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

Hossein Abdipour, Seyed Ebrahim Seyed Mousavi, Abdolali khammari et al.

The primary aim of the present research was to investigate the correlation between the level of airborne particulate matter (PM10) and C-reactive protein(CRP) in healthy individuals residing in Zahedan. The study was supported from February 2024 to September 2024. Environmental samples were collected every 6 days and on days with high levels of dust in Zahedan due to its climatic conditions. The concentration of suspended particulate matter was measured during these collections. In addition, the enzyme-linked immunosorbent assay (ELISA) method was utilized to analyze the amount of CRP in the blood samples of 40 volunteers aged between 20 and 35 years under varying levels of air contamination. Throughout the study period, the average, minimum, and maximum concentrations of suspended particulate matter (PM10) were recorded as 55, 212, and 500 µg /m3, respectively, signifying that PM10 levels exceeded standard limits. Current evidence indicates that exposure to ambient particulate matter (PM) contributes to a range of harmful health effects, primarily through systemic inflammation.The levels of plasma CRP were notably higher during heightened air pollution conditions compared to normal conditions (p-value < 0.05). The concentration of suspended particles (PM10) surpassed the permissible threshold on the majority of measurement days. A positive association was observed between dust concentration and CRP levels. Given the exacerbation of cardiovascular and respiratory ailments when the levels of suspended particulate matter exceed regulated standards, it is imperative to prioritize health programs and personal well-being.

Xiaohan Xu, Wei Ma, Zhiheng Shi et al.

With the global energy transition and the rapid penetration of electric vehicles (EVs), the widening travel cost gap between EVs and gasoline vehicles (GVs) increasingly affects commuters' route choices and may reshape urban congestion patterns. Existing research remains in its preliminary exploratory phase. On the one hand, multi-class models do not account for fixed user class scenarios, which may not align with actual commuters; on the other hand, there is a lack of systematic quantitative analysis based on real-world complex road networks across multiple cities. As a result, the congestion effects induced by heterogeneous GV-EV cost structures may be mischaracterized or substantially underestimated. To address these limitations, this paper proposes a multi-user equilibrium (MUE) assignment model for mixed GV-EV traffic, constructs a dual algorithm with convergence guarantees, and designs multi-dimensional evaluation metrics for congestion patterns. Using 10 representative U.S. cities as a case study, this research explores the evolution trends of traffic congestion under different EV penetration scenarios based on real city-level road networks and block-level commuter origin-destination (OD) demand. The results show that full EV penetration reduces average system travel time by 2.27%--10.78% across the 10 cities, with New Orleans achieving the largest reduction (10.78%) and San Francisco the smallest (2.27%), but the effectiveness of alleviating congestion exhibits urban heterogeneity. Moreover, for cities with sufficient network redundancy, benefits are primarily concentrated during the low to medium EV penetration stage (0-0.5), though cities with topological constraints (e.g., San Francisco) show more limited improvements throughout all penetration levels. This paper can provide a foundation for formulating differentiated urban planning and congestion management policies.

Mulugeta Girma, Zelalem Mulatu

Corridor development refers to the strategic planning, building, and operation of transportation infrastructure that connects key metropolitan areas within a city. This study aims to assess the effects of corridor development initiatives in Addis Ababa. Data were collected through on-site observations and interviews with key informants from relevant offices, alongside secondary data. Thematic analysis was employed to interpret the data. The study’s findings indicate that corridor development initiatives have decreased traffic congestion, enhanced pedestrian and bicycle access, and improved mobility, making commuting more enjoyable and efficient. Furthermore, it promotes sustainability through improving green spaces, open public areas, and non-motorized transportation infrastructure. Overall, the study found that the corridor development project has significantly boosted the city’s image. Finally, the study recommends using Addis Ababa’s corridor development as a model for urban planning and financial investment in transportation infrastructure, which can enhance the city’s quality of life. Besides, to address the city’s mobility challenges and promote a smart city, the study advocates for implementing integrated transit systems, vehicle sharing, traffic calming measures, and parking fees as part of the city’s ongoing development efforts.

Muhammad Abdul Mubdi Bindar, Muhammad Zainal Ibad, Goldie Melinda Wijayanti et al.

This paper studies the commuting patterns of students and staff at the Sumatera Institute of Technology (ITERA), a rapidly growing university located in a peri-urban area of Lampung Province, Indonesia. The research is grounded in the understanding that peri-urban commuters face unique mobility challenges shaped by transitional land use, limited infrastructure, and high motorcycle dependency. Using both statistical and spatial analyses, the article analyzed distinct travel behaviors and their socioeconomic determinants. Findings reveal that motorcycles dominate as the primary commuting mode for both groups, driven by cultural norms and constrained public transport access. Staff exhibit higher rates of vehicle ownership and longer, more dispersed commutes, while students tend to reside closer to campus and rely on borrowed motorcycles. Temporal analysis shows structured weekday travel among staff and more flexible, weekend-active patterns among students. The findings offer targeted insights for developing sustainable transportation strategies in rapidly expanding peri-urban institutions—such as promoting bicycle and pedestrian infrastructure, designing transport policies that account for widespread motorcycle borrowing among students, and differentiating mobility interventions based on the spatial dispersion and financial profiles of staff versus students.

Siqi Lai, Yansong Ning, Zirui Yuan et al.

Large language models (LLMs) have shown emerging potential in spatiotemporal reasoning, making them promising candidates for building urban agents that support diverse urban downstream applications. Despite these benefits, existing studies primarily focus on evaluating urban LLM agent on outcome-level metrics (e.g., prediction accuracy, traffic efficiency), offering limited insight into their underlying reasoning processes. As a result, the strengths and limitations of urban LLM agents in spatiotemporal reasoning remain poorly understood. To this end, we introduce USTBench, the first benchmark to evaluate LLMs' spatiotemporal reasoning abilities as urban agents across four decomposed dimensions: spatiotemporal understanding, forecasting, planning, and reflection with feedback. Specifically, USTBench supports five diverse urban decision-making and four spatiotemporal prediction tasks, all running within our constructed interactive city environment UAgentEnv. The benchmark includes 62,466 structured QA pairs for process-level evaluation and standardized end-to-end task assessments, enabling fine-grained diagnostics and broad task-level comparison across diverse urban scenarios. Through extensive evaluation of thirteen leading LLMs, we reveal that although LLMs show promising potential across various urban downstream tasks, they still struggle in long-horizon planning and reflective adaptation in dynamic urban contexts. Notably, recent advanced reasoning models (e.g., DeepSeek-R1) trained on general logic or mathematical problems do not consistently outperform non-reasoning LLMs. This discrepancy highlights the need for domain-specialized adaptation methods to enhance urban spatiotemporal reasoning. Overall, USTBench provides a foundation to build more adaptive and effective LLM-based urban agents and broad smart city applications.

Dongsheng Chen, Yu Feng, Xun Li et al.

Understanding the high-order relationship between urban form and function is essential for modeling the underlying mechanisms of sustainable urban systems. Nevertheless, it is challenging to establish an accurate data representation for complex urban forms that are readily explicable in human terms. This study proposed the concept of core urban morphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call CoMo. By interpretating the well-trained deep learning model with a stable weighted F1-score of 89.14%, CoMo presents a promising approach for revealing links between urban function and urban form in terms of core urban morphology representation. Using Boston as a study area, we analyzed the core urban forms at the individual-building, block, and neighborhood level that are important to corresponding urban functions. The residential core forms follow a gradual morphological pattern along the urban spine, which is consistent with a center-urban-suburban transition. Furthermore, we prove that urban morphology directly affects land use efficiency, which has a significantly strong correlation with the location (R2=0.721, p<0.001). Overall, CoMo can explicably symbolize urban forms, provide evidence for the classic urban location theory, and offer mechanistic insights for digital twins.

Filippo Marchesani

This chapter explores the six core dimensions of smart cities (i.e. smart economy, mobility, environment, people, living, and governance) emphasizing their interdependence and the need for holistic orchestration. Building on Giffinger et al. (2007) and subsequent literature, it argues that integrating these dimensions is crucial for sustainable urban development. ICT plays a key enabling role but must be complemented by human and social capital. Through institutional examples, such as the creation of dedicated municipal offices for digital innovation, the chapter illustrates how governance and internal capacity shape smart transitions. A human-centric approach is also essential, ensuring inclusivity, creativity, and active civic participation. Ultimately, smart cities must be viewed as cohesive urban ecosystems where technology, people, and governance interact dynamically.

Jindong Han, Yansong Ning, Zirui Yuan et al.

The long-standing vision of intelligent cities is to create efficient, livable, and sustainable urban environments using big data and artificial intelligence technologies. Recently, the advent of Large Language Models (LLMs) has opened new ways toward realizing this vision. With powerful semantic understanding and reasoning capabilities, LLMs can be deployed as intelligent agents capable of autonomously solving complex problems across domains. In this article, we focus on Urban LLM Agents, which are LLM-powered agents that are semi-embodied within the hybrid cyber-physical-social space of cities and used for system-level urban decision-making. First, we introduce the concept of urban LLM agents, discussing their unique capabilities and features. Second, we survey the current research landscape from the perspective of agent workflows, encompassing urban sensing, memory management, reasoning, execution, and learning. Third, we categorize the application domains of urban LLM agents into five groups: urban planning, transportation, environment, public safety, and urban society, presenting representative works in each group. Finally, we discuss trustworthiness and evaluation issues that are critical for real-world deployment, and identify several open problems for future research. This survey aims to establish a foundation for the emerging field of urban LLM agents and to provide a roadmap for advancing the intersection of LLMs and urban intelligence. A curated list of relevant papers and open-source resources is maintained and continuously updated at https://github.com/usail-hkust/Awesome-Urban-LLM-Agents.

Giovanni Mauro, Nicola Pedreschi, R. Lambiotte et al.

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis also highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.

Jonghyun Kim, Donghyeon Yu, Hyoji Choi et al.

This study introduces a metric designed to measure urban structures through the economic complexity lens, building on the foundational theories of urban spatial structure, the Central Place Theory (CPT) (Christaller, 1933). Despite the significant contribution in the field of urban studies and geography, CPT has limited in suggesting an index that captures its key ideas. By analyzing various urban big data of Seoul, we demonstrate that PCI and ECI effectively identify the key ideas of CPT, capturing the spatial structure of a city that associated with the distribution of economic activities, infrastructure, and market orientation in line with the CPT. These metrics for urban centrality offer a modern approach to understanding the Central Place Theory and tool for urban planning and regional economic strategies without privacy issues.

Batel Yossef Ravid, Meirav Aharon-Gutman

Complexity theory has become a conceptual framework and a source of inspiration for Smart City initiatives. In addition to many other conceptions, the Urban Digital Twin (UDT) became both a concept and a tool for generating the revolutionary act of data-driven 3D city modeling. Indeed, the UDT has increased the ability of planners to make decisions vis-à-vis data-driven city models; at the same time, however, it has attracted criticism because of its focus on the physical dimensions of cities. In facing these challenges, we seek to join the conceptual and practical efforts to generate a social turn in the field of Smart Cities and urban innovation. Creating a UDT with a social focus, we maintain, is not only a 1:1 translation of the built environment into the social realm, but also demands interdisciplinary knowledge from the fields of sociology, anthropology, planning, and ethics studies. This article makes theoretical and methodological contributions. Theoretically, it discusses the potential contribution of the Social Urban Digital Twin (SUDT) to the theory of urbanism, enabling us to represent the physical and the social environments as a single fabric. Methodologically, it enhances the know-how of the City Analytics research community by advancing a six-phase protocol for developing SUDTs, each phase of which integrates technological conceptions and social-theoretical content. The phases of the SUDT protocol are demonstrated using a specific case study: the experience of elderly residents of the Haifa neighborhood of Hadar—a low-income neighborhood in Israel characterized by ethnic and national diversity—during the Coronavirus pandemic. We conclude by discussing the contributions and limitations of the SUDT.

Prentiss A. Dantzler, Elizabeth Korver‐Glenn, Junia Howell

Social scientists have long debated whether racial inequality is an unfortunate consequence of political and economic exploitation or a core feature of capitalism. In 1983, Cedric Robinson synthesized these two opposing perspectives, calling the latter racial capitalism and demonstrating its theoretical viability. In recent years, scholars have increasingly employed Robinson’s conception of racial capitalism to explain a wide array of phenomena. Yet, urban sociology has not fully explored how racial capitalism changes and reshapes our core theoretical approaches. To begin to fill this gap, this special issue presents original papers that employ racial capitalism to extend, challenge, or refine theories of and methods for understanding cities and communities. In this introduction, we outline urban scholars’ historical explanations of racial inequality and provide an overview of the development and definition(s) of racial capitalism. We then summarize the papers included in this special issue and discuss a pathway forward for urban sociology.

رضا عیوض لو, داوود رزاقی

ارزشگذاری داراییها اعم از اوراق بهادار و داراییهای واقعی یکی از ارکان موثر بر تصمیمات سرمایه گذاری است، ارزشگذاری منصفانه منجر به تخصیص بهینه منابع سرمایه ای می شود و تخصیص بهینه سرمایه در اقتصاد نقش بی بدیلی را در رشد و توسعه اقتصادی ایفا می کند. در حال حاضر فقدان چارچوب مدون و مشخصی که بتواند برآورد دقیقی از ارزش تبیین نماید اهمیت دو چندان یافته است. بنابراین ارائه چارچوبی که بتواند فارغ از قضاوتهای شخصی و سلایق مختلف به صورت علمی و مستدل جهت ارائه الگوی مناسب به منظور ارزشگذاری شرکتها مورد استفاده قرار گیرد، اهمیت یافته است. در این پژوهش ابتدا با استفاده از مصاحبه با خبرگان به انتخاب الگوی مناسب ارزشگذاری سهام در 14 گونه شرکت مختلف خواهیم پرداخت بدین صورت که ابتدا با استفاده از فرآیند تحلیل شبکه ای و براساس نظر خبرگان وزن معیارها محاسبه شده و درنهایت الگویی جامع برای ارزشگذاری اقسام گوناگون شرکتها پیشنهاد شده است. در انتها هم آسیب شناسی جامعی از محیط ارزشگذاری ارائه شده است.معیارهای به کارگرفته شده در این پژوهش در 4 دسته رویکرد سودآوری گذشته (شامل میانگینEBIT، سود به قیمت گذشته)، رویکرد مبتنی بر دارایی(شامل ارزش اسمی، ارزش دفتری، ارزش جایگزینی، ارزش خالص دارایی ها و ارزش تصفیه)، رویکرد تنزیل جریانهای نقدی(شامل fcff،fcfe،apv،eva و ddm) و رویکرد بازار(p/s،p/nav، p/e به جزگذشته، ev/ebit،p/c،ev/s،p/b،p/cf و p/dps) قرار گرفتند. این پژوهش از نظر هدف از نوع کاربردی است و از نظر ماهیت و روش جمع اوری داده ها، از نوع توصیفی و از شاخه مطالعه موردی می باشد.

Kejiang Qian, Lingjun Mao, Xin Liang et al.

In urban planning, land use readjustment plays a pivotal role in aligning land use configurations with the current demands for sustainable urban development. However, present-day urban planning practices face two main issues. Firstly, land use decisions are predominantly dependent on human experts. Besides, while resident engagement in urban planning can promote urban sustainability and livability, it is challenging to reconcile the diverse interests of stakeholders. To address these challenges, we introduce a Consensus-based Multi-Agent Reinforcement Learning framework for real-world land use readjustment. This framework serves participatory urban planning, allowing diverse intelligent agents as stakeholder representatives to vote for preferred land use types. Within this framework, we propose a novel consensus mechanism in reward design to optimize land utilization through collective decision making. To abstract the structure of the complex urban system, the geographic information of cities is transformed into a spatial graph structure and then processed by graph neural networks. Comprehensive experiments on both traditional top-down planning and participatory planning methods from real-world communities indicate that our computational framework enhances global benefits and accommodates diverse interests, leading to improved satisfaction across different demographic groups. By integrating Multi-Agent Reinforcement Learning, our framework ensures that participatory urban planning decisions are more dynamic and adaptive to evolving community needs and provides a robust platform for automating complex real-world urban planning processes.

Yanyan Xu, Luis E. Olmos, David Mateo et al.

The urban spatial structure represents the distribution of public and private spaces in cities and how people move within them. While it usually evolves slowly, it can change fast during large-scale emergency events, as well as due to urban renewal in rapidly developing countries. This work presents an approach to delineate such urban dynamics in quasi-real-time through a human mobility metric, the mobility centrality index $ΔKS$. As a case study, we tracked the urban dynamics of eleven Spanish cities during the COVID-19 pandemic. Results revealed that their structures became more monocentric during the lockdown in the first wave, but kept their regular spatial structures during the second wave. To provide a more comprehensive understanding of mobility from home, we also introduce a dimensionless metric, $KS_{HBT}$, which measures the extent of home-based travel and provides statistical insights into the transmission of COVID-19. By utilizing individual mobility data, our metrics enable the detection of changes in the urban spatial structure.

Mohamed El Asawy, Eman Badawy Ahmed

تسعى الدولة الي حوکمة العمران في مصر وذلک من خلال إصدار العديد من القوانين والتشريعات التخطيطية لرفع کفاءة التجمعات العمرانية، وتعتبر التعديلات المقترح تنفيذها على بنود قانون البناء الموحد من أهم التشريعات القانونية محل الدراسة في وقتنا الحالي. تتناول الدراسة تحليل وتقييم الأثر البيئي جراء تطبيق التعديلات المقترحة على متوسط الطاقة المستهلکة بالوحدات السکنية سواء بالسلب أو الإيجاب، مع ذکر خاص لمدى توافق تلک التعديلات مع التوصيات المقترحة بأکواد البناء المصري المعنية بالنواحي البيئية للمباني السکنية، بالاضافة الي بعض التعديلات المقترحة والتي يوصي البحث بضرورة ضمها الي قانون البناء الموحد. منهجية البحث: يتبع البحث المنهج الاستقرائي من خلال دراسة القوانين والمعايير الحاکمة لتصميم الوحدات السکنية والتي تشمل قانون البناء الموحد رقم 119 لسنة 2008 والضوابط والاشتراطات التخطيطية والبنائية للمدن المصرية 2020, والکود المصري لتحسين کفاءة استخدام الطاقة في المباني, بالاضافة الي الکود المصري للتهوية في المباني. ثم المنهج التطبقي وذلک من خلال اقتراح النموذج السکني للدراسة التطبيقية واستخدام برامج المحاکاة البيئية (designbuilder and energy plus) لقياس تاثير المتغيرات التصميمية المقترحة (ارتفاع المبنى والمسافات البينية بين المباني المتقابلة, والبروزات الخارجية, وطبقات الغلاف الخارجي المصمت, وأبعاد ونسب الفتحات الخارجية, والمناور السکنية الداخلية) علي استهلاک الطاقة بالمبني السکني. هذا وتشير نتائج الدراسة البحثية إلى أن تعديلات قانون البناء الموحد بمنظومة الاشتراطات الجديده2020 ذات تأثير ايجابي في زيادة الوفر في الطاقة المستهلکة للوحدات السکنية عن مثيلاتها في حال تطبيق قانون البناء الموحد لمقدار التوفير في الطاقة المستهلکة بمعدل 4% للمناور السکنية وبنسبة تتراوح ما بين 14 : 17% للبروزات ومن 12 : 16% لتأثير عرض الطريق وعلاقته بارتفاع المبني. Egypt seeks to govern urbanization by issuing many planning laws to increase the efficiency of urban communities. The proposed amendments to the Building Law are considered one of the most important legal studies during these days. The research focuses on analyzing and evaluating the environmental impact of applying amendments on the average energy consumption in residential buildings, whether negatively or positively. In addition to some proposed amendments, which the research recommends be included in the amendments. Research Methodology depends on the inductive approach by studying the laws for the housing unit’s design, which include the Building Law No. 119 of 2008, the planning and building requirements for Egyptian cities 2020, the Egyptian Code for Energy in Buildings, and the Egyptian code for ventilation in buildings. The second part depends on the applied approach by proposing the residential model for the applied study and using the environmental simulation programs (design builder and energy plus) to measure the effectiveness of the proposed design variables (building height, distances between opposite buildings, external shades, components of the building's external envelope, openings and courtyard) on the energy consumption of the residential building. The results of the study indicate that the modification of the building law with the new requirements (2020) has a positive effect on the building's energy saving compared to the case of applying the building law. The modifications achieve 4% in energy savings for the courtyard, 14:17 % for the cantilevers, and 12:16 % for the relationship between road width and the building height.

Yanmei LI

South Florida has been among the top foreclosure markets in the United States, but little research has explored whether this market presents different dynamics compared to other metropolitan areas. This research chooses Broward County to explore whether socioeconomic characteristics and certain public policy instruments relate to subprime lending and mortgage foreclosure patterns. Results indicate areas bounded by linear highways and railroads have a concentration of low-income black population and subprime loans. The spatial distribution of subprime loans is mostly explained by a higher percentage of minority and/or Hispanic population in a neighborhood. Yet, racial minorities, instead of Hispanic origin, contributes mostly to the concentration of subprime loans. The spatial pattern of foreclosures is more complex, determined not only by subprime loans but also possibly other factors associated with the mortgage crisis. This suggests that disadvantaged neighborhoods are disproportionally lacking favorable opportunities due to institutional and sub- cultural forces shaping the geography of subprime and foreclosure.

Patrícia Santos Pedrosa, Eliana Sousa Santos, Nuria Álvarez Lombardero et al.

Keith Burghardt, Johannes H. Uhl, Kristina Lerman et al.

The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 United States metropolitan areas over an unprecedented 115 years using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries based on settlement density. We demonstrate the clearest evidence that urban scaling exponents can closely match theoretical models over a century if cities are defined as dense settlement patches. Despite the close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that a city in 2015 uses more developed land and kilometers of road than a city with a similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results offer a new way to study urban systems based on novel, geohistorical data.

Shi Jer Low, Venugopalan, S. G. Raghavan et al.

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods.