Hasil untuk "Urban renewal. Urban redevelopment"

Lubin Bai, Sheng Xiao, Ziyu Yin et al.

Urban Villages (UVs) represent a distinctive form of high-density informal settlement embedded within China's rapidly urbanizing cities. Accurate identification of UVs is critical for urban governance, renewal, and sustainable development. But due to the pronounced heterogeneity and diversity of UVs across China's vast territory, a consistent and reliable nationwide dataset has been lacking. In this work, we present GeoLink-UV, a high-resolution nationwide UV mapping product that clearly delineates the locations and boundaries of UVs in 342 Chinese cities. The dataset is derived from multisource geospatial data, including optical remote sensing images and geo-vector data, and is generated through a foundation model-driven mapping framework designed to address the generalization issues and improve the product quality. A geographically stratified accuracy assessment based on independent samples from 28 cities confirms the reliability and scientific credibility of the nationwide dataset across heterogeneous urban contexts. Based on this nationwide product, we reveal substantial interregional disparities in UV prevalence and spatial configuration. On average, UV areas account for 8 % of built-up land, with marked clustering in central and south China. Building-level analysis further confirms a consistent low-rise, high-density development pattern of UVs nationwide, while highlighting regionally differentiated morphological characteristics. The GeoLink-UV dataset provides an open and systematically validated geospatial foundation for urban studies, informal settlement monitoring, and evidence-based urban renewal planning, and contributes directly to large-scale assessments aligned with Sustainable Development Goal 11. The GeoLink-UV dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.18688062.

سیده زهرا درواری, امید جمشیدی, فاطمه قربانی پیرعلیدهی

کارآفرینی پایدار به‌عنوان یکی از مؤلفه‌های کلیدی توسعۀ روستایی، نقش مؤثری در توانمندسازی اقتصادی و اجتماعی جوامع محلی ایفا می‌کند. بااین‌حال، در بسیاری از مناطق روستایی به‌ویژه نواحی ساحلی، توسعۀ کارآفرینی با موانع گوناگونی مواجه است.هدف پژوهش حاضر، شناسایی و تحلیل چالش‌ها و ارائۀ راهکارهای توسعۀ کارآفرینی پایدار در روستاهای ساحلی با تمرکز بر روستاهای ساحلی شهرستان‌های ساری و میاندرود مازندران است. این پژوهش با بهره‌گیری از رویکرد ترکیبی (کیفی–کمی) انجام گرفت. در بخش کیفی، از طریق مصاحبه‌های نیمه‌ساختاریافته با 11 خبرۀ حوزۀ توسعۀ روستایی و کارآفرینی، مجموعه‌ای از موانع مؤثر و راهکارهای توسعۀ کارآفرینی پایدار روستایی شناسایی و با استفاده از تحلیل مضمون دسته‌بندی شد. در ادامه، به‌منظور اعتبارسنجی و تحلیل اولویت موانع و راهکارها، بخش کمی با توزیع پرسشنامه در میان 60 متخصص و تحلیل داده‌ها به روش تحلیل مقایسه‌ای میزان اهمیت و اجرای راهکارها دنبال شد.نتایج بخش کیفی پژوهش نشان داد هفت بعد کلیدی موانع اصلی توسعۀ کارآفرینی پایدار در روستاهای ساحلی هستند. براساس فراوانی کدهای استخراج‌شده، موانع اقتصادی با ۴۴ کد در رتبۀ نخست قرار گرفت. پس از آن به‌ترتیب موانع زیرساختی (۲۲ کد)، موانع پشتیبانی و حمایتی (۱۷ کد)، موانع روان‌شناختی (۱۲ کد)، موانع فرهنگی (۱۱ کد)، موانع سیاست‌گذاری و برنامه‌ریزی (۹ کد) و موانع آموزشی و اطلاع‌رسانی (۸ کد) قرار داشتند. در بخش راهکارها نیز سه دستۀ اصلی شامل حمایت‌های غیرمادی، آموزش و ترویج کارآفرینی و حمایت‌های مادی شناسایی شدند. تحلیل مقایسه‌ای این راهکارها با آزمون من-ویتنی نشان‌دهندۀ شکاف معنادار بین میزان اهمیت و سطح اجرا در تمامی موارد بود؛ به‌طوری‌که میانگین نمرۀ اهمیت در هر سه دسته راهکار بسیار بیشتر از میانگین نمرۀ اجرا ارزیابی شد. این یافته مؤید فاصلۀ محسوس بین ضرورت ادراک‌شده و عملیاتی‌سازی سیاست‌ها از دیدگاه ذی‌نفعان محلی است.نتایج تحقیق ضمن تأکید بر چندبعدی‌بودن فرایند توسعۀ کارآفرینی در مناطق روستایی ساحلی، بر ضرورت طراحی سیاست‌های منطقه‌محور، تقویت ساختارهای حمایتی و آموزش‌محور و رفع موانع نهادی تأکید دارد.

Haotian Ma, Siqing Chen

During the COVID-19 emergency, the escalating number of cases overwhelmed the admission capacity of operating hospitals in many cities. The pandemic thus prompted the rapid construction of temporary field hospitals in cities like Wuhan, China, to relieve pressure on existing health infrastructure. While their operational phase has been well-documented, the post-pandemic reuse of these facilities remains underexplored. This study proposes a novel design paradigm - Anonymity Landscape Memorial Design - to transform the retired Huoshenshan Field Hospital into a public memorial park. Drawing on counter-memorial theory and spatial translation methods, the project reimagines commemorative landscapes through abstraction, emotional disruption, and interactive experience, rather than conventional symbolism. The design unfolds in four stages aligned with the emotional arc of the pandemic: outbreak, lockdown, recovery, and reflection. Methodologically, the study integrates multi-source data analysis, theoretical modeling, and adaptive reuse strategies to address spatial, social, and economic dimensions. Findings demonstrate how this approach fosters inclusive memory-making while yielding 62.5% material recycling and approximately CNY 7.94 million (US$1.10million) in cost savings. The project contributes a replicable framework for converting ephemeral urban infrastructure into resilient civic spaces that blend memory, sustainability, and public use. These outcomes demonstrate how post-pandemic urban transformations can reduce resource waste, strengthen local economies, enhance spatial equity, and expand access to quality civic spaces – offering insightful perspectives to other COVID-19-affected cities on similar issues of contemporary urbanisation.

Hrvoje Bartulović, Dujmo Žižić

Urban rivers are critical ecological and cultural assets facing accelerating biodiversity loss. This study examines the integrated redevelopment of the Jadro River spring in Solin, Croatia, where a protected ichthyological reserve intersects layered heritage and urban edges to enhance conservation and public value. Using a single-case study design that combines archival project documentation, participant observation by the architect–authors, and a post-occupancy review three years after completion, the analysis synthesizes ecological, social, and design evidence across planning, delivery, and operation phases. The project delivered phased visitor and interpretation centers, accessible paths and bridges, habitat-compatible materials, and formalized access management that relocated parking from riverbanks, reduced episodic pollution sources, and prioritized inclusive, low-impact use. Governance and programming established a municipal management plan, curriculum-ready interpretation, and carrying capacity monitoring, transforming an underused picnic area into an educational, recreational, and conservation-oriented public landscape while safeguarding sensitive habitats. A transferable design protocol emerged, aligning blue green infrastructure, heritage conservation, adaptive reuse, and social–ecological system (SES)-informed placemaking to protect the endemic soft-mouth trout and strengthen a sense of place and community stewardship. The case supports SES-based riverpark renewal in which conservative interventions within protected cores are coupled with consolidated services on resilient ground, offering a replicable framework for ecologically constrained urban headwaters.

Cheng Chen, Geng Tian, Shaoxiang Qin et al.

Simulation of urban wind environments is crucial for urban planning, pollution control, and renewable energy utilization. However, the computational requirements of high-fidelity computational fluid dynamics (CFD) methods make them impractical for real cities. To address these limitations, this study investigates the effectiveness of the Fourier Neural Operator (FNO) model in predicting flow fields under different wind directions and urban layouts. In this study, we investigate the effectiveness of the Fourier Neural Operator (FNO) model in predicting urban wind conditions under different wind directions and urban layouts. By training the model on velocity data from large eddy simulation data, we evaluate the performance of the model under different urban configurations and wind conditions. The results show that the FNO model can provide accurate predictions while significantly reducing the computational time by 99%. Our innovative approach of dividing the wind field into smaller spatial blocks for training improves the ability of the FNO model to capture wind frequency features effectively. The SDF data also provides important spatial building information, enhancing the model's ability to recognize physical boundaries and generate more realistic predictions. The proposed FNO approach enhances the AI model's generalizability for different wind directions and urban layouts.

Geoff Boeing

OSMnx is a Python package for downloading, modeling, analyzing, and visualizing urban networks and any other geospatial features from OpenStreetMap data. A large and growing body of literature uses it to conduct scientific studies across the disciplines of geography, urban planning, transport engineering, computer science, and others. The OSMnx project has recently developed and implemented many new features, modeling capabilities, and analytical methods. The package now encompasses substantially more functionality than was previously documented in the literature. This article introduces OSMnx's modern capabilities, usage, and design -- in addition to the scientific theory and logic underlying them. It shares lessons learned in geospatial software development and reflects on open science's implications for urban modeling and analysis.

En Xu, Huandong Wang, Yunke Zhang et al.

Urban systems are typical examples of complex systems, where the integration of physics-based modeling with artificial intelligence (AI) presents a promising paradigm for enhancing predictive accuracy, interpretability, and decision-making. In this context, AI excels at capturing complex, nonlinear relationships, while physics-based models ensure consistency with real-world laws and provide interpretable insights. We provide a comprehensive review of physics-informed AI methods in urban applications. The proposed taxonomy categorizes existing approaches into three paradigms - Physics-Integrated AI, Physics-AI Hybrid Ensemble, and AI-Integrated Physics - and further details seven representative methods. This classification clarifies the varying degrees and directions of physics-AI integration, guiding the selection and development of appropriate methods based on application needs and data availability. We systematically examine their applications across eight key urban domains: energy, environment, economy, transportation, information, public services, emergency management, and the urban system as a whole. Our analysis highlights how these methodologies leverage physical laws and data-driven models to address urban challenges, enhancing system reliability, efficiency, and adaptability. By synthesizing existing methodologies and their urban applications, we identify critical gaps and outline future research directions, paving the way toward next-generation intelligent urban system modeling.

Soheil Sabri, Sherah Kurnia

Over the past two decades, several governments in developing and developed countries have started their journey toward digital transformation. However, the pace and maturity of digital technologies and strategies are different between public services. Current literature indicates that research on the digital transformation of urban planning is still developing. Therefore, the aim of this study is to understand the influencing factors and key challenges for the digital transformation of urban planning in Australia. The study adopts the inter-organisational theory and Planning Support Science (PSScience) under the Technological, Organisational, and External Environmental (TOE) framework. It involves a multiple case study, administered semi-structured interviews with thirteen IT and urban planning experts across Victoria and New South Wales governments and private industries. The study findings indicate that the main challenges for digital transformation of the Australian urban planning system are related to organisational and external environmental factors. Furthermore, a digital maturity model is absent in the Australian urban planning industry. This study offers important implications to research and practice related to digital transformation in urban planning.

Jingfei Huang, Han Tu

The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating sentiment inconsistency, constructing a dataset encompassing 140,750 Baidu and Tencent Street view images to measure perceptions, and 984,024 Weibo social media text posts to measure opinions. A reaction index is developed, integrating object detection and natural language processing techniques to classify sentiment in Beijing Second Ring for 2016 and 2022. Classified sentiment reaction is analysed and visualized using regression analysis, image segmentation, and word frequency based on land-use distribution to discern underlying factors. The perception affective reaction trend map reveals a shift toward more evenly distributed positive sentiment, while the opinion affective reaction trend map shows more extreme changes. Our mismatch map indicates significant disparities between the sentiments of human perception and opinion of urban areas over the years. Changes in sentiment reactions have significant relationships with elements such as dense buildings and pedestrian presence. Our inconsistent maps present perception and opinion sentiments before and after the pandemic and offer potential explanations and directions for environmental management, in formulating strategies for urban renewal.

Xusen Guo, Mingxing Peng, Xixuan Hao et al.

Web-based participatory urban sensing has emerged as a vital approach for modern urban management by leveraging mobile individuals as distributed sensors. However, existing urban sensing systems struggle with limited generalization across diverse urban scenarios and poor interpretability in decision-making. In this work, we introduce AgentSense, a hybrid, training-free framework that integrates large language models (LLMs) into participatory urban sensing through a multi-agent evolution system. AgentSense initially employs classical planner to generate baseline solutions and then iteratively refines them to adapt sensing task assignments to dynamic urban conditions and heterogeneous worker preferences, while producing natural language explanations that enhance transparency and trust. Extensive experiments across two large-scale mobility datasets and seven types of dynamic disturbances demonstrate that AgentSense offers distinct advantages in adaptivity and explainability over traditional methods. Furthermore, compared to single-agent LLM baselines, our approach outperforms in both performance and robustness, while delivering more reasonable and transparent explanations. These results position AgentSense as a significant advancement towards deploying adaptive and explainable urban sensing systems on the web.

Xin Zhang, Yu Liu, Yuming Lin et al.

Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities. Traditionally, governments heavily depend on field survey methods to monitor the urban villages, which however are time-consuming, labor-intensive, and possibly delayed. Thanks to widely available and timely updated satellite images, recent studies develop computer vision techniques to detect urban villages efficiently. However, existing studies either focus on simple urban village image classification or fail to provide accurate boundary information. To accurately identify urban village boundaries from satellite images, we harness the power of the vision foundation model and adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. Specifically, UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification. Extensive experimental results on two datasets in China demonstrate that UV-SAM outperforms existing baselines, and identification results over multiple years show that both the number and area of urban villages are decreasing over time, providing deeper insights into the development trends of urban villages and sheds light on the vision foundation models for sustainable cities. The dataset and codes of this study are available at https://github.com/tsinghua-fib-lab/UV-SAM.

Wayne Wu, Honglin He, Jack He et al.

Public urban spaces like streetscapes and plazas serve residents and accommodate social life in all its vibrant variations. Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Food delivery bots and electric wheelchairs have started sharing sidewalks with pedestrians, while robot dogs and humanoids have recently emerged in the street. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. Ensuring the generalizability and safety of AI models maneuvering mobile machines is essential. In this work, we present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research. MetaUrban can construct an infinite number of interactive urban scenes from compositional elements, covering a vast array of ground plans, object placements, pedestrians, vulnerable road users, and other mobile agents' appearances and dynamics. We design point navigation and social navigation tasks as the pilot study using MetaUrban for urban micromobility research and establish various baselines of Reinforcement Learning and Imitation Learning. We conduct extensive evaluation across mobile machines, demonstrating that heterogeneous mechanical structures significantly influence the learning and execution of AI policies. We perform a thorough ablation study, showing that the compositional nature of the simulated environments can substantially improve the generalizability and safety of the trained mobile agents. MetaUrban will be made publicly available to provide research opportunities and foster safe and trustworthy embodied AI and micromobility in cities. The code and dataset will be publicly available.

Zhilun Zhou, Yuming Lin, Yong Li

Participatory urban planning is the mainstream of modern urban planning and involves the active engagement of different stakeholders. However, the traditional participatory paradigm encounters challenges in time and manpower, while the generative planning tools fail to provide adjustable and inclusive solutions. This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback iteration, solving a community-level land-use task catering to 1000 distinct interests. Empirical experiments in diverse urban communities exhibit LLM's adaptability and effectiveness across varied planning scenarios. The results were evaluated on four metrics, surpassing human experts in satisfaction and inclusion, and rivaling state-of-the-art reinforcement learning methods in service and ecology. Further analysis shows the advantage of LLM agents in providing adjustable and inclusive solutions with natural language reasoning and strong scalability. While implementing the recent advancements in emulating human behavior for planning, this work envisions both planners and citizens benefiting from low-cost, efficient LLM agents, which is crucial for enhancing participation and realizing participatory urban planning.

Azhar Ramy Ibrahim, Diana Susilowati

Light is an important element in human life. Without lighting, humans cannot see an object visually. It is from the reflection of the light of these objects that humans visualize shapes clearly and see comfortably. The coffee shop as a public space certainly requires adequate lighting so that visual comfort is maintained. Visual comfort will be achieved if the terms and conditions for achieving visual comfort have been fulfilled, including the brightness level according to the recommended standards and the distribution of lighting according to the spatial layout according to the standards. With the aim to determine the natural and artificial lighting conditions in coffee shops and lighting standards for visual comfort, this study will use a comparative research method. Where this method compares lighting standards with research results. The results of this research are that the level of light intensity found outside and inside the building meets the SNI standard at 250 lux by taking data that has an average lux during the day and lux in the afternoon. The conclusion from this study is that BENING coffee & space on one of the visual comfort factors has met the visual comfort standards set by the SNI.

Sadat Momoh Shuaibu, Dervis Kirikkaleli

The empirical investigation of which risk factor—political or financial—is the optimal driver of country risk in emerging economies in the twenty-first century has grown into a significant and volatile issue in recent decades. This paper investigates the linkages between political risk and financial risk in four Balkan economies (i.e., Greece, Albania, Bulgaria, and Romania) from 1984 Q3 to 2018 Q4, using non-linear autoregressive distributed lag co-integration (NARDL) and wavelet coherence approaches. As a result, findings from the links between political risk and financial risk are being used to provide significant insights into effective urban planning in Balkan cities. The outcomes of the NARDAL analysis indicate that there are short-term and long-term asymmetric links between political risk and financial risk in the Balkan countries except for Romania. The wavelet coherence study also revealed that there is significant vulnerability between political risk and financial risk at different frequencies in the region, also, political risk is a key for predicting financial risk over the selected study period at different frequencies in Albania and Bulgaria.

Dania González Couret

This contribution starts with the evolution of the main theoretical approaches through the last thirty years and some key principles for urban sustainability, followed by reflections about sustainable development in Cuba as well as challenges to improve it in urban areas, and closing with some final reflections. Approaches about the sustainable city have been changing during the last three decades, but to be sustainable, a city should be holistically planned in a participatory way, taking as much advantage as possible of the urban land and guaranteeing appropriate domestic environment, by passive energy means. Advanced concepts of integral and sustainable development are not yet applied to the city in Cuba, as there is not enough awareness about its importance for economic and social development. Priorities focus on social services and not enough on housing and habitat. Changing these approaches is one of the first challenges

Ahmet Gün, Burak Pak, Yüksel Demir

Turkey is vulnerable to disasters such as earthquakes, flood, and landslides. Millions of housing units are planned to be demolished and rebuilt in the next 15 years within the scope of urban transformation. The existing legal framework and implementations are hardly participatory and thus cause conflicts. The aims of this study are: 1) to address the weaknesses and strengths of urban transformation practices in Turkey critically with a focus on Istanbul, and 2) to develop a new participatory design model for urban transformation processes by making use of contextualised ICT-based participation tools and techniques. This paper critically maps and discusses urban transformation processes in Istanbul by tracing how these practices take place on the ground and considering the different perspectives of stakeholder groups. We present an applicable model that consists of a system of participatory actions, interaction environments such as an expert collaboration platform developed specifically for Istanbul’s urban transformation process.

Md. Haider Ali Biswas, Pinky Rani Dey, Md. Sirajul Islam et al.

Recently the effect of greenhouse gases (GHGs) is worldwide terrified anxiety to the public and scholars. Even this global problem is one of the great issues that continuously makes worrying the governments and environmentalists, but its solution findings are not out of the image at all. In this study, we have proposed and analysed a mathematical model for the solvable management of GHGs by sowing the seeds of green building dynamic systems. Moreover, in the model, the human community is used to enhance the production power of individuals of green buildings by absorbing the GHGs. The model is analysed by stability analysis at the equilibrium points: trivial and global equilibrium, and also by convincing the stability and instability of the system of equations. The behaviour of the propound model has been developed by numerical simulations which shows the rate of the fruitfulness of GHG components.