Hasil untuk "Urbanization. City and country"

Rujin Huang, Yanlin Zhang, C. Bozzetti et al.

Tianqi Wang, Teemu Jama, Henrikki Tenkanen

Many cities promote walkability through concepts such as the compact city and 15-minute city to enhance urban livability, yet few methods link spatial walkability features to empirically measured livability and account for temporal dynamics. The method developed for this study uses mobile phone data from the Helsinki Metropolitan Area (Finland) to assess whether commonly used, literature-derived livability indicators (diversity, density, proximity, accessibility) predict observed human activity patterns across different times of day. We constructed two key dimensions of livability: attractiveness and walkability with quantifiable sub-indicators that were selected based on literature. Our analysis shows that walkability, and even more so the combined livability index, correlates with activity patterns, outperforming the pure attractiveness perspective. However, this relationship is temporally unstable, significantly weakening at night and fluctuating daily. Moreover, based on Geographically Weighted Regression analysis, our results reveal significant spatial variation in the relationship between livability and the intensity of human activities. The findings suggest that traditional urban planning goals, such as functional diversity to enhance walkability, contribute to livability but have a limited impact on the 15-minute city's overall sustainable mobility objectives, necessitating a larger-scale perspective and more functionally profiled approaches for urban development.

Lindsay W. Gaimaro, Humberto Castillo-Gonzalez, Stephanie Yarwood

Abstract Fairfax County government (Virginia, USA) conducted an extensive survey of urban/suburban forests. Measurements such as tree health, impervious surface, and invasive species was used to calculate a quality index with the iTree tool kit. Building on survey results, our team sampled soils and tree roots in a subset of sites representing a range of forest quality index values. Our goal was to determine if aboveground forest quality correlated to belowground soil biomass, microbial community composition, and mycorrhizal fungal abundance. Soil bacterial/archaeal and fungal communities were quantified (qPCR) and characterized (amplicon sequencing). We observed differences in community composition, but not quantity. Putative functional assignments indicated a decrease in ectomycorrhizal fungi with declining quality and arbuscular mycorrhizal fungal root colonization also decreased. This study demonstrates the crucial above- and belowground connections within urban forests and highlights the need for managers to consider soil biology when assessing ecosystem health.

Qiongyan Wang, Xingchen Zou, Yutian Jiang et al.

Rapid urbanization intensifies the demand for Urban General Intelligence (UGI), referring to AI systems that can understand and reason about complex urban environments. Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs, yet these models exhibit persistent geospatial bias, producing regionally skewed predictions and limited generalization. To this end, we propose Urban-R1, a reinforcement learning-based post-training framework that aligns MLLMs with the objectives of UGI. Urban-R1 adopts Group Relative Policy Optimization (GRPO) to optimize reasoning across geographic groups and employs urban region profiling as a proxy task to provide measurable rewards from multimodal urban data. Extensive experiments across diverse regions and tasks show that Urban-R1 effectively mitigates geo-bias and improves cross-region generalization, outperforming both SFT-trained and closed-source models. Our results highlight reinforcement learning alignment as a promising pathway toward equitable and trustworthy urban intelligence.

Yanina Benedetti, C. Callaghan, I. Ulbrichová et al.

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-meter buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.

Johann S. Schuur, Michal Switalski, Nicolas Salliou et al.

Abstract Growing urban population and contemporary urban systems lock-in unsustainable urban development pathways, deteriorating the living quality of urban dwellers. The systemic complexity of these challenges renders it difficult to find solutions using existing planning processes. Alternatively, transformative planning processes are radical, take place on multiple scales, and are often irreversible; therefore, require the integration of local stakeholders’ perspectives, which are often contradictory. We identify perceived levers of urban transformative change using a serious game to facilitate the integration of these perspectives through simulating neighbourhood transformation processes in two European case studies. Building on existing transformation frameworks, we organize, conceptualize, and compare the effectiveness of these levers through demonstrating their interactions with different scales of transformation. Specifically, drawing from close commonalities between large-scale (Three Spheres of Transformation) and place-based (Place-making) transformation frameworks, we show how these interactions can help to develop recommendations to unlock urban transformative change. Results show that access to participation is a key lever enabling urban transformative change. It appears to be mid-level effective to unlock urban transformative change through interactions with the political sphere of transformation and procedural element of Place-making. Ultimately, however, most effective are those levers that interact with all scales of transformation. For example, by engaging a combination of levers including access to participation, public spaces, parking, place-characteristics and place-identity. These findings could be operationalized by self-organized transformation processes focused on repurposing hard infrastructure into public spaces, whilst ensuring continuity of place-based social- and physical features. Local stakeholders could further use such processes to better understand and engage with their individual roles in the transformative process, because interactions with the personal scale, i.e., personal sphere of transformation appear paramount to unlock urban transformative change.

Amany Saker

Amidst rapid urbanization and escalating environmental concerns, exploring biomimicry and sustainable future architecture has gained significant prominence. This research focuses on elucidating the pivotal role of biomimicry as a transformative design approach for constructing ecologically responsible and energy-efficient architecture. The study initiates a comprehensive exploration of biomimicry as a design philosophy, drawing inspiration from nature's proven solutions. It delves into integrating biomimetic principles into architectural concepts, emphasizing their contribution to shaping a sustainable built environment. Through a detailed analysis, the Eden Project serves as a compelling biomimicry case study, illustrating how this approach addresses diverse challenges architects face. The research concludes by advocating a profound paradigm shift in conceiving and constructing future architecture. By embracing the holistic concept of biomimicry, this approach offers a promising avenue for creating architecture that seamlessly coexists with the natural world, ensuring energy efficiency, thermal comfort, functionality, and resilience for its inhabitants. في ظل التطور الحضري السريع والمخاوف البيئية المتصاعدة، اكتسب استكشاف التقليد البيولوجي والعمارة المستدامة المستقبلية أهمية كبيرة. تركز هذه البحث على توضيح الدور الحيوي للتقليد البيولوجي كنهج تصميمي محوري لإنشاء عمارة مسؤولة بيئيًا وفعالة من حيث الطاقة. يبدأ الدراسة استكشافًا شاملاً للتقليد البيولوجي كفلسفة تصميمية، مستلهمًا من الحلول المثبتة من قبل الطبيعة. وتتناول الدراسة دمج مبادئ التقليد البيولوجي في مفاهيم العمارة، مؤكدة إسهامها في تشكيل بيئة بنائية مستدامة. من خلال تحليل مفصل، يعتبر مشروع إيدين (Eden Project) دراسة حالة متقنة للتقليد البيولوجي، موضحًا كيفية معالجة هذا النهج للتحديات المتنوعة التي يواجهها المهندسون المعماريون. تختتم البحث بالدعوة إلى تحول نمطي عميق في تصور وإنشاء العمارة المستقبلية. من خلال تبني مفهوم التقليد البيولوجي الشامل، يقدم هذا النهج مساراً واعداً لخلق عمارة تتعايش بسلاسة مع العالم الطبيعي، مضمونًا ليس فقط كفاءة الطاقة والراحة الحرارية، ولكن أيضًا الوظيفية والمرونة لسكانها.

Gustavo Adolfo Carabajal, Mauro Marzo

Ayad Ghany Ismaeel, S. J. Jereesha Mary, C. Anitha et al.

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large scale data sets.

Zihao Jiao, Mengyi Sha, Haoyu Zhang et al.

Existing operations research (OR) models and tools play indispensable roles in smart-city operations, yet their practical implementation is limited by the complexity of modeling and deficiencies in optimization proficiency. To generate more relevant and accurate solutions to users' requirements, we propose a large language model (LLM)-based agent ("City-LEO") that enhances the efficiency and transparency of city management through conversational interactions. Specifically, to accommodate diverse users' requirements and enhance computational tractability, City-LEO leverages LLM's logical reasoning capabilities on prior knowledge to scope down large-scale optimization problems efficiently. In the human-like decision process, City-LEO also incorporates End-to-end (E2E) model to synergize the prediction and optimization. The E2E framework be conducive to coping with environmental uncertainties and involving more query-relevant features, and then facilitates transparent and interpretable decision-making process. In case study, we employ City-LEO in the operations management of e-bike sharing (EBS) system. The numerical results demonstrate that City-LEO has superior performance when benchmarks against the full-scale optimization problem. With less computational time, City-LEO generates more satisfactory and relevant solutions to the users' requirements, and achieves lower global suboptimality without significantly compromising accuracy. In a broader sense, our proposed agent offers promise to develop LLM-embedded OR tools for smart-city operations management.

Inês Tavares

A pandemia gerada pela Covid-19 teve impacto em diversas áreas, desocultando e intensificando as desigualdades sociais. O presente artigo analisa a influência da pandemia na reprodução de desigualdades sociais na educação, partindo do reconhecimento do efeito cíclico que a escola assume na reprodução de desigualdades e tendo presente a relação entre os recursos expressos através dos diversos tipos de capitais e o desempenho escolar.Pretende-se, assim, compreender como a pandemia potenciou a reprodução de desigualdades, tendo em foco o contexto português e analisando as consequências em dois prismas: imediatas e a longo prazo. Sabendo que as desigualdades se apuram também numa perspetiva macro, na medida em que as diferentes desigualdades estão interligadas entre si e que a carência de diferentes capitais tem impacto em várias áreas, nas quais a educação se inclui, quais são os alunos mais penalizados? Que desigualdades são mais tangíveis?A presente investigação utiliza também como fundamentação empírica uma base de dados que engloba todos os alunos inscritos no sistema de ensino púbico português, nos anos letivos 2018/19 e 2019/20, de forma a explorar as diferenças verificadas no primeiro ano de pandemia, as consequências imediatas. Assim, analisam-se as desigualdades económicas, sociais, escolares e territoriais que a pandemia causou, bem como os seus diferentes impactos nos resultados escolares.

Chanyoung Park, Gyu Seon Kim, Soohyun Park et al.

The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this paper proposes a novel air transportation service management algorithm based on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM cooperation. Specifically, the proposed algorithm in this paper is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. Furthermore, this paper adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. Furthermore, there are no inferior UAMs by utilizing parameter sharing in CommNet and a centralized critic network in CTDE. Therefore, it can be confirmed that the research results in this paper can provide a promising solution for autonomous air transportation management systems in city-wide urban areas.

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.

Md. Ahsan Habib, M. A. H. Akhand

Mobility is a key factor in urban life and transport network plays a vital role in mobility. Worse transport network having less mobility is one of the key reasons to decline the living standard in any unplanned mega city. Transport mobility enhancement in an unplanned mega city is always challenging due to various constraints including complex design and high cost involvement. The aim of this thesis is to enhance transport mobility in a megacity introducing a bicycle lane. To design the bicycle lane natural Physarum, brainless single celled multi-nucleated protist, is studied and modified for better optimization. Recently Physarum inspired techniques are drawn significant attention to the construction of effective networks. Exiting Physarum inspired models effectively and efficiently solves different problems including transport network design and modification and implication for bicycle lane is the unique contribution of this study. Central area of Dhaka, the capital city of Bangladesh, is considered to analyze and design the bicycle lane network bypassing primary roads.

Ali Usman Gondal, Muhammad Imran Sadiq, Tariq Ali et al.

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

Sreechanth Sundaram, Suresh Devaraj, K. Yarrakula

Ilaria Delponte, Valentina Costa, Ennio Cascetta et al.

Port-City interface is becoming increasingly pivotal in both urban and infrastructural sustainable development. Urban centers tend to regain their overlook on the sea, while “gigantic” ships require ports to become bigger and bigger. These convergent processes frequently lead to conflicts and unsolved issues. This is the reason why solutions are often searched in defining specifical and dedicated areas and routes to reduce interferences. Buffer Areas for logistics-related operations and procedures are often mentioned. The present work concerns the stakeholders’ engagement process conducted in order to evaluate most suitable areas and relevant features to host these activities before freight vehicles reach the proper port area, thus reducing externalities on ordinary traffic flows. In particular, in-depth interviews to several stakeholders of Genoese Port community were conducted and their results were later mainstreamed into a multi-criteria analysis. Despite not being a structured participatory process, the present methodology could help defining intervention priorities and identifying the added value of this kind of facilities for different members of local port community.

Berenike Masing, Niels Lindner, Ralf Borndörfer

We consider the line planning problem in public transport in the Parametric City, an idealized model that captures typical scenarios by a (small) number of parameters. The Parametric City is rotation symmetric, but optimal line plans are not always symmetric. This raises the question to quantify the symmetry gap between the best symmetric and the overall best solution. For our analysis, we formulate the line planning problem as a mixed integer linear program, that can be solved in polynomial time if the solutions are forced to be symmetric. The symmetry gap is provably small when a specific Parametric City parameter is fixed, and we give an approximation algorithm for line planning in the Parametric City in this case. While the symmetry gap can be arbitrarily large in general, we show that symmetric line plans are a good choice in most practical situations.

Emily Muller, Emily Gemmell, Ishmam Choudhury et al.

The interactions of individuals with city neighbourhoods is determined, in part, by the perceived quality of urban environments. Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents. Large-scale assessment of perceptions of neighbourhood quality was pioneered by the Place Pulse projects. Researchers demonstrated the efficacy of crowd-sourcing perception ratings of image pairs across 56 cities and training a model to predict perceptions from street-view images. Variation across cities may limit Place Pulse's usefulness for assessing within-city perceptions. In this paper, we set forth a protocol for city-specific dataset collection for the perception: 'On which street would you prefer to walk?'. This paper describes our methodology, based in London, including collection of images and ratings, web development, model training and mapping. Assessment of within-city perceptions of neighbourhoods can identify inequities, inform planning priorities, and identify temporal dynamics. Code available: https://emilymuller1991.github.io/urban-perceptions/.

Qiance Liu, Z. Cao, Xiaojie Liu et al.

The rapid urbanization in China since the 1970s has led to an exponential growth of metal stocks (MS) in use in cities. A retrospect on the quantity, quality, and patterns of these MS is a prerequisite for projecting future metal demand, identifying urban mining potentials of metals, and informing sustainable urbanization strategies. Here, we deployed a bottom-up stock accounting method to estimate stocks of iron, copper, and aluminum embodied in 51 categories of products and infrastructure across 10 Chinese megacities from 1980 to 2016. We found that the MS in Chinese megacities had reached a level of 2.6-6.3 t/cap (on average 3.7 t/cap for iron, 58 kg/cap for copper, and 151 kg/cap for aluminum) in 2016, which still remained behind the level of western cities or potential saturation level on the country level (e.g., approximately 13 t/cap for iron). Economic development was identified as the most powerful driver for MS growth based on an IPAT decomposition analysis, indicating further increase in MS as China's urbanization and economic growth continues in the next decades. The latecomer cities should therefore explore a wide range of strategies, from urban planning to economy structure to regulations, for a transition toward more "metal-efficient" urbanization pathways.