Hasil untuk "Urban groups. The city. Urban sociology"

Chongyu Wang, Kunlei Jing, Jihua Zhu et al.

Open-vocabulary semantic segmentation enables models to recognize and segment objects from arbitrary natural language descriptions, offering the flexibility to handle novel, fine-grained, or functionally defined categories beyond fixed label sets. While this capability is crucial for large-scale urban point clouds that support applications such as digital twins, smart city management, and urban analytics, it remains largely unexplored in this domain. The main obstacles are the frequent absence of high-quality, well-aligned multi-view imagery in large-scale urban point cloud datasets and the poor generalization of existing three-dimensional (3D) segmentation pipelines across diverse urban environments with substantial variation in geometry, scale, and appearance. To address these challenges, we present OpenUrban3D, the first 3D open-vocabulary semantic segmentation framework for large-scale urban scenes that operates without aligned multi-view images, pre-trained point cloud segmentation networks, or manual annotations. Our approach generates robust semantic features directly from raw point clouds through multi-view, multi-granularity rendering, mask-level vision-language feature extraction, and sample-balanced fusion, followed by distillation into a 3D backbone model. This design enables zero-shot segmentation for arbitrary text queries while capturing both semantic richness and geometric priors. Extensive experiments on large-scale urban benchmarks, including SensatUrban and SUM, show that OpenUrban3D achieves significant improvements in both segmentation accuracy and cross-scene generalization over existing methods, demonstrating its potential as a flexible and scalable solution for 3D urban scene understanding.

Yanghong Mei, Yirong Yang, Longteng Guo et al.

Navigating complex urban environments using natural language instructions poses significant challenges for embodied agents, including noisy language instructions, ambiguous spatial references, diverse landmarks, and dynamic street scenes. Current visual navigation methods are typically limited to simulated or off-street environments, and often rely on precise goal formats, such as specific coordinates or images. This limits their effectiveness for autonomous agents like last-mile delivery robots navigating unfamiliar cities. To address these limitations, we introduce UrbanNav, a scalable framework that trains embodied agents to follow free-form language instructions in diverse urban settings. Leveraging web-scale city walking videos, we develop an scalable annotation pipeline that aligns human navigation trajectories with language instructions grounded in real-world landmarks. UrbanNav encompasses over 1,500 hours of navigation data and 3 million instruction-trajectory-landmark triplets, capturing a wide range of urban scenarios. Our model learns robust navigation policies to tackle complex urban scenarios, demonstrating superior spatial reasoning, robustness to noisy instructions, and generalization to unseen urban settings. Experimental results show that UrbanNav significantly outperforms existing methods, highlighting the potential of large-scale web video data to enable language-guided, real-world urban navigation for embodied agents.

Tianyuan Huang, Zejia Wu, Jiajun Wu et al.

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.

Yanguang Chen

Fractal dimension is an effective scaling exponent of characterizing scale-free phenomena such as cities. Urban growth can be described with time series of fractal dimension of urban form. However, how to explain the factors behind fractal dimension sequences that affect fractal urban growth remains a problem. This paper is devoted to developing a method of logistic regression modeling, which can be employed to find the influencing factors of urban growth and rank them in terms of importance. The logistic regression model comprises three components. The first is a linear function indicating the relationship between time dummy and influencing variables. The second is a logistic function linking fractal dimension and time dummy. The third is a ratio function representing normalized fractal dimension. The core composition is the logistic function that implies the dynamics of spatial replacement. The logistic regression modeling can be extended to other spatial replacement phenomena such as urbanization, traffic network development, and technology innovation diffusion. This study contributes to the development of quantitative analysis tools based on the combination of fractal geometry and conventional mathematical methods.

Alice Battiston, Rossano Schifanella

With the recent expansion of urban greening interventions, the definition of spatial indicators to measure the provision of urban greenery has become of pivotal importance in informing the policy-design process. By analyzing the stability of the population and area rankings induced by several indicators of green accessibility for over 1,000 cities worldwide, we investigate the extent to which the use of a single metric provides a reliable assessment of green accessibility in a city. The results suggest that, due to the complex interaction between the spatial distribution of greenspaces in an urban center and its population distribution, the use of a single indicator might lead to insufficient discrimination across areas or subgroups of the population, even when focusing on one form of green accessibility. From a policy perspective, this indicates the need to switch toward a multi-dimensional framework that is able to organically evaluate a range of indicators at once.

Alperen Enes Bayar, Ufuk Uyan, Elif Toprak et al.

Urban environments are characterized by complex structures and diverse features, making accurate segmentation of point cloud data a challenging task. This paper presents a comprehensive study on the application of RandLA-Net, a state-of-the-art neural network architecture, for the 3D segmentation of large-scale point cloud data in urban areas. The study focuses on three major Chinese cities, namely Chengdu, Jiaoda, and Shenzhen, leveraging their unique characteristics to enhance segmentation performance. To address the limited availability of labeled data for these specific urban areas, we employed transfer learning techniques. We transferred the learned weights from the Sensat Urban and Toronto 3D datasets to initialize our RandLA-Net model. Additionally, we performed class remapping to adapt the model to the target urban areas, ensuring accurate segmentation results. The experimental results demonstrate the effectiveness of the proposed approach achieving over 80\% F1 score for each areas in 3D point cloud segmentation. The transfer learning strategy proves to be crucial in overcoming data scarcity issues, providing a robust solution for urban point cloud analysis. The findings contribute to the advancement of point cloud segmentation methods, especially in the context of rapidly evolving Chinese urban areas.

Junwei Ma, Ali Mostafavi

Understanding the relationship between urban form and structure and spatial variation of property flood risk has been a longstanding challenge in urban planning and city flood risk management. Yet limited data-driven insights exist regarding the extent to which variation in spatial inequality of property flood risk in cities can be explained by heterogenous features of urban form and structure. In this study, we explore eight key features (i.e., population density, point of interest density, road density, minority segregation, income segregation, urban centrality index, gross domestic product, and human mobility index) related to urban form and structure to explain variability in spatial inequality of property flood risk among 2567 US counties. Using rich datasets related to property flood risk, we quantify spatial inequality in property flood risk and delineate features of urban form and structure using high-resolution human mobility and facility distribution data. We identify significant variation in spatial inequality of property flood risk among US counties with coastline and metropolitan counties having the greatest spatial inequality of property flood risk. The results also reveal variations in spatial inequality of property flood risk can be effectively explained based on principal components of development density, economic activity, and centrality and segregation. Using a classification and regression tree model, we demonstrate how these principal components interact and form pathways that explain levels of spatial inequality in property flood risk in US counties. The findings offer important insights for the understanding of the complex interplay between urban form and structure and spatial inequality of property flood risk and have important implications for integrated urban design strategies to address property flood risk as cities continue to expand and develop.

Taiwo Adetiloye, Anjali Awasthi

The logistics of urban areas are becoming more sophisticated due to the fast city population growth. The stakeholders are faced with the challenges of the dynamic complexity of city logistics(CL) systems characterized by the uncertainty effect together with the freight vehicle emissions causing pollution. In this conceptual paper, we present a research methodology for the environmental sustainability of CL systems that can be attained by effective stakeholders' collaboration under non-chaotic situations and the presumption of the human levity tendency. We propose the mathematical axioms of the uncertainty effect while putting forward the notion of condition effectors, and how to assign hypothetical values to them. Finally, we employ a spider network and causal loop diagram to investigate the system's elements and their behavior over time.

Jie Hu, Wei Li

Concrete-filled steel tubular (CFST) structures using high-strength materials have been increasingly used in civil engineering due to their exceptional mechanical performance. A comprehensive numerical analysis was performed in this study, where a finite element model was established for a CFST stub column using high-strength materials with consideration of fire and load combinations. The influence of critical parameters to the resistance in axial compression were analyzed based on the verified model, including the fire exposure time, the axial load level, the confinement factor, etc. The results showed that the residual resistance of a high-strength circular CFST (HCFST) column in axial compression decreased with the increase of axial load level and fire exposure time, while it increased with the increase of material strength.

Barbara Samorek

Włączenie sfery mieszkaniowej w zintegrowaną politykę rozwoju poprzez wdrażanie modelu zwartych struktur przestrzennych stanowi rzeczywistą szansę przeciwdziałania negatywnym trendom urbanizacji. Studia nad gospodarką mieszkaniową Wiednia – zaawansowaną w działaniach i skuteczną w realizacji – pozwalają na zidentyfikowanie w obrębie polityki miejskiej dobrych praktyk, sprzyjających koncentrycznemu kształtowaniu tkanki urbanistycznej. Analiza doświadczeń wiedeńskich oraz wyróżnienie związków pomiędzy cechami polityki miejskiej a kierunkiem wzrostu ośrodka dają podstawy do uznania, że mieszkalnictwo jest skorelowane z planowaniem przestrzennym. Autorską częścią pracy jest zainicjowanie badań nad problematyką polityki mieszkaniowej miasta zwartego. Stanowi ona otwartą koncepcję rozwoju, odpowiadającą na przestrzenno-funkcjonalne oraz społeczne przemiany współczesnych aglomeracji.

Inske Groenen, Stevan Rudinac, Marcel Worring

Large collections of geo-referenced panoramic images are freely available for cities across the globe, as well as detailed maps with location and meta-data on a great variety of urban objects. They provide a potentially rich source of information on urban objects, but manual annotation for object detection is costly, laborious and difficult. Can we utilize such multimedia sources to automatically annotate street level images as an inexpensive alternative to manual labeling? With the PanorAMS framework we introduce a method to automatically generate bounding box annotations for panoramic images based on urban context information. Following this method, we acquire large-scale, albeit noisy, annotations for an urban dataset solely from open data sources in a fast and automatic manner. The dataset covers the City of Amsterdam and includes over 14 million noisy bounding box annotations of 22 object categories present in 771,299 panoramic images. For many objects further fine-grained information is available, obtained from geospatial meta-data, such as building value, function and average surface area. Such information would have been difficult, if not impossible, to acquire via manual labeling based on the image alone. For detailed evaluation, we introduce an efficient crowdsourcing protocol for bounding box annotations in panoramic images, which we deploy to acquire 147,075 ground-truth object annotations for a subset of 7,348 images, the PanorAMS-clean dataset. For our PanorAMS-noisy dataset, we provide an extensive analysis of the noise and how different types of noise affect image classification and object detection performance. We make both datasets, PanorAMS-noisy and PanorAMS-clean, benchmarks and tools presented in this paper openly available.

Matteo Cardellini

Avoiding congestion and controlling traffic in urban scenarios is becoming nowadays of paramount importance due to the rapid growth of our cities' population and vehicles. The effective control of urban traffic as a means to mitigate congestion can be beneficial in an economic, environmental and health way. In this paper, a framework which allows to efficiently simulate and optimize traffic flow in a large roads' network with hundreds of vehicles is presented. The framework leverages on an Answer Set Programming (ASP) encoding to formally describe the movements of vehicles inside a network. Taking advantage of the ability to specify optimization constraints in ASP and the off-the-shelf solver Clingo, it is then possible to optimize the routes of vehicles inside the network to reduce a range of relevant metrics (e.g., travel times or emissions). Finally, an analysis on real-world traffic data is performed, utilizing the state-of-the-art Urban Mobility Simulator (SUMO) to keep track of the state of the network, test the correctness of the solution and to prove the efficiency and capabilities of the presented solution.

Y. Useni, F. Malaisse, J. Yona et al.

Abstract Recently, the growing need to complement rural and foreign sources of food and woodfuel is driving interest in urban forestry management in medium cities. The present study was designed to characterize the diversity of fruit trees in households of two rapidly developing cities in southeastern DR Congo (Lubumbashi and Kolwezi), and shed light on the sociological aspects of their management. Analyses of data collected through surveys carried out in planned and unplanned neighborhoods revealed noticeable botanical differences between the two neighborhoods within cities. In Lubumbashi, a greater number of fruit trees (6.5) and species (5.7) per 1000 m2 was recorded in the unplanned neighborhood compared to the planned neighborhood (3.4 trees and 2.0 species). A similar trend was noted in Kolwezi, although with significantly reduced values (by more than half). Across the two cities, a total of 36 fruit trees species were listed, of which 8 were exclusively identified in unplanned neighborhoods of Lubumbashi, showing a comparatively greater species richness of the city. Coincidentally, the 8 specific species are characteristic of Miombo woodland, suggesting preexistence of Miombo vegetation in these areas. Overall, the listed flora of studied neighborhoods in the two cities is dominated by exotic species, with Rutaceae the most represented family. Straightforward differences in the use of fruit trees were noted between the two cities; medicinal uses stand out in Lubumbashi, whereas uses such as shading and properties boundary predominate in Kolwezi. As common trend in the two cities, however, fruit trees scarcely receive arboricultural care, partly explained by limited knowledge on the ecological requirements of fruit trees. Current results have provided important insights into the botanical richness of fruit trees and related sociological aspects of their management at household-scale, which may help in formulating guidelines and technical tools to assessing and monitoring urban forestry in Southeastern DR Congo.

Aniello Lampo, Javier Borge-Holthoefer, Sergio Gómez et al.

The quantitative study of traffic dynamics is crucial to ensure the efficiency of urban transportation networks. The current work investigates the spatial properties of congestion, that is, we aim to characterize the city areas where traffic bottlenecks occur. The analysis of a large amount of real road networks in previous works showed that congestion points experience spatial abrupt transitions, namely they shift away from the city center as larger urban areas are incorporated. The fundamental ingredient behind this effect is the entanglement of central and arterial roads, embedded in separated geographical regions. In this paper we extend the analysis of the conditions yielding abrupt transitions of congestion location. First, we look into the more realistic situation in which arterial and central roads, rather than lying on sharply separated regions, present spatial overlap. It results that this affects the position of bottlenecks and introduces new possible congestion areas. Secondly, we pay particular attention to the role played by the edge distribution, proving that it allows to smooth the transitions profile, and so to control the congestion displacement. Finally, we show that the aforementioned phenomenology may be recovered also as a consequence of a discontinuity in the nodes density, in a domain with uniform connectivity. Our results provide useful insights for the design and optimization of urban road networks, and the management of the daily traffic.

Billy Pik Lik Lau, Benny Kai Kiat Ng, Chau Yuen et al.

With the advancement of the Internet of Things (IoT) and communication platform, large scale sensor deployment can be easily implemented in an urban city to collect various information. To date, there are only a handful of research studies about understanding the usage of urban public spaces. Leveraging IoT, various sensors have been deployed in an urban residential area to monitor and study public space utilization patterns. In this paper, we propose a data processing system to generate space-centric insights about the utilization of an urban residential region of multiple points of interest (PoIs) that consists of 190,000m$^2$ real estate. We identify the activeness of each PoI based on the spectral clustering, and then study their corresponding static features, which are composed of transportation, commercial facilities, population density, along with other characteristics. Through the heuristic features inferring, the residential density and commercial facilities are the most significant factors affecting public place utilization.

L. Titarenko, R. Karapetyan

Article is written on the basis of an analysis of empirical data obtained in 2021 from sociological studies conducted in Saint Petersburg and Minsk – cities with a population of one million, which served as the object of research of the current digital transformation. A feature of both samples was the large percentage of people with higher education among the employed population. In fact, we studied a group of urban professionals. The authors identify trends in the world of work that detail digital transformation processes. The purpose of the article is to describe the trends in the digitalisation process in the world of work in a large city and to reveal how relevant these trends are for the Belarusian and Russian professionals. The authors show the level of digital acquisition of the employed urban population today, as well as the impact on this process of the previous year associated with the pandemic and the inevitable transition of a part of the employed population to remote work. The article presents the factors that determine the labour motivation of urban professionals of different levels in their mastering of information and communication technologies, reveals the development trends of labour digitalisation processes and their impact on certain groups of professionals employed in both production and non-production spheres. It is concluded that the motivation of the employed population to master new digital knowledge directly depends on how much a person needs it in the workplace, contributes to his career advancement or helps to keep the workplace.

Rebecca Owusu, Florence Sefakor Dekagbey

This study uses choice experiment to investigate men and women consumers’ preferences and willingness to pay for edible mushrooms in Ghana. We used a mixed logit model to examine preference heterogeneity. The econometric modelling revealed that men consumers have a negative utility for oyster mushrooms compared to straw mushrooms. They also have preference for cheap and locally cultivated mushrooms compared to expensive and imported mushrooms. However, women consumers have preferences for the shiitake mushroom variety compared to the straw mushroom variety. They also prefer cheap mushrooms irrespective of their location and such mushrooms must be frozen and not fresh. The findings highlight variation between men and women in preferences for mushroom variety, however, both have preferences for low prices, suggesting that both genders are economically rational and obey the law of demand. JEL codes: B21, D12

Ahoura Zandiatashbar, Carla Maria Kayanan

Emergent economic development policies reflect the challenges urban growth coalitions face in attracting the footloose tech-entrepreneurs of the global economy. This convergence between the focus on place and the harnessing of global capital has led to the proliferation of innovation-igniting urban developments (IIUD)—place-based economic development strategies to boost the local knowledge economy. Economic developers are using IIUD strategies to convert areas of the city into entrepreneurial “launch pads” for innovation. However, because these developments remain young, considerations to implement IIUDs lack an evidence-base to show the potential for negative consequences on the communities where they are embedded. This research addresses this gap through: 1) a review of studies of similar developments to identify negative consequences; and 2) using a quasi-experimental method composed of Propensity Score Matching and Average Treatment Effect analyses from IIUDs in three US cities (Boston, MA, St. Louis, MO, and Buffalo, NY). Combined, results demonstrate that the greatest implications of IIUDs are the increased polarized division of labor, housing unaffordability, and income inequality. As IIUDs gain in popularity, it is critical to correlate negative consequences with IIUDs to inform economic developers in assessing trade-offs.

Azam Sadat Razavizadeh, Ali Ghaffari

In this study, the method of behavioral patterns map is used to investigate the effect of designed environment on individuals, success and impact of design in guiding behavior and providing appropriate opportunities for various behaviors. In this field of study, collected data on behavioral patterns is been considered under three categories; essential, recreational, and social. The purpose of this study is direct observation of presence in space in order to complete the collected data, methods efficiency achievement and techniques ofquantitative tests. Observation repeat records is essential to gain reliability of records. Because nonverbal behavior observation is based on our observation, camera and events location map are used together and behavioral pattern record is specified with location. At first, the target fabric and its general features is considered and then after an overview on neighborhood is centers, center of Sultan Mir Ahmad neighborhood elected to study. To test the hypotheses a combination of software techniques with field observations are selected. Finally through the proposed steps, the relationship between behavior and urban spaces are recognized. The results show that the relationship between behavior and environment may be considered in assessments of space quality. The analysis of visual observations of users behavior in a space led to an appropriate pattern which could be effective on improvement of physical environment quality and the human presence.