Hasil untuk "Urban renewal. Urban redevelopment"

Bharat Sharma, Abhilasha J. Saroj, Evan Scherrer et al.

Representation of cities as organisms with metabolic processes is a useful analogy for urban design, development and sustainability. Urban metabolism can be modeled by representing urban systems as networks. The various networks included in a city's metabolism are interdependent in complex ways. Thus, understanding the interaction among these networks is essential to understanding how a healthy urban metabolism is sustained and how injuries to the metabolic system can "heal". It is particularly important to understand how disruptions to one system in an urban area affect the functioning of other systems. Using distribution-level data from a real U.S. city on the electricity distribution system and road geometry, we apply connected network modeling to two critical inter-connected urban infrastructure sectors: energy and transportation. We quantify the robustness of these interdependent networks by evaluating the connectivity disruptions that may occur due to natural or synthetic disruptive events, using both unweighted and weighted metrics.

Gellért Vilmos Szabó, Petra Emese Dragán, Andrea Wallner et al.

A globális éghajlatváltozás és az urbanizáció miatt avárosi faültetési programok egyre nagyobb szerepet kap-nak mind hazánkban, mind nemzetközi szinten. Jelentanulmány áttekintést ad a budapesti és nemzetközi faültetési gyakorlatokról és technológiákról, különös tekintettel a városi fásítások kihívásaira és a hosszú távú fenntarthatóság kérdéseire. A Budapesti Közművek FőkertKertészeti Divíziónál (Főkert) végzett munkám tapasztalatai és a MATE Tájépítészeti és Tájökológiai Doktori Iskolájában végzett kutatásaim alapján elemeztük a fővárosban alkalmazott faültetési technológiákat, a fajválasztást,valamint a jogi környezetet és szabványokat is. A kutatáscélja, hogy röviden feltárja a magyarországi és külföldigyakorlatok közötti különbségeket és rávilágítson a fenntartható városi zöldinfrastruktúra fejlesztésére irányulófaültetési módszerekre. Az eredmények megmutatják, hogy a megfelelő fajválasztás és az élőhelyi környezetoptimalizálása, valamint a telepítési technológiák összehangolt alkalmazása elengedhetetlen a városi faállományfenntarthatósága szempontjából. Ugyanakkor láthatóváválik, hogy a hazai környezetben további kutatásokra, a helyi körülményekhez igazított módszerekre van szükséga hosszú távú élhető települési környezet biztosítása érdekében.

اکبر طالب پور, مهناز کرمی

کیفیت فضاهای عمومی شهری در ایران، به‌ویژه میادین تاریخی، طی دهه‌های اخیر با بحران مواجه شده است، چون بسیاری از مداخلات صرفاً کالبدی بوده‌اند و پیامدهای اجتماعی و فرهنگی اجرای پروژه‌ها نادیده گرفته شده است که مشکلاتی برای ساکنین،کسبه و استفاده‌کنندگان از فضا ایجاد نموده‌است. این پژوهش با هدف ارزیابی تأثیرات اجتماعی و فرهنگی طرح ساماندهی میدان شهرری، بعنوان یکی از میادینی تاریخی و مذهبی تهران، انجام شد. مطالعه از رویکرد ترکیبی کمّی–کیفی بهره برده و داده‌ها از طریق پرسشنامه (۲۷۰ پاسخ‌دهنده شامل ساکنان، کسبه و استفاده‌کنندگان از فضا)، مصاحبه‌های نیمه‌ساختاریافته با ۳۰ متخصص و مشاهده میدانی گردآوری شد. تحلیل داده‌ها بر اساس نظریه‌های ارزیابی تأثیرات اجتماعی ونکلی، حس مکان و تعلق مکانی رلف و توآن، سرمایه اجتماعی پاتنام، عدالت فضایی سوجا و فضاهای عمومی و حیات شهری جاکوبز و ژل انجام شد. یافته‌ها نشان داد که ساماندهی میدان، علاوه بر بهبود شرایط کالبدی و زیست‌محیطی، تأثیرات مثبت محدودی بر حس تعلق، تعاملات اجتماعی و هویت فرهنگی داشته است، اما نابرابری فضایی و برخی نارضایتی‌های کسبه نیز به عنوان پیامدهای منفی شناسایی شدند که در میان‌مدت می‌تواند باعث افزایش نارضایتی عمومی و برهم خوردن طرح ساماندهی اجراشده شود. توجه به این نتایج ضمن اینکه اهمیت توجه همزمان به ابعاد اجتماعی، فرهنگی و کالبدی پروژه‌های بازآفرینی شهری را مورد تأکید قرار می‌دهد، ارائه راهکارهایی برای تقویت آثار مثبت و کاهش پیامدهای منفی در پروژه‌های مشابه شهری را ضروری می‌سازد. از سناریوهای مختلف(تداوم، تغییر و تعدیل وضع موجود) که در مطالعات اتاف بررسی می‌شوند تعدیل وضع موجود نسخه پیشنهادی محققین می‌باشد.

Milojević Ivan, Milanović Aleksandar

The research of ecological aspects in the macroeconomic trends of agricultural production occupies a significant place in improving the position of primary economic activities. Today, environmental air pollution is extremely significant primarily in the domain of livestock production and the emission of harmful substances into the air. In this paper, an attempt was made to present the macroeconomic indicators of agricultural production in ecological dimensions, using a comparative method. Investigation of comparative advantage through the RCA and LFI indices of the food processing industry and the intra-industry character of exchange measured by the GL index. refers to predispositions violation of ecological parameters.

Diego Maria Pinto, Davide Donato Russo, Antonio M. Sudoso

Increased urbanization and climate change intensify urban heat islands and degrade air quality, making current mitigation strategies insufficient. Nature-based solutions (NBSs), such as parks, green walls, roofs, and street trees, offer a promising means to regulate urban temperatures and enhance air quality. However, determining their optimal placement to maximize environmental benefits remains a pressing challenge. Leveraging Operational Research (OR) tools, we propose a Mixed-Integer Linear Programming (MILP) model that integrates multiple factors, including urban challenges, physical constraints, clustering techniques, convolution theory, and fairness considerations. This model determines the optimal placement of NBSs by addressing metrics such as ground temperature, air quality, and accessibility to green spaces. Through several case study analyses, we demonstrate the effectiveness of our approach in improving environmental and social indicators. This research holds implications for policy and practice, empowering urban planners and policymakers to make informed decisions regarding NBS implementation. Such decisions ensure that investments in urban greening yield maximum environmental, social, and economic benefits.

Can Rong, Jingtao Ding, Meng Li et al.

Commuting Origin-Destination (OD) flows capture movements of people from residences to workplaces, representing the predominant form of intra-city mobility and serving as a critical reference for understanding urban dynamics and supporting sustainable policies. However, acquiring such data requires costly, time-consuming censuses. In this study, we introduce a commuting OD flow dataset for cities around the world, spanning 6 continents, 179 countries, and 1,625 cities, providing unprecedented coverage of dynamics under diverse urban environments. Specifically, we collected fine-grained demographic data, satellite imagery, and points of interest~(POIs) for each city as foundational inputs to characterize the functional roles of urban regions. Leveraging these, a deep generative model is employed to capture the complex relationships between urban geospatial features and human mobility, enabling the generation of commuting OD flows between urban regions. Comprehensively, validation shows that the spatial distributions of the generated flows closely align with real-world observations. We believe this dataset offers a valuable resource for advancing sustainable urban development research in urban science, data science, transportation engineering, and related fields.

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.

Fernando Almeida

Partnerships are crucial for municipal sustainable development, leveraging diverse expertise and resources. Collaborations between local governments, businesses, NGOs, and community groups drive innovation and shared goals, ensuring more comprehensive and resilient solutions. This study aims to characterise and understand the role of municipal partnerships as a vital tool for driving sustainable progress. A mixed-methods methodology was applied by collecting 874 projects from 308 municipalities in Portugal. The findings identify 506 partnerships and reveal significant asymmetries in the distribution of municipal sustainable development projects. The results also identify 9 motivational factors for the emergence of these initiatives, with the economic and community dimensions standing out. Additionally, 11 actors involved in these initiatives and 4 clusters of collaboration between these entities are mapped. Finally, this study is particularly relevant for establishing public policies that can reduce geographical asymmetries and maximise the impact and resilience of these municipal sustainable development projects.

Jingxiao Liu, Haipeng Li, Hae Young Noh et al.

The analysis of urban seismic signals offers valuable insights into urban environments and society. Yet, accurate detection and localization of seismic sources on a city-wide scale with conventional seismographic network is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies over time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array, we generate spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities, such as traffic, construction, and school operations. We also show strong correlations between SSP values and environmental noise levels, as well as various persistent urban features, including land use patterns and demographics.

Yasmin Rahmati

The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detection and monitoring, achieving over 90% accuracy in urban wildlife tracking and invasive species management; (b) integrating data from remote sensing, acoustic monitoring, and citizen science enables large-scale ecosystem analysis; and (c) AI decision tools improve conservation planning and resource allocation, increasing prediction accuracy by up to 18.5% compared to traditional methods. The research presents an AI-Driven Framework for Urban Biodiversity Management, highlighting AI's impact on monitoring, conservation strategies, and ecological outcomes. Implementation strategies include: (a) standardizing data collection and model validation, (b) ensuring equitable AI access across urban contexts, and (c) developing ethical guidelines for biodiversity monitoring. The study concludes that integrating AI in urban biodiversity conservation requires balancing innovation with ecological wisdom and addressing data quality, socioeconomic disparities, and ethical concerns.

Jey Puget Gil, Emmanuel Coquery, John Samuel et al.

The continuous evolution of cities poses significant challenges in terms of managing and understanding their complex dynamics. With the increasing demand for transparency and the growing availability of open urban data, it has become important to ensure the reproducibility of scientific research and computations in urban planning. To understand past decisions and other possible scenarios, we require solutions that go beyond the management of urban knowledge graphs. In this work, we explore existing solutions and their limits and explain the need and possible approaches for querying across multiple graph versions.

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.

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.

Andy Jingqian Xue, Chenyu Huang, Bin Jiang

As urban critic Jane Jacobs conceived, a city is essentially the problem of organized complexity. What underlies the complexity refers to a structural factor, called living structure, which is defined as a mathematical structure composed of hierarchically organized substructures. Through these substructures, the complexity of cities, or equivalent to the livingness of urban space (L), can be measured by the multiplication the number of cities or substructures (S) and their scaling hierarchy (H), indicating that complexity is about both quantity of cities and how well the city is organized hierarchically. In other words, complexity emerges from a hierarchical structure where there are far more small cities or substructures than large ones across all scales, and cities are more or less similar within each individual hierarchical level. In this paper, we conduct comprehensive case studies to investigate urban complexity on a global scale using multisource geospatial data. We develop an efficient approach to recursively identifying all natural cities with their inner hotspots worldwide through connected component analysis. To characterize urban complexity, urban space is initially represented as a hierarchy of recursively defined natural cities, and all the cities are then represented as a network for measuring the degree of complexity or livingness of the urban space. The results show the Earth's surface is growing more complex from an economic perspective, and the dynamics of urban complexity are more explicit from nighttime light imagery than from population data. We further discuss the implications in city science, aiming to help create and recreate urban environments that are more resilient and livable by fostering organized complexity from the perspective of living structure.

Adheena Kottappurath Aliyar, Mark David Major, Heba O Tannous et al.

In the late 20th century, Doha’s rapid urbanization and globalization led to the loss of housing and the compact, traditional urban fabric in the old city center. The Qatari government and Msheireb Properties developed Msheireb Downtown Doha to bring urban living back with a contemporary re-interpretation of the traditional urban fabric and modern life conveniences. Our study's primary objective is to investigate the relationship between urban form and the rental value of residential units, identifying factors that might influence rental asking prices. The paper examines morphological characteristics through field surveys and the real-estate variables such as location, floor area, number of bedrooms/bathrooms, and asking price collected from publicly available real estate websites. The findings indicate that the residential units' layout and adjacent streets' morphological characteristics clearly define specific targeted user groups. Larger residential units target Qatari families via more bedrooms/bathrooms and quieter urban settings, emphasizing Islamic cultural values. Smaller units target ex-pat workers (especially Westerners) using open-plan layouts in more lively urban environments of the development. The price per square meter also increases for residential units closer to the Doha Metro station. The study reiterates the success of compact living for improving urban living in other neighborhoods of old Doha.

Asghar Molaei, Mohammad Akbari Ryabi

Irregularity and disturbance in facades’ materials, facades’ form and style, openings’ number and form, billboard advertisements, and extensions to facades have distanced the landscape of modern cities from the ideal Islamic model. The damages to the facades and their insecurity necessitate carefully examining the conformity of the facades’ condition with the expectations of Islamic jurisprudence. This qualitative research tries to pathologize urban landscape and facade policies through library studies, content analysis of Shia jurisprudence texts, and field observations. Assessing the conformity of the policies governing urban landscaping with Shia jurisprudence is the primary purpose of this research, and by referring to the rules of the sanctity of emulating outsiders, the rule of prohibition of detriment, the rule of destruction, and the rule of respecting human rights and avoiding aristocracy, it criticizes and analyzes the pathology of urban facades. This article concludes that a committee of experts in facade design, urban landscape, and jurisprudential rules should examine the conformity of facade designs and patterns with the jurisprudential rules and the ethical rules of the urban landscape. At the undergraduate level, it is essential to teach construction supervisors the policies of jurisprudence governing the urban landscape. Furthermore, due to its characteristics, each of the materials may conflict with some jurisprudential rules, which require facade designers to provide patterns in accordance with the ethics of landscaping and the jurisprudence rules.

Thanh Ho, Mark Stevenson, Jason Thompson

In light of the rapid global urbanization, urban design has been shown to contribute largely to promoting the health and well-being of urban citizens. However, studies of urban design are underrepresented in low- and middle-income countries in Asia, where urban forms are traditionally compact and complex with multiple layers. Hanoi, a typical city in low- and middle-income countries, exhibits five unique urban typologies generated through official planning, unregulated development, and historical fluctuations. This study examines the perceived urban design from a sample of 218 participants across five urban typologies in Hanoi using an established scale. The findings suggest that perceived urban design is significantly influenced by urban typologies. Old urban typologies tend to report higher scores of land use mix and access to services but lower scores of walking facilities and street connectivity than modern urban typologies. The study contributes to our understanding of urban design in Hanoi, providing policymakers and urban designers with essential insights for sustainable urban development.

Mohammad Ghaffari, Mahla Rezaei

Understanding the motivations behind tourists' decision to travel to a particular region greatly contributes to comprehending their behavior. This study aims to identify, categorize, and prioritize the motives of domestic tourists who choose to travel to Kerman city. The research methodology employed an applied approach, a descriptive nature, and utilized a mixed data type. The collection of research data involved the utilization of field and library methods. The statistical population for this study consisted of domestic tourists who visited Kerman city in March 2022 and were accessible for the researcher. The research sampling method used was non-probability sampling, specifically employing the easy or available sampling type. The identification and categorization of tourists' motives were achieved through the application of the Exploratory Factor Analysis method, while the prioritization of the identified motives was conducted using the TOPSIS method. The results of the data analysis demonstrated that the motives of domestic tourists for traveling to Kerman city encompassed the following aspects: the desire to visit historical-cultural attractions, religious motives, work-related motives, motives related to the economic benefits of travel, motives associated with group travel, motives to visit friends and relatives, shopping motives, entertainment and recreational motives, nostalgia, appreciation of the natural environment, and learning motives.

Zhilun Zhou, Jingtao Ding, Yu Liu et al.

Although generative AI has been successful in many areas, its ability to model geospatial data is still underexplored. Urban flow, a typical kind of geospatial data, is critical for a wide range of urban applications. Existing studies mostly focus on predictive modeling of urban flow that predicts the future flow based on historical flow data, which may be unavailable in data-sparse areas or newly planned regions. Some other studies aim to predict OD flow among regions but they fail to model dynamic changes of urban flow over time. In this work, we study a new problem of urban flow generation that generates dynamic urban flow for regions without historical flow data. To capture the effect of multiple factors on urban flow, such as region features and urban environment, we employ diffusion model to generate urban flow for regions under different conditions. We first construct an urban knowledge graph (UKG) to model the urban environment and relationships between regions, based on which we design a knowledge-enhanced spatio-temporal diffusion model (KSTDiff) to generate urban flow for each region. Specifically, to accurately generate urban flow for regions with different flow volumes, we design a novel diffusion process guided by a volume estimator, which is learnable and customized for each region. Moreover, we propose a knowledge-enhanced denoising network to capture the spatio-temporal dependencies of urban flow as well as the impact of urban environment in the denoising process. Extensive experiments on four real-world datasets validate the superiority of our model over state-of-the-art baselines in urban flow generation. Further in-depth studies demonstrate the utility of generated urban flow data and the ability of our model for long-term flow generation and urban flow prediction. Our code is released at: https://github.com/tsinghua-fib-lab/KSTDiff-Urban-flow-generation.

Fengli Xu, Jun Zhang, Chen Gao et al.

Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization. These challenges, ranging from traffic congestion and pollution to social inequality, call for advanced technological interventions. Recent developments in big data, artificial intelligence, urban computing, and digital twins have laid the groundwork for sophisticated city modeling and simulation. However, a gap persists between these technological capabilities and their practical implementation in addressing urban challenges in an systemic-intelligent way. This paper proposes Urban Generative Intelligence (UGI), a novel foundational platform integrating Large Language Models (LLMs) into urban systems to foster a new paradigm of urban intelligence. UGI leverages CityGPT, a foundation model trained on city-specific multi-source data, to create embodied agents for various urban tasks. These agents, operating within a textual urban environment emulated by city simulator and urban knowledge graph, interact through a natural language interface, offering an open platform for diverse intelligent and embodied agent development. This platform not only addresses specific urban issues but also simulates complex urban systems, providing a multidisciplinary approach to understand and manage urban complexity. This work signifies a transformative step in city science and urban intelligence, harnessing the power of LLMs to unravel and address the intricate dynamics of urban systems. The code repository with demonstrations will soon be released here https://github.com/tsinghua-fib-lab/UGI.