<p>The examination of the iwan as one of the principal elements of architecture and climate in the historical houses of Iran's hot and dry climate has always held particular significance. This research aims to classify the iwans in the historical houses of the Qajar period in Shiraz, analyzing this element from situational, morphological, and functional perspectives. The research methodology is descriptive-analytical, and the data collection method involves both library and field studies. The statistical population consists of 72 historically registered houses in the national heritage of Shiraz from the Qajar period, among which 15 houses are purposefully selected as samples. The findings of the research indicate that, broadly speaking, the identified porticoes can be categorized into four main types: the principal portico, the subordinate portico, the columned portico, and the small portico, all of which predominantly take on a rectangular form. However, they can be aesthetically divided into two varieties: simple and columned. These porticoes are directly connected to the courtyard, linking it to the main areas of the house, such as the rooms, the large hall, or the five- and three-bay spaces. In terms of their placement and positioning among the spaces, porticoes can be classified into three categories: 1) the principal portico situated in the southwest corner of the courtyard, 2) the subordinate portico located centrally on the eastern front, and 3) the small portico and columned portico positioned in the southwest, facing the summer sitting area. Regarding orientation and elongation, there are two types: 1) those stretching east to west and 2) those extending north to south. Nevertheless, all principal, subordinate, and columned porticoes in these houses share a uniform height relative to the floor, specifically positioned at a meter above the level of the central courtyard. In contrast, only the small porticoes exhibit varied heights and are constructed at different levels. The function of iwans in some houses is purely aesthetic and communicative, while in others, such as the houses of Azadeh Hosseini and Saber, they serve climatic roles, providing thermal comfort for residents. This study reveals that iwans in the Qajar houses of Shiraz exhibit significant convergence and commonality in terms of form, proportions, and morphological patterns. Their situational patterns and orientations also follow a specific order and harmony, predominantly situated in similar locations relative to the central courtyard and primary spaces of the house. This convergence reflects a cohesive and purposeful approach in the architecture of the Qajar houses of Shiraz, shaped by aesthetic, functional, and climatic principles.</p>
Urban groups. The city. Urban sociology, Architecture
Nicolò Cenni, Marina Barbiroli, Vittorio Degli-Esposti
et al.
The evolution toward 6G communication systems is expected to rely on integrated three-dimensional network architectures where terrestrial infrastructures coexist with non-terrestrial stations such as satellites, enabling ubiquitous connectivity and service continuity. In this context, accurate channel models for satellite-to-ground propagation in urban environments are essential, particularly for user equipment located at street level where obstruction and multipath effects are significant. This work investigates satellite-to-urban propagation through deterministic ray-tracing simulations. Three representative urban layouts are considered, namely dense urban, urban, and suburban. Multiple use cases are investigated, including handheld devices, vehicular terminals, and fixed rooftop receivers operating across several frequency bands. The analysis focuses on the relative importance of competing propagation mechanisms and on two key channel parameters, namely the Rician K-factor and the delay spread, which are relevant for the calibration of channel models to be used in link- and system-level simulations. Results highlight the strong - and in some cases unconventional - dependence of channel dispersion and fading characteristics on satellite elevation, antenna placement, and urban morphology.
This paper introduces Virtual Urbanism (VU), a multimodal AI-driven analytical framework for quantifying urban identity through the medium of synthetic urban replicas. The framework aims to advance computationally tractable urban identity metrics. To demonstrate feasibility, the pilot study Virtual Urbanism and Tokyo Microcosms is presented. A pipeline integrating Stable Diffusion and LoRA models was used to produce synthetic replicas of nine Tokyo areas rendered as dynamic synthetic urban sequences, excluding existing orientation markers to elicit core identity-forming elements. Human-evaluation experiments (I) assessed perceptual legitimacy of replicas; (II) quantified area-level identity; (III) derived core identity-forming elements. Results showed a mean identification accuracy of ~81%, confirming the validity of the replicas. Urban Identity Level (UIL) metric enabled assessment of identity levels across areas, while semantic analysis revealed culturally embedded typologies as core identity-forming elements, positioning VU as a viable framework for AI-augmented urban analysis, outlining a path toward automated, multi-parameter identity metrics.
Understanding and predicting pedestrian dynamics has become essential for shaping safer, more responsive, and human-centered urban environments. This study conducts a comprehensive scientometric analysis of research on data-driven pedestrian trajectory prediction and crowd simulation, mapping its intellectual evolution and interdisciplinary structure. Using bibliometric data from the Web of Science Core Collection, we employ SciExplorer and Bibliometrix to identify major trends, influential contributors, and emerging frontiers. Results reveal a strong convergence between artificial intelligence, urban informatics, and crowd behavior modeling--driven by graph neural networks, transformers, and generative models. Beyond technical advances, the field increasingly informs urban mobility design, public safety planning, and digital twin development for smart cities. However, challenges remain in ensuring interpretability, inclusivity, and cross-domain transferability. By connecting methodological trajectories with urban applications, this work highlights how data-driven approaches can enrich urban governance and pave the way for adaptive, socially responsible mobility intelligence in future cities.
Mohammad Mehdi Oshanreh, Daniel Malarkey, Don MacKenzie
This study examines the impact of AI-based feedback and speed restrictions on reducing sidewalk riding in shared e-scooters. In partnership with Spin, 100 scooters in Santa Monica, California were fitted with computer vision, with feedback features activated on half. Data from 488 trips revealed that feedback-equipped scooters spent 22% less time and 20% less distance on sidewalks. Nearly half of riders used sidewalks for less than 10% of their trip, while around 10% spent over 60% of trip time on sidewalks, regardless of feedback. These results suggest AI feedback modifies behavior but doesn't fundamentally change diverse riding patterns.
Transportation and communications, Urban groups. The city. Urban sociology
Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, failing to capture real-time physical changes. While street view imagery, capturing the heartbeat of urban spaces from a pedestrian point of view, can add as a high-definition, up-to-date, and on-the-ground visual proxy of urban change. We curate the largest street view time series dataset to date, and propose an end-to-end change detection model to effectively capture physical alterations in the built environment at scale. We demonstrate the effectiveness of our proposed method by benchmark comparisons with previous literature and implementing it at the city-wide level. Our approach has the potential to supplement existing dataset and serve as a fine-grained and accurate assessment of urban change.
Chuhan Yang, Fares B. Mehouachi, Monica Menendez
et al.
Predicting traffic flow in data-scarce cities is challenging due to limited historical data. To address this, we leverage transfer learning by identifying periodic patterns common to data-rich cities using a customized variant of Dynamic Mode Decomposition (DMD): constrained Hankelized DMD (TrHDMD). This method uncovers common eigenmodes (urban heartbeats) in traffic patterns and transfers them to data-scarce cities, significantly enhancing prediction performance. TrHDMD reduces the need for extensive training datasets by utilizing prior knowledge from other cities. By applying Koopman operator theory to multi-city loop detector data, we identify stable, interpretable, and time-invariant traffic modes. Injecting ``urban heartbeats'' into forecasting tasks improves prediction accuracy and has the potential to enhance traffic management strategies for cities with varying data infrastructures. Our work introduces cross-city knowledge transfer via shared Koopman eigenmodes, offering actionable insights and reliable forecasts for data-scarce urban environments.
In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized Large Language Model tailored for urban and spatial planning. Developed through collaborative efforts with institutions like the Chinese Academy of Urban Planning, PlanGPT leverages a customized local database retrieval framework, domain-specific fine-tuning of base models, and advanced tooling capabilities. Empirical tests demonstrate that PlanGPT has achieved advanced performance, delivering responses of superior quality precisely tailored to the intricacies of urban planning.
The growth of urban areas intensifies the need for sustainable, efficient transportation infrastructure and mobility systems, driving initiatives to enhance infrastructure and public transit while reducing traffic congestion and emissions. By utilizing real-world data, a data-driven approach can provide crucial insights for urban mobility planning and decision-making. This study explores the efficacy of leveraging telecoms data from cellular network signals for studying crowd movement patterns, focusing on Trondheim, Norway. It examines routing reports to understand the spatiotemporal dynamics of various transportation routes and modes. A data preprocessing and feature engineering framework was developed to process raw routing reports for historical analysis. This enabled the examination of geospatial trends and temporal patterns, including a comparative analysis of various transportation modes, along with public transit usage. Specific routes and areas were analyzed in-depth to compare their mobility patterns with the broader city context. The study highlights the potential of cellular network data as a resource for shaping urban transportation and mobility systems. By identifying deficiencies and potential improvements, city planners and stakeholders can foster more sustainable and effective transportation and mobility solutions.
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.
Genetic algorithm (GA) are based on the continuation of fitter ones’ lives considering the natural evolution. Data are coded as genes in the genetic algorithms. Optimal solutions can be achieved through the methods of crossing and mutation performed on these coded genes. Facade elements of the buildings with an architectural design in this study are independent of sustainability-related concerns, suggesting a great issue for the new buildings to be constructed in the traditional pattern. Accordingly, using the genetic algorithm method, proposals were presented for the new door and window typologies with genetic fitness for the architectural designing process of the buildings to be constructed in Suriçi Region, Diyarbakır, Turkey. Shape grammar, fractal and genetic algorithm, three generative designing systems, were used as the methods. Utilizing the genetic algorithm method, a field study was performed for the proposal of new door and window typologies with the fitness value. The field study was assessed through the plans and facade analyses regarding six Diyarbakır traditional houses with U plan type in Suriçi region of Diyarbakır. An identity card was created for the plan and facade data of the buildings and transferred to the table. Then, the door and window typologies of the exterior facade elements of each examined building were crossed within themselves with the GA method. As a result of the crossover, alternative joinery typologies with a total of 31 windows and 53 different door typologies with compatibility values were produced. Thus, the sustainability of the data of traditional joinery typologies for use in contemporary houses has been ensured. In conclusion, optimal alternative typologies were presented in regard to every chopping typology assessed with the genetic algorithm method. It is thought that this study should be a method that can be used in the production of exterior joinery typologies of contemporary houses to be built in many different cities of our country, especially in the historical texture. Thus, by using the GA method for the production of exterior joinery typologies of contemporary houses to be built in the region, different designers will be able to obtain various designs compatible with the traditional architectural texture while preserving their originality.
Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress in neural rendering. While most of recent neural rendering works focus on objects and small-scale scenes, developing neural rendering methods for city-scale scenes is of great potential in many real-world applications. However, this line of research is impeded by the absence of a comprehensive and high-quality dataset, yet collecting such a dataset over real city-scale scenes is costly, sensitive, and technically difficult. To this end, we build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we develop a pipeline to easily collect aerial and street city views, accompanied by ground-truth camera poses and a range of additional data modalities. Flexible controls over environmental factors like light, weather, human and car crowd are also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size $28km^2$. On top of MatrixCity, a thorough benchmark is also conducted, which not only reveals unique challenges of the task of city-scale neural rendering, but also highlights potential improvements for future works. The dataset and code will be publicly available at our project page: https://city-super.github.io/matrixcity/.
Remotely sensed nighttime lights (NTL) uniquely capture urban change processes that are important to human and ecological well-being, such as urbanization, socio-political conflicts and displacement, impacts from disasters, holidays, and changes in daily human patterns of movement. Though several NTL products are global in extent, intrinsic city-specific factors that affect lighting, such as development levels, and social, economic, and cultural characteristics, are unique to each city, making the urban processes embedded in NTL signatures difficult to characterize, and limiting the scalability of urban change analyses. In this study, we propose a data-driven approach to detect urban changes from daily satellite-derived NTL data records that is adaptive across cities and effective at learning city-specific temporal patterns. The proposed method learns to forecast NTL signatures from past data records using neural networks and allows the use of large volumes of unlabeled data, eliminating annotation effort. Urban changes are detected based on deviations of observed NTL from model forecasts using an anomaly detection approach. Comparing model forecasts with observed NTL also allows identifying the direction of change (positive or negative) and monitoring change severity for tracking recovery. In operationalizing the model, we consider ten urban areas from diverse geographic regions with dynamic NTL time-series and demonstrate the generalizability of the approach for detecting the change processes with different drivers and rates occurring within these urban areas based on NTL deviation. This scalable approach for monitoring changes from daily remote sensing observations efficiently utilizes large data volumes to support continuous monitoring and decision making.
City-scale outdoor thermal comfort diagnostics are essential for understanding actual heat stress. However, previous research primarily focused on the street scale. Here, we present the WRF-UCM-SOLWEIG framework to achieve fine-grained thermal comfort mapping at the city scale. The background climate condition affecting thermal comfort is simulated by the Weather Research and Forecasting (WRF) model coupled with the urban canopy model (UCM) at a local-scale (500m). The most dominant factor, mean radiant temperature, is simulated using the Solar and Longwave Environmental Irradiance Geometry (SOLWEIG) model at the micro-scale (10m). The Universal Thermal Climate Index (UTCI) is calculated based on the mean radiant temperature and local climate parameters. The influence of different ground surface materials, buildings, and tree canopies is simulated in the SOLWEIG model using integrated urban morphological data. We applied this proposed framework to the city of Guangzhou, China, and investigated the intra-day variation in the impact of urban morphology during a heat wave period. Through statistical analysis, we found that the elevation in UTCI is primarily attributed to the increase in the fraction of impervious surface (ISF) during daytime, with a maximum correlation coefficient of 0.80. Tree canopy cover has a persistent cooling effect during the day. Implementing 40% of tree cover can reduce the daytime UTCI by 1.5 to 2.0 K. At nighttime, all urban features have a negligible contribution to outdoor thermal comfort. Overall, the established framework provides essential input data and references for studies and urban planners in the practice of urban (micro)climate diagnostics and planning.
O presente trabalho tem por meta apresentar duas exposições nacionais promovidas pelo Estado Novo Brasileiro (1937-45): A Exposição Nacional do Estado Novo (RJ-1938) e a Grande Exposição Nacional de Pernambuco (PE-1939). Tomando por base a primeira edição da Mostra della Rivoluzione Fascista (1932), o artigo almeja evidenciar como as mostras nacionais getulistas O presente trabalho tem por meta apresentar duas exposições nacionais promovidas pelo Estado Novo Brasileiro (1937-45): A Exposição Nacional do Estado Novo (RJ-1938) e a Grande Exposição Nacional de Pernambuco (PE-1939). Tomando por base a primeira edição da Mostra della Rivoluzione Fascista (1932), o artigo almeja evidenciar como as mostras nacionais getulistas apresentavam similaridades discursivas e programáticas com a exposição fascista e como esses eventos foram planejados e executados para a criação de espaço de consenso político e ideológico.
Architecture, Urban groups. The city. Urban sociology
Celem badania jest ukazanie zróżnicowania powiatowych rynków pracy województwa wielkopolskiego ze względu na wysokość stóp bezrobocia rejestrowanego i charakter bezrobocia, a także ustalenie najważniejszych czynników wpływających na te wielkości. Podstawą badania są dane roczne z lat 2010–2021 pochodzące z Banku Danych Lokalnych GUS oraz sprawozdań powiatowych urzędów pracy (formularzy MRPiPS-01). Wyniki pozwalają stwierdzić, że choć województwo wielkopolskie charakteryzuje jedna z najniższych stóp bezrobocia w kraju, to jednak wewnętrznie jest ono dość zróżnicowane. Relatywnie najtrudniejsza sytuacja ze względu na stopę i charakter bezrobocia dotyczy powiatu konińskiego, natomiast najkorzystniejsza występuje w powiatach poznańskim, miasta Poznania, nowotomyskim i wolsztyńskim.
Political science, Urban groups. The city. Urban sociology
Two approaches to rationality characterize the understanding of management accounting and control: one descriptive, based on empirical research and allowing for a variety of versions of rationality; the other normative, explained to undergraduate students through voluminous textbooks on management accounting and control and based predominantly on the idea of formal rationality. Whereas the descriptive approach is often presented as a contrast to the normative approach, the normative approach seems largely independent of empirical research or new management methods. It is suggested that the teachings of management accounting and control be based on insights from academic research prevalent within the descriptive approach to rationality.
The Covid-19 pandemic has been a serious challenge to the entire global community. Globally, countries were forced to introduce restrictive measures to contain the infection, inevitably causing popular discontent. Germany introduced some of the most painful restrictions. In times of crisis, timely and reliable information is a prerequisite for public motivation to comply with restrictive measures. Thus, it seems essential to retrace how the German leadership tried to contain citizens’ dissatisfaction with the restrictions, using information campaigns and strategies. This theoretical work aims to systematise available data on how Covid-awareness was raised in Germany, compare them with data from the Russian Federation, and identify the most successful communication strategies and weaknesses. It is clear from the findings that the channels of communication between the government and society should be diversified using all available means, and experts and opinion leaders, who are more trusted than politicians, should be recruited. In addition, there is a need to combat misinformation and dispel unproven facts. The data obtained can be of value in conducting information campaigns during future global crises.
José Mario Mayorga Henao, Laura Milena Hernández, María Camila Lozano
El artículo tiene como objetivos identificar los patrones de distribución de la pobreza multidimensional en las principales aglomeraciones urbanas del Sistema de Ciudades colombiano y estimar la magnitud de la segregación residencial entre la población pobre y no pobre asentada en estos territorios. A partir de una reflexión conceptual sobre la pobreza y la segregación, se plantea un estudio comparativo para establecer cómo este fenómeno se presenta en el Sistema de Ciudades, mediante la implementación de múltiples métodos de análisis espacial y estadístico que permiten construir índices a diferentes escalas. Lo anterior, permite concluir que el fenómeno de la segregación residencial presenta particularidades en sus patrones de distribución geográfica al interior de cada aglomeración urbana.
Urban groups. The city. Urban sociology, City planning