Abstract Typo-morphology can be seen as the research perspective and methodology of urban morphology. The terms typo-morphology are typological and morphological because they describe urban forms according to the detailed classification of building types, emphasizing the relationship between building types and overall form in the evolution of urban morphology. In order to better integrate urban morphological research with practice to identify traditional landscape region, it is worthwhile to apply the perspective and method of typo-morphology to deeply recognize the traditional landscape characteristics of the old town based on local types. This study examines the characteristics of GIS platform on the basis of the tradition of Conzen and Caniggia through the case of our urban renewal projects in Changting Town, a historic town in the south of China. First of all, investigation and learning of local building types with traditional characteristics are the tools to understand the basic unit of urban form preliminarily. Secondly, presenting traditional landscape characteristics from two aspects including characteristics of building composition and spatial perception, and doing some simple data overlay analysis helps us to clarify more scientific traditional landscape areas. Finally, we have determined the reasonable traditional landscape region by combining the discussion of other historical information in details. The paper concludes that the term typo-morphology are meaningful in delineating traditional landscape region, which makes an important contribution to the renewal and protection of old town in China.
Road traffic accidents remain a major public health challenge worldwide, with urbanisation and population density identified as key factors influencing risk. This study analyses monthly accident data from 2009 to 2023 across 632 parliamentary constituencies in England, Wales, and Scotland, using an area-normalised approach based on population density. Segmented power law models consistently identified breakpoints separating sublinear rural from superlinear urban scaling behaviours. Seasonal variation in scaling exponents was pronounced in rural regions but less evident in urban ones. Fourier-based cross-spectral analysis of yearly cycles revealed systematic phase shifts: rural exponents lagged pre-exponential factors by 4.5 months, while urban exponents were 2.7 months out of phase, producing a 5.3 month shift between rural and urban exponents. These findings highlight the importance of pre-exponentials-defined as the expected density of accidents at unit population density-as comparable descaled metrics, revealing both long-term national declines and recurring seasonal peaks. Notably, the phase offsets suggest structurally distinct causes of rural and urban accident risk, with urban regions exhibiting increasing acceleration in accident scaling, potentially linked to growth in vehicle numbers, size, and weight. Residuals, modelled with the Type I Generalised Logistic Distribution (GLD), captured skewness and heterogeneity more effectively than normal assumptions. Geospatial mapping highlighted persistent urban hotspots alongside rural and coastal constituencies with systematically lower accident densities than predicted. Together, these findings advance understanding of how density and urbanisation shape accident risk and provide evidence to support more targeted road safety interventions and policy planning.
Migration plays a crucial role in urban growth. Over time, individuals opting to relocate led to vast metropolises like London and Paris during the Industrial Revolution, Shanghai and Karachi during the last decades and thousands of smaller settlements. Here, we analyze the impact that migration has on population redistribution. We use a model of city-to-city migration as a process that occurs within a network, where the nodes represent cities, and the edges correspond to the flux of individuals. We analyze metrics characterizing the urban distribution and show how a slight preference for some destinations might result in the observed distribution of the population.
Unmanned Aerial Vehicles (UAVs) are increasingly populating urban areas for delivery and surveillance purposes. In this work, we develop an optimal navigation strategy based on Deep Reinforcement Learning. The environment is represented by a three-dimensional high-fidelity simulation of an urban flow, characterized by turbulence and recirculation zones. The algorithm presented here is a flow-aware Proximal Policy Optimization (PPO) combined with a Gated Transformer eXtra Large (GTrXL) architecture, giving the agent richer information about the turbulent flow field in which it navigates. The results are compared with a PPO+GTrXL without the secondary prediction tasks, a PPO combined with Long Short Term Memory (LSTM) cells and a traditional navigation algorithm. The obtained results show a significant increase in the success rate (SR) and a lower crash rate (CR) compared to a PPO+LSTM, PPO+GTrXL and the classical Zermelo's navigation algorithm, paving the way to a completely reimagined UAV landscape in complex urban environments.
Understanding how people read city scenes can inform design and planning. We introduce a small benchmark for testing vision-language models (VLMs) on urban perception using 100 Montreal street images, evenly split between photographs and photorealistic synthetic scenes. Twelve participants from seven community groups supplied 230 annotation forms across 30 dimensions mixing physical attributes and subjective impressions. French responses were normalized to English. We evaluated seven VLMs in a zero-shot setup with a structured prompt and deterministic parser. We use accuracy for single-choice items and Jaccard overlap for multi-label items; human agreement uses Krippendorff's alpha and pairwise Jaccard. Results suggest stronger model alignment on visible, objective properties than subjective appraisals. The top system (claude-sonnet) reaches macro 0.31 and mean Jaccard 0.48 on multi-label items. Higher human agreement coincides with better model scores. Synthetic images slightly lower scores. We release the benchmark, prompts, and harness for reproducible, uncertainty-aware evaluation in participatory urban analysis.
Abhijeet Vishwasrao, Sai Bharath Chandra Gutha, Andres Cremades
et al.
Rapid urbanization demands accurate and efficient monitoring of turbulent wind patterns to support air quality, climate resilience and infrastructure design. Traditional sparse reconstruction and sensor placement strategies face major accuracy degradations under practical constraints. Here, we introduce Diff-SPORT, a diffusion-based framework for high-fidelity flow reconstruction and optimal sensor placement in urban environments. Diff-SPORT combines a generative diffusion model with a maximum a posteriori (MAP) inference scheme and a Shapley-value attribution framework to propose a scalable and interpretable solution. Compared to traditional numerical methods, Diff-SPORT achieves significant speedups while maintaining both statistical and instantaneous flow fidelity. Our approach offers a modular, zero-shot alternative to retraining-intensive strategies, supporting fast and reliable urban flow monitoring under extreme sparsity. Diff-SPORT paves the way for integrating generative modeling and explainability in sustainable urban intelligence.
Fouzia Masud Mouri, Ahmad Abdul Wasi, Farida Nilufar
Urban waterbodies play a vital role in enhancing ecological sustainability, social interaction, and neighbourhood liveability, yet in rapidly urbanising contexts such as Dhaka, they are increasingly threatened by encroachment, pollution, and reduced accessibility. This study investigates the accessibility and spatial integration of two significant urban waterbodies (Dholaikhal Narinda Pond and Shahjahanpur Jheel) under the Dhaka City Neighbourhood Upgrading Project (DCNUP), which seeks to revitalise public open spaces for disadvantaged communities. Using Space Syntax analysis, the research assesses spatial configuration through measures of Connectivity (CN), Global Integration (Rn), and Local Integration (R4) to evaluate accessibility at city and neighbourhood scales. Results indicate that Dholaikhal Narinda Pond demonstrates higher connectivity and global integration values, positioning it as a potential city-scale destination capable of enhancing urban inclusivity. By contrast, Shahjahanpur Jheel, though limited in city-wide accessibility, exhibits strong local integration, highlighting its potential as a neighbourhood-scale public space. These findings underscore the importance of aligning spatial analysis with urban design strategies to enhance accessibility and maximise social benefits. The study concludes that integrating syntactic measures into early project planning can help policymakers and urban designers anticipate patterns of use and achieve more equitable and sustainable revitalisation of public waterbodies in Dhaka.
Ronald Sakaya, Abishek S. Narayan, Alex Y. Katukiza
et al.
In urbanising areas, siloed planning for water supply, sanitation, and solid waste management impedes sustainable development. This study examines barriers to integrating services in urban Uganda, focusing on Wobulenzi and Kakooge with different service models. A mixed-method approach using qualitative and quantitative data was applied. Document reviews and interviews with 48 stakeholders were conducted. The Problem-Driven Political Economy Analysis framework was used with thematic analysis and quantitative assessment of political economy attributes. Findings showed fragmented institutions, insufficient resources, and poor coordination hinder integration. Small towns face financial constraints, technological gaps, and political interference. Key attributes influencing integration include implementation dynamics (19/20), accountability (12/20), and collaboration (10/20). An enabling political economy is crucial for integrated services in urban Uganda. Addressing fragmented governance through policies, coordination, decentralisation, capacity building, and financing is essential. Realigning resource planning can enable strategic solutions, promote service integration, and ensure sustainable WASH and SWM services.
گردشگری روستایی جامعهمحور یکی از مهمترین انواع گردشگری است که در دهههای اخیر توجه ویژهای را به خود جلب کرده است. همچنین مفهوم خوداتکایی روستاهای گردشگرپذیر از مفاهیم کلیدی و تأثیرگذار در روستاها است که بسیاری از جوامع در زمینۀ توسعۀ پایدار و خودکفایی محلی آن را دنبال میکنند.تحقیق حاضر از نوع ترکیبی و از نظر هدف، کاربردی است. روش تحقیق آمیخته یا ترکیبی است و از رویکرد متوالی-اکتشافی بهره جسته است. در فاز اول، با استفاده از روش کیفی گروه کانونی، ابعاد و مؤلفههای اصلی مفهوم خوداتکایی از گردشگری روستایی مبتنی بر جامعه در روستاهای گردشگرپذیر شناسایی و استخراج شدند. در مرحلۀ دوم که از روش کمی پیمایش استفاده شده، دربارۀ تأیید و اتقان آنها نظر داده میشود. ابزار گردآوری دادهها و اطلاعات در فاز اول، مطالعات کتابخانهای و مصاحبههای نیمهساختاریافته است. دادهها از 17 تن از خبرگان مرتبط گردآوری شدند. در فاز دوم، با استفاده از پرسشنامه اطلاعاتی جمعآوری شد.مطابق یافتههای تحقیق، 231 کد خام گردآوری شدند که با استفاده از رویکرد استقرایی در قالب 83 مضمون کلیدی، 24 مضمون یکپارچهکننده و چهار مضمون کلان دستهبندی شدند. همچنین در فاز دوم تمامی مؤلفهها و شاخصهای استخراجشده مورد تأیید قرار گرفتند.مطابق نتایج، نقش گردشگری روستایی جامعهمحور، رکنی راهبردی در مفهوم خوداتکایی روستاهای گردشگرپذیر شهرستان سرعین است و استفاده از گردشگری روستایی جامعهمحور میتواند بهعنوان راهبرد احیای جوامع روستایی از منظر توسعۀ پایدار بهکار گرفته شود.
Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic scenes often involve explicit modeling of scene dynamics. However, this approach faces challenges in modeling scene dynamics in urban environments, where moving objects of various categories and scales are present. In such settings, it becomes crucial to effectively eliminate moving objects to accurately reconstruct static backgrounds. Our research introduces an innovative method, termed here as Entity-NeRF, which combines the strengths of knowledge-based and statistical strategies. This approach utilizes entity-wise statistics, leveraging entity segmentation and stationary entity classification through thing/stuff segmentation. To assess our methodology, we created an urban scene dataset masked with moving objects. Our comprehensive experiments demonstrate that Entity-NeRF notably outperforms existing techniques in removing moving objects and reconstructing static urban backgrounds, both quantitatively and qualitatively.
Commuting flow prediction is an essential task for municipal operations in the real world. Previous studies have revealed that it is feasible to estimate the commuting origin-destination (OD) demand within a city using multiple auxiliary data. However, most existing methods are not suitable to deal with a similar task at a large scale, namely within a prefecture or the whole nation, owing to the increased number of geographical units that need to be maintained. In addition, region representation learning is a universal approach for gaining urban knowledge for diverse metropolitan downstream tasks. Although many researchers have developed comprehensive frameworks to describe urban units from multi-source data, they have not clarified the relationship between the selected geographical elements. Furthermore, metropolitan areas naturally preserve ranked structures, like cities and their inclusive districts, which makes elucidating relations between cross-level urban units necessary. Therefore, we develop a heterogeneous graph-based model to generate meaningful region embeddings at multiple spatial resolutions for predicting different types of inter-level OD flows. To demonstrate the effectiveness of the proposed method, extensive experiments were conducted using real-world aggregated mobile phone datasets collected from Shizuoka Prefecture, Japan. The results indicate that our proposed model outperforms existing models in terms of a uniform urban structure. We extend the understanding of predicted results using reasonable explanations to enhance the credibility of the model.
Barrios-Munoz Ricardo, Bernabe Matteo, Lopez-Perez David
et al.
Following the burgeoning interest in unmanned aerial vehicles (UAVs) utilization within human-inhabited spaces, critical challenges arise in ensuring reliable, low-latency communication-particularly important given the safety-critical nature of such operations in densely populated urban environments. Therefore, adequate cellular communication capabilities are essential to enable safe and effective operations within the so-called U-Spaces. In this context, this paper investigates the communication performance of cellular-connected UAVs in dense urban environments. In particular, the analysis is based on a comprehensive measurement campaign conducted in the city of Benidorm, Spain-an urban area well known for its high concentration of tall buildings and overall urban density. More specifically, we evaluated key performance indicators (KPIs) related to received signal strength and quality, data rate, and latency across various altitudes, mobile network operators, access technologies, and frequency bands, using multiple types of measurement equipment. The results highlight significant challenges, primarily due to the lack of dedicated planning for aerial coverage and interference management, revealing that current cellular networks may fall short in supporting reliable and ubiquitous UAVs communication. Thus, this paper calls for improved network solutions to ensure the reliability of UAV operations in urban airspace, thereby contributing to the integration of UAVs into urban logistics and mobility.
The urban-rural consumption gap, as one of the important indicators in social development, directly reflects the imbalance in urban and rural economic and social development. Data elements, as an important component of New Quality Productivity, are of significant importance in promoting economic development and improving people's living standards in the information age. This study, through the analysis of fixed-effects regression models, system GMM regression models, and the intermediate effect model, found that the development level of data elements to some extent promotes the narrowing of the urban-rural consumption gap. At the same time, the intermediate variable of urban-rural income gap plays an important role between data elements and consumption gap, with a significant intermediate effect. The results of the study indicate that the advancement of data elements can promote the balance of urban and rural residents' consumption levels by reducing the urban-rural income gap, providing theoretical support and policy recommendations for achieving common prosperity and promoting coordinated urban-rural development. Building upon this, this paper emphasizes the complex correlation between the development of data elements and the urban-rural consumption gap, and puts forward policy suggestions such as promoting the development of the data element market, strengthening the construction of the digital economy and e-commerce, and promoting integrated urban-rural development. Overall, the development of data elements is not only an important path to reducing the urban-rural consumption gap but also one of the key drivers for promoting the balanced development of China's economic and social development. This study has a certain theoretical and practical significance for understanding the mechanism of the urban-rural consumption gap and improving policies for urban-rural economic development.
Thang-Anh-Quan Nguyen, Luis Roldão, Nathan Piasco
et al.
The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years. However, capturing large-scale scenes still poses a challenge due to their complex geometric structures and unconstrained dynamics. Without the help of 3D motion cues, previous methods often require simplified setups with slow camera motion and only a few/single dynamic actors, leading to suboptimal solutions in most urban setups. To overcome such limitations, we present RoDUS, a pipeline for decomposing static and dynamic elements in urban scenes, with thoughtfully separated NeRF models for moving and non-moving components. Our approach utilizes a robust kernel-based initialization coupled with 4D semantic information to selectively guide the learning process. This strategy enables accurate capturing of the dynamics in the scene, resulting in reduced floating artifacts in the reconstructed background, all by using self-supervision. Notably, experimental evaluations on KITTI-360 and Pandaset datasets demonstrate the effectiveness of our method in decomposing challenging urban scenes into precise static and dynamic components.
Carolina Veiga, Ashish Sharma, Daniel de Oliveira
et al.
The impacts of climate change are intensifying existing vulnerabilities and disparities within urban communities around the globe, as extreme weather events, including floods and heatwaves, are becoming more frequent and severe, disproportionately affecting low-income and underrepresented groups. Tackling these increasing challenges requires novel approaches that integrate expertise across multiple domains, including computer science, engineering, climate science, and public health. Urban computing can play a pivotal role in these efforts by integrating data from multiple sources to support decision-making and provide actionable insights into weather patterns, infrastructure weaknesses, and population vulnerabilities. However, the capacity to leverage technological advancements varies significantly between the Global South and Global North. In this paper, we present two multiyear, multidisciplinary projects situated in Chicago, USA and Niterói, Brazil, highlighting the opportunities and limitations of urban computing in these diverse contexts. Reflecting on our experiences, we then discuss the essential requirements, as well as existing gaps, for visual analytics tools that facilitate the understanding and mitigation of climate-related risks in urban environments.
پژوهشهایی که باهدف ارزیابی تأثیرات اجتماعی- فرهنگی انجام میشوند به دنبال ارتقای کیفیت زندگی، توانمندسازی اجتماعات انسانی و تحقق عدالت اجتماعی هستند. در این پژوهش تلاش شده است تا به ارزیابی پیامدهای اجتماعی و فرهنگی احداث پارکلت کتاب بهمثابه یک فضای شهری کوچک، منعطف و کاربردی در خیابان فخر رازی پرداخته شود. در بخش کمی که از روش پیمایش و ابزار پرسشنامه استفاده شده است، جامعه آماری شامل همه عابرین پیادهای شده از این خیابان استفاده کرده و رفتوآمد کردهاند. شیوه نمونهگیری تصادفی احتمالی و همچنین تعداد نمونه 200 نفر از عابرین پیاده بوده است. بعلاوه در بخش روش کیفی با استفاده آر، تحلیل مضمون، تلاش شده است تا پیامدهای اجتماعی-فرهنگی مرتبط با پارکلت موردبررسی قرار گیرد. یافتهها نشان میدهد پیامدهای احتمالی احداث پروژه شامل تأثیرات مثبت و منفی مانند شکلگیری حوزه عمومی بهویژه برای گروههای کم قدرت، شکلگیری پیوندهای اجتماعی و کردارهای مشارکتی، شکلگیری هویت محلهای و منطقهای، افزایش مشارکت گروههای مختلف، احساس تعلق شهروندان به محله، بهبود کسبوکارهای مجاور وندالیسم. افزایش ترافیک در محدوده و ایجاد معارضه با کسبه محل میشود.
This study examines the relationship between Information and Communication Technology (ICT) and smart governance in smart cities, emphasising the importance of participation and responsiveness for effective governance. Focused on India, it evaluates global smart city governance models, comparing them with the Indian system. The study analyses the current governance structure and the potential of ICT to enhance participatory governance in smart cities. Despite challenges, including limited participation and responsiveness, ICT holds promise for improving governance. The study proposes a governance framework based on pluralist democracy, with ICT playing a central role in fostering collaboration among stakeholders. Despite obstacles like the digital divide, this model prioritises people and ICT within India’s governance, offering insights relevant globally to democratic nations. It presents a new conceptualisation for smart city governance, applicable not only to India but also to democracies worldwide.
Against the background of urban redevelopment programs through the lens of varied, country-related planning cultures, this article sets the stage for learning from one country’s recent experience. In this article, we focus on two Israeli urban regeneration programs operating since 2000: the ‘Integrated National Planning Scheme’ (TAMA 38) and ‘Evacuate and Build’ (Pinuy Binuy) programs. This article aims to examine the governmental agenda behind these programs in comparison to previous urban regeneration programs and, especially, to critically review the implementation and implication of these programs considering social sustainability issues. Methodologically, the policies and practices of this recent wave of urban renewals in Israel are revisited through a critical reading of a series of studies from the last five years, bringing together multidimensional societal aspects. In terms of ethics and qualitative dimensions, the examined societal aspects are rooted in social sustainability theory and contemporary urban policy design. Our findings regarding this country-related recent experience in urban renewals have identified several gaps concerning certain aspects of social sustainability theory and practice. These gaps call for a better understanding by Israel’s urban policy makers of the comprehensive essence of contemporary social sustainability theory, which should also be mirrored in the respective professional discourse. The gaps are also useful in informing our accumulating transnational knowledge and experience in urban renewal schemes, based on a chain of country-related experiences and planning cultures.
Takahiro Yabe, Bernardo Garcia Bulle Bueno, Morgan Frank
et al.
Urban economic resilience is intricately linked to how disruptions caused by pandemics, disasters, and technological shifts ripple through businesses and urban amenities. Disruptions, such as closures of non-essential businesses during the COVID-19 pandemic, not only affect those places directly but also influence how people live and move, spreading the impact on other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other in these situations. Leveraging large-scale human mobility data and millions of same-day visits in New York, Boston, Los Angeles, Seattle, and Dallas, we quantify dependencies between points-of-interest (POIs) encompassing businesses, stores, and amenities. Compared to places' physical proximity, dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of POI categories. We show that using behavior-based dependency relationships improves the predictability of business resilience during shocks, such as the COVID-19 pandemic, by around 40% compared to distance-based models. Simulating hypothetical urban shocks reveals that neglecting behavior-based dependencies can lead to a substantial underestimation of the spatial cascades of disruptions on businesses and urban amenities. Our findings underscore the importance of measuring the complex relationships woven through behavioral patterns in human mobility to foster urban economic resilience to shocks.