 15 US cities covering more than 103 million residents across three distinct urban shocks. Despite a general trend of declining visitation and weakened social mixing, 36.28%-53.01% of venues exhibit reduced segregation, and 21.04%-38.55% of venues exhibit increased visitation. By constructing a mobility network interlinking types of urban venues, we reveal that eigenvector network centrality tends to indicate the provision of essential services and robustly predicts social resilience across varied urban shocks. Specifically, centrality elevates the explanatory power by more than 80% in predicting both segregation and mobility change, compared with more intuitive features. Furthermore, compared to peripheral venues, core venues featuring shorter visit distances, broader neighborhood visitation, shorter visitor dwell times, and steadier popularity throughout the day. Such patterns imply a dual social mechanism: core venues sustain social ties through frequent informal interaction, while peripheral ones facilitate deeper engagement around specialized interests and their corresponding social circles. By bridging urban mobility research with economic theories that distinguish staple from discretionary products, we propose a well-and-pool analogy that suggests how people spend their varying urban mobility budgets.

Martina Garcia de Cezar, Séverine Tomas, Bruno Cheviron et al.

Accurately modelling urban microclimates is essential for developing effective mitigation strategies against urban overheating. This study assesses the potential of ANSYS Fluent to simulate an experimental urban canyon with vegetated planters, using three different simulation methods. The analysis focuses on accuracy, operational suitability, and an improved understanding of the physical mechanisms operating at the scale of an urban canyon. Numerical results related to radiative, thermal, and aerodynamic fluxes, are evaluated based on (i) experimental data obtained from a dense network of sensors and (ii) the physical consistency obtained in the spatial distribution of the variables analysed. Despite some discrepancies in spatial and temporal variations, the model demonstrated strong agreement with experimental data, with absolute errors in air temperature and relative humidity below 3 % on average (maximum 11 %). Radiation, as the most sensitive factor for daytime thermal comfort variation in the study area, highlights the importance of improving radiative exchange in the proposed models. While certain software limitations require user-defined functions, such as representation of average radiant temperature, thermal comfort indices and multiple vegetation heat source terms, the study underscores the tool’s capacity to generate detailed and high-resolution microclimate data. This rich numerical database improves our understanding of urban heat dynamics, paving the way for more efficient urban climate solutions.

Turgut Kalay, Ayşenur Kandemir

This study examines how Starbucks, as a global brand, employs glocalization strategies in its interior design by incorporating local architectural elements into standardized global formats. The primary objective is to analyze how local and global elements are integrated into the design of the second-wave coffee shops, a globally recognized brand, and to evaluate the balance between these elements from a design perspective. To achieve this, six Starbucks stores, selected as the sample for the current study, located in the EMEA (Europe, Middle East, and Africa), Asia-Pacific, and Japan regions were examined. The research focused on key interior design components, including vertical structural elements, flooring, ceiling features, openings, furniture, and decorative details, assessing them within the framework of local and global characteristics. A qualitative research approach was adopted, employing content analysis to systematically evaluate the selected stores. The originality of this study lies in its structured examination of how glocalization strategies manifest in interior spaces through regional variations. The findings reveal that local elements such as ceiling types, materials, and seating styles were consistently integrated to reflect regional identities, while standardized lighting and furniture layouts preserved the brand's global coherence. Thus, the findings show that integrating local motifs into spatial design enables global brands to establish a meaningful connection with the local cultural context while maintaining their global identity. This balance not only enhances user experience but also reinforces brand identity through culturally resonant spaces. Overall, the research underscores the significance of preserving cultural codes in interior design as a means of fostering commercial success for global brands. By offering a framework for incorporating glocalization strategies in spatial design, this study provides valuable insights for designers and brands aiming to achieve cultural sustainability while maintaining a strong global presence.

Modan Tailleur, Pierre Aumond, Vincent Tourre et al.

Urban noise maps and noise visualizations traditionally provide macroscopic representations of noise levels across cities. However, those representations fail at accurately gauging the sound perception associated with these sound environments, as perception highly depends on the sound sources involved. This paper aims at analyzing the need for the representations of sound sources, by identifying the urban stakeholders for whom such representations are assumed to be of importance. Through spoken interviews with various urban stakeholders, we have gained insight into current practices, the strengths and weaknesses of existing tools and the relevance of incorporating sound sources into existing urban sound environment representations. Three distinct use of sound source representations emerged in this study: 1) noise-related complaints for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3) guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations, which should use indicators adapted to the target audience, and enable data accessibility.

Gustavo Moreira, Maryam Hosseini, Carolina Veiga et al.

Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke applications that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio's flexibility in tackling pressing societal challenges. Curio is available at https://urbantk.org/curio.

Jiaxin Zhang, Yunqin Li, Tomohiro Fukuda et al.

Measuring urban safety perception is an important and complex task that traditionally relies heavily on human resources. This process often involves extensive field surveys, manual data collection, and subjective assessments, which can be time-consuming, costly, and sometimes inconsistent. Street View Images (SVIs), along with deep learning methods, provide a way to realize large-scale urban safety detection. However, achieving this goal often requires extensive human annotation to train safety ranking models, and the architectural differences between cities hinder the transferability of these models. Thus, a fully automated method for conducting safety evaluations is essential. Recent advances in multimodal large language models (MLLMs) have demonstrated powerful reasoning and analytical capabilities. Cutting-edge models, e.g., GPT-4 have shown surprising performance in many tasks. We employed these models for urban safety ranking on a human-annotated anchor set and validated that the results from MLLMs align closely with human perceptions. Additionally, we proposed a method based on the pre-trained Contrastive Language-Image Pre-training (CLIP) feature and K-Nearest Neighbors (K-NN) retrieval to quickly assess the safety index of the entire city. Experimental results show that our method outperforms existing training needed deep learning approaches, achieving efficient and accurate urban safety evaluations. The proposed automation for urban safety perception assessment is a valuable tool for city planners, policymakers, and researchers aiming to improve urban environments.

Fabio Miranda, Thomas Ortner, Gustavo Moreira et al.

Urbanization has amplified the importance of three-dimensional structures in urban environments for a wide range of phenomena that are of significant interest to diverse stakeholders. With the growing availability of 3D urban data, numerous studies have focused on developing visual analysis techniques tailored to the unique characteristics of urban environments. However, incorporating the third dimension into visual analytics introduces additional challenges in designing effective visual tools to tackle urban data's diverse complexities. In this paper, we present a survey on visual analytics of 3D urban data. Our work characterizes published works along three main dimensions (why, what, and how), considering use cases, analysis tasks, data, visualizations, and interactions. We provide a fine-grained categorization of published works from visualization journals and conferences, as well as from a myriad of urban domains, including urban planning, architecture, and engineering. By incorporating perspectives from both urban and visualization experts, we identify literature gaps, motivate visualization researchers to understand challenges and opportunities, and indicate future research directions.

Nada Samir Farag, Gehan Elsayed Abd eldayem, Ahmed Saleh Abd Elfatah

Cities confront massive issues like Disasters, climate change, urbanization, population growth, and economic growth; it is necessary to reduce their impact to the minimum possible. To accomplish this, A smart, resilient society intended to manage cities using Big Data, the Internet of Things (IoT), and intelligent information technologies to improve the ability to resist, absorb, and adapt to external changes resulting in urban resilience. Beyond that, constructing a smart, resilient city is a more advanced strategy for reducing vulnerabilities to emergencies like the COVID-19 pandemic and natural disasters like earthquakes and tsunamis. This study proposes a conceptual design for smart resilience cities and explores how a system can improve risk reduction and adaptation approaches and natural disaster recovery. Using various examples, the various states how smart cities' characteristics help cities be more resilient to disasters. The paper explains the differences and similarities between a smart city and a resilient city.

Oksana Radchenko, Leonid Tulush, Serhii Leontovych

State security is the main guideline of state policy in the face of global challenges. For Ukraine, it is especially relevant, because during the period of the russian-Ukrainian war, its foundations and essence experience significant deformations. Since the risks and threats to national security have increased enormously in Ukraine under martial law, its financial component should be formed according to the tools corresponding to the challenges, even ahead of them, since, according to analysts, modern war is a war of finances. The problems faced by the state, the banking system, financial and commodity markets and institutions, corporations and households need new financial instruments to ensure flexibility in financing strategic goals. As of September, the losses of the Ukrainian economy from the war are estimated according to various estimates, from USD 105 billion, or 70% of the average annual GDP over the past 5 years, to USD 600 billion, and this exceeds the level of GDP in 2021 by 3 times. This actualizes the needs of the scientific study of financial instruments with the aim of effective state regulation and equalization in the face of limited and increasing losses of human and material resources, changes in the direction and speed of financial flows, their sources, structure, reproduction and reservation. The study examines financial instruments of a predominantly budgetary direction, as well as the components of national indicators of financial security. It is also important to analyze the share of the state in the economy, the size of which determines the speed of response and the completeness of resistance due to a threat to national security. To achieve the goals of the study, the main legally established risks of financial instruments of the national economy during the period of martial law are systematized. The indicators of the financial security of the state for the period of hybrid and military aggression of the rf (2013-2021) were assessed, and according to open sources of data, which are rather limited, a forecast of these indicators for 2022 was made. On the basis of the Financial Stability Report of the National Bank of Ukraine, the budget innovations of the period of martial law are analyzed. The sources of financing the state budget for the period of the legal regime of martial law and its main directions for 2023 are summarized. It is concluded that the financial system of Ukraine in a short time managed to organize financial flows in accordance with the needs of ensuring national security, form an optimal balance of resources, maintain the volume of financing of basic budget expenditures, attract donor resources and resist the inevitable decline of the economy during the war. The role of donor countries of economic and military assistance, in particular Latvia, in deterring military aggression and ensuring the stability of Ukraine's financial policy was emphasized.

Sanna Ala-Mantila, Antti Kurvinen, Aleksi Karhula

Abstract As a result of the ongoing urbanization megatrend, cities have an increasingly critical role in the search for sustainability. To create sustainable strategies for cities and to follow up if they induce desired effects proper metrics on the inter and intra-urban development is needed. In this paper, we analyze the sustainability development in the 20 largest cities in Finland through a residential area classification framework. The results based on high-quality register data show concerning trends in some sustainability measures, and divergent trends between cities and residential areas within. Overall, while densities have increased modestly, we see no clear signs of decreasing car ownership rates. Further, also manifestations of social sustainability seem to be insufficient in many locations–especially in residential mid-rise areas from the '60s and '70s, and '80s and '90s.

Bibandhan Poudyal, Gourab Ghoshal, Alec Kirkley

The spatial configuration of urban amenities and the streets connecting them collectively provide the structural backbone of a city, influencing its accessibility, vitality, and ultimately the well-being of its residents. Most accessibility measures focus on the proximity of amenities in space or along transportation networks, resulting in metrics largely determined by urban density alone. These measures are unable to gauge how efficiently street networks can navigate between amenities, since they neglect the circuity component of accessibility. Existing measures also often require ad hoc modeling choices, making them less flexible for different applications and difficult to apply in cross-sectional analyses. Here we develop a simple, principled, and flexible measure to characterize the circuity of accessibility among heterogeneous amenities in a city, which we call the pairwise circuity (PC). The PC quantifies the excess travel distance incurred when using the street network to route between a pair of amenity types, summarizing both spatial and topological correlations among amenities. Measures developed using our framework exhibit significant statistical associations with a variety of urban prosperity and accessibility indicators when compared to an appropriate null model, and we find a clear separation in the PC values of cities according to development level and geographic region.

Ruisheng Wang, Shangfeng Huang, Hongxin Yang

Urban modeling from LiDAR point clouds is an important topic in computer vision, computer graphics, photogrammetry and remote sensing. 3D city models have found a wide range of applications in smart cities, autonomous navigation, urban planning and mapping etc. However, existing datasets for 3D modeling mainly focus on common objects such as furniture or cars. Lack of building datasets has become a major obstacle for applying deep learning technology to specific domains such as urban modeling. In this paper, we present a urban-scale dataset consisting of more than 160 thousands buildings along with corresponding point clouds, mesh and wire-frame models, covering 16 cities in Estonia about 998 Km2. We extensively evaluate performance of state-of-the-art algorithms including handcrafted and deep feature based methods. Experimental results indicate that Building3D has challenges of high intra-class variance, data imbalance and large-scale noises. The Building3D is the first and largest urban-scale building modeling benchmark, allowing a comparison of supervised and self-supervised learning methods. We believe that our Building3D will facilitate future research on urban modeling, aerial path planning, mesh simplification, and semantic/part segmentation etc.

Yuxiang Li, Jens-Christian Svenning, Weiqi Zhou et al.

Heat extremes are projected to severely impact humanity and with increasing geographic disparities. Global South countries are more exposed to heat extremes and have reduced adaptation capacity. One documented source of such adaptation inequality is a lack of resources to cool down indoor temperatures. Less is known about the capacity to ameliorate outdoor heat stress. Here, we assess global inequality in green infrastructure, on which urban residents critically rely to ameliorate lethal heat stress outdoors. We use satellite-derived indicators of land surface temperature and urban green space area to quantify the daytime cooling capacity of urban green spaces in the hottest months across ~500 cities with population size over 1 million per city globally. Our results show a striking contrast with an about two-fold lower cooling capacity in Global South cities compared to the Global North (2.1 degrees Celsius vs. 3.8 degrees Celsius). A similar gap occurs for the cooling adaptation benefits received by an average urban resident (Global South 1.9 degrees Celsius vs. North 3.6 degrees Celsius), i.e., accounting for relative spatial distributions of people and urban green spaces. This cooling adaptation inequality is attributed to the discrepancies in urban green space quantity and quality between Global North and South cities, jointly shaped by natural and socioeconomic factors. Our analyses suggest vast potential for enhancing outdoor cooling adaptation while reducing its global inequality through expanding and optimizing urban green infrastructure.

Dongjie Wang, Chang-Tien Lu, Xinyue Ye et al.

The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics.

Gezhi Xiu, Jianying Wang, Thilo Gross et al.

Monitoring urban structure and development requires high-quality data at high spatiotemporal resolution. While traditional censuses have provided foundational insights into demographic and socioeconomic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analyzing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

Hoai Nguyen Huynh, Muhamad Azfar Bin Ramli

Spatial road networks have been widely employed to model the structure and connectivity of cities. In such representation, the question of spatial scale of the entities in the network, i.e. what its nodes and edges actually embody in reality, is of particular importance so that redundant information can be identified and eliminated to provide an improved understanding of city structure. To address this, we investigate in this work the relationship between the spatial scale of the modelled network entities against the amount of useful information contained within it. We employ an entropy measure from complexity science and information theory to quantify the amount of information residing in each presentation of the network subject to the spatial scale and show that it peaks at some intermediate scale. The resulting network presentation would allow us to have direct intuition over the hierarchical structure of the urban organisation, which is otherwise not immediately available from the traditional simple road network presentation. We demonstrate our methodology on the Singapore road network and find the critical spatial scale to be 85 m, at which the network obtained corresponds very well to the planning boundaries used by the local urban planners, revealing the essential urban connectivity structure of the city. Furthermore, the complexity measure is also capable of informing the secondary transitions that correspond well to higher-level hierarchical structures associated with larger-scale urban planning boundaries in Singapore.

Barbara Carolina Paris, Hitomi Mukai, Douglas André Roesler

Esta pesquisa realiza o estudo bibliográfico sobre jardins terapêuticos buscando a identificação de diretrizes projetuais que possam orientar a proposição desses espaços em ambientes hospitalares, contribuindo para a sua qualificação ambiental e, consequentemente, para a humanização do atendimento da saúde. Desenvolve-se a partir do seguinte questionamento: Quais são as diretrizes projetuais para um jardim terapêutico hospitalar? Para isso, objetiva definir o que é um jardim terapêutico; analisar as características de jardins terapêuticos hospitalares através de estudos de casos e demais publicações sobre o tema e identificar um conjunto de diretrizes projetuais para jardins terapêuticos hospitalares. Caracteriza-se como pesquisa qualitativa, de caráter exploratório e realiza a busca de dados em artigos revisados por pares, avaliações pós ocupacionais (APOs) e estudos de casos de jardins terapêuticos hospitalares. Ainda, relaciona os autores com suas contribuições por meio de tabelas, que foram divididas em diretrizes de jardins terapêuticos para trabalhadores da saúde, para pacientes adultos e seus acompanhantes/visitantes e para pacientes pediátricos e seus acompanhantes/visitantes. As diretrizes identificadas nessa pesquisa não têm o intuito de substituir ou sobrepor demais diretrizes projetuais, normas técnicas e leis, mas sim somarem-se a elas.

N. Geržinič, N. van Oort, S. Hoogendoorn-Lanser et al.

On-demand mobility services are promising to revolutionise urban travel, but preliminary studies are showing they may actually increase total vehicle miles travelled, worsening road congestion in cities. In this study, we assess the demand for on-demand mobility services in urban areas, using a stated preference survey, to understand the potential impact of introducing on-demand services on the current modal split. The survey was carried out in the Netherlands and offered respondents a choice between bike, car, public transport and ondemand services. 1,063 valid responses are analysed with a multinomial logit and a latent class choice model. By means of the latter, we uncover four distinctive groups of travellers based on the observed choice behaviour. The majority of the sample, the Sharing-ready cyclists (55%), are avid cyclists and do not see on-demand mobility as an alternative for making urban trips. Two classes, Tech-ready individuals (27%) and Flex-ready individuals (9%) would potentially use on-demand services: the former is fairly time-sensitive and would thus use on-demand service if they were sufficiently fast. The latter is highly costsensitive, and would therefore use the service primarily if it is cheap. The fourth class, Flex-sceptic individuals (9%) shows very limited potential for using on-demand services.

Zhou Fang, Tianren Yang, Ying Jin

In countries experiencing unprecedented waves of urbanization, there is a need for rapid and high quality urban street design. Our study presents a novel deep learning powered approach, DeepStreet (DS), for automatic street network generation that can be applied to the urban street design with local characteristics. DS is driven by a Convolutional Neural Network (CNN) that enables the interpolation of streets based on the areas of immediate vicinity. Specifically, the CNN is firstly trained to detect, recognize and capture the local features as well as the patterns of the existing street network sourced from the OpenStreetMap. With the trained CNN, DS is able to predict street networks' future expansion patterns within the predefined region conditioned on its surrounding street networks. To test the performance of DS, we apply it to an area in and around the Eixample area in the City of Barcelona, a well known example in the fields of urban and transport planning with iconic grid like street networks in the centre and irregular road alignments farther afield. The results show that DS can (1) detect and self cluster different types of complex street patterns in Barcelona; (2) predict both gridiron and irregular street and road networks. DS proves to have a great potential as a novel tool for designers to efficiently design the urban street network that well maintains the consistency across the existing and newly generated urban street network. Furthermore, the generated networks can serve as a benchmark to guide the local plan-making especially in rapidly developing cities.

Euclides C. Pinto Neto, Derick M. Baum, Jorge Rady de Almeida Junior et al.

Nowadays, the demand for optimized services in urban environments to provide better society wellness is increasing. In this sense, ground transportation in dense urban environments has been facing challenges for many years (e.g., congestion and resilience). One import outcome of the effort made toward the creation of new concepts for enhancing urban transportation is the Urban Air Mobility (UAM) concept. UAM aims at enhancing city transportation services using manned and unmanned vehicles. However, these operations bring many challenges to be faced, e.g., the interaction between the controller agent and autonomous vehicles. Furthermore, trajectory planning is not a simple task due to several factors. Firstly, the trajectories must consider a reduced minimum separation as eVTOL vehicle are expected to operate in complex urban environments. This leads the trajectory planning process to observe safety primitives more restrictively once the airspace is expected to comport many vehicles that follow small minimum separation standards. Thereupon, the main goal of the Trajectory-Based UAM Operations Simulator (TUS) is to simulate the Trajectory-Based UAM operations in urban environments considering the presence of both manned and unmanned eVTOL vehicles. For this, a Discrete Event Simulation (DES) approach is adopted, which considers an input (i.e., the eVTOL vehicles, their origin and destination, and their respective trajectories) and produces an output (which describes if the trajectories are safe and the elapsed operation time). The main contribution of this simulation tool is to provide a simulated environment for testing and measuring the effectiveness (e.g., flight duration) of trajectories planned for eVTOL vehicles.